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https://www.himss.org/resources/healthcare-informatics

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Abstract

Healthcare Informatics is defined as "the integration of healthcare sciences, computer science, information science, and cognitive science to assist in the management of healthcare information" (Saba & McCormick, 2015, p. 232). Nursing Informatics is a subset of informatics, specific to the field and the role of the nurse in the healthcare setting. The American Nurses Association (ANA) identified nursing informatics as "a specialty that integrates nursing, science, computer science, and information science to manage and communicate data, information, and knowledge in nursing practice" (ANA, 2001, p.17). Healthcare and nursing informatics are both vastly growing fields within the medical field and are continuously incorporating new and evolving technology. Both have been around for the past three decades, at least. The technology boom at the turn of the century has helped informatics and information systems further evolve. Enhanced delivery of care, improved health outcomes, and advanced patient education are just a few aspects that have improved. With any new technology or innovation there are implications, some foreseeable and some that come to light after the unveiling of the new process or product: some impacts that are most notable are clinical, managerial, and policy implications. This paper explores the implications, (both constructive and adverse), that are the most notable in today's healthcare world within the healthcare and nursing informatics fields.

Introduction

Healthcare Informatics is defined as "the integration of healthcare sciences, computer science, information science, and cognitive science to assist in the management of healthcare information" (Saba & McCormick, 2015, Pg. 232). Nursing Informatics is a subset of informatics, specific to the field and the roll of the nurse in the healthcare setting. The American Nurses Association (ANA) identified nursing informatics as "a specialty that integrates nursing, science, computer science, and information science to manage and communicate data, information, and knowledge in nursing practice" (ANA, 2001, Pg.17). Healthcare and nursing informatics are both vastly growing fields within the medical field and are continuously incorporating new and evolving technology. Both have been around for the past three decades, at least. The technology boom at the turn of the century has helped informatics and information systems further evolve. Enhanced delivery of care, improved health outcomes, and advanced patient education are just a few aspects that have improved. With any new technology or innovation there are implications, some foreseeable and some that come to light after the unveiling of the new process or product. Some impacts that are most notable are clinical, managerial, and policy implications. This paper will explore the implications, both constructive and adverse, most notable in today's healthcare world within the healthcare informatics and nursing informatics fields.

Clinical Implications

The use of informatics is seen in a multitude of processes within the clinical setting. Whether inpatient or outpatient, clinicians and patients utilize online portal systems, electronic medical records, data collection devices such as vital sign machines and glucometers, as well as personal data devices and email, to name a few. When considering these systems and how they affect the process and flow of the clinical setting, it is important to not only consider the technology at hand but also the workflow and the data collection process. Norris, Hinrichs, & Brown, tell us "gaps are present between the technology and the process. Informatics can help bridge that gap. Skills needed include understanding of data collection, storage, and extraction, in addition to an appreciation for the power of data to drive and inform practice" (2015, p. 11-12). Healthcare informaticists, especially nursing informaticists, are the prime group to help bridge that gap. Without a strong clinician presence in the building and implementation process, gaps will remain. With healthcare informaticists involved in the development, a strong product can be delivered that is usable to all members of the healthcare team.

President Obama signed the American Recovery and Reinvestment Act (ARRA) into law in 2009. This law includes the Health Information and Technology for Economic Clinical Health Act (HITECH). HITECH formulated the Meaningful Use (MU) program under the Centers for Medicare and Medicaid Services (CMS). Meaningful Use incentivizes providers to use their Electronic Health Records (EHR) by financially rewarding them when demonstrating their use, but also poses a threat by penalizing them if not used in the future (Norris, Hinrichs, & Brown, 2015). These Acts greatly impact both nursing and the healthcare field clinically. Providers who do not typically use an EHR are now finding themselves in a learning curve while still maintaining their patient caseload, and providing comprehensive care. However this vast amount of data collection across a multitude of healthcare settings has provided opportunity in enhancements of care. While the initial rollout of these initiatives may cause chaos, the benefits of these initiatives will allow clinicians to provide comprehensive, safe, evidence-based care to all of their patients. Health care staff will be able to quickly and safely access pertinent information on their patients throughout the health institution and beyond.

In addition to enhancements in care and improved evidence-based practice, the increased amount of data collected by EHRs and other data systems has created a massive amount of data that hospitals and health care organizations now have to manage and analyze. "This has led to increased demand for professionals who are well versed in both informatics and medicine. To meet this demand, the American Medical Informatics Association spearheaded the establishment of professional-level education and certification for physicians in informatics" (Simpao, Ahumada, Galvez, & Rehman, 2014, p. 45). We are now seeing a new influx of healthcare professionals entering this sub-specialty. The needs for these professionals to collect, interpret, and study the data and the operation of information systems is crucial to the success and usability of these systems. Lehman, Shorte, & Gundlapalli (2013) stated that "it is reasonable to predict that the number of leadership positions in clinical informatics with titles and roles such as chief medical informatics officer, chief health informatics officer, directors of clinical informatics, and lead of EHR implementation, etc., will increase in the near future" (p. 528).

Managerial Implications

The use of healthcare informatics is not only prevalent in the clinical setting but also in the managerial setting. In our society, communication comes in a multitude of applications: verbal, physical, and now electronic. We often find that individuals can contact us by using various applications such as telephone, fax, pager, instant messenger, email, and so on. It is crucial for those in managerial positions to utilize these information systems to aid their work and the work of their staff while being mindful to set limits and standards. Time management is a prevalent issue in the healthcare setting, thus the use of informatics to aid and organize and not create barriers is essential. However, these expansive communication tools can create stress and feelings of intake overload. Marquis & Huston (2013) suggested "to reduce interruptions and distractions, individuals should shut off their email, isolate themselves, and make sure the environment around them is working to strengthen their willpower and focus" (p. 189). Healthcare professionals need to ensure that they are utilizing informatics to aid their work and time management, not impeding them. Marquis & Huston (2013) go on to note that "creating a workspace that has a desk with enough clear space to do your work, good lighting, and a comfortable chair" is crucial (p.190).

Information systems in the managerial role often consist of interpreting information and modifying data to be utilized in decision-making processes. Managerial programs facilitate payroll functions, streamline material control, and assist with financial and administrative factors of their role (Pacheco de Souza, Santiago & Izu, 2015, p.7284). As seen in clinical implications, Meaningful Use also plays a role in the role of the manager. It is important for "nurse managers to utilize information from the EHR to show Meaningful Use and are important to the process of determining how information is organized and categorized within the EHR" (Biddle & Milstead, 2016, p.12). Without a nurse manager and other administrator identification of how to best capture and report Meaningful Use information, the healthcare organization may not be compliant with this aspect of the HITECH Act.
The continuously growing field of informatics is of great benefit to healthcare managers at all levels. Using this growing technology can greatly benefit their role and improve the function of their staff. Pacheco de Souza, Santiago, & Izu (2015) go on to acknowledge that this advancing technology "should be utilized as a management tool, giving power and autonomy to nurse managers in more efficient use of available technological resources" (p.7285). One example of this is where a unit may identify incomplete documentation on a given point of care. The nurse manager can then construct a workgroup of colleagues who are involved in the process including an IT specialist. Once this group identifies the issues and improved processes, they can present their recommendations to administration, ultimately improving documentation in the EHR (Biddle & Milstead, 2016). Thus, healthcare information systems can improve communication, time-management, and delivery of information amongst staff and patients.

Policy Implications

As previously noted in clinical implications and managerial implications, the amount of data that is now available from EHRs and other forms of information systems, is larger than healthcare providers have ever dealt with. Policy makers are now able to utilize this data to inform their decision-making about public-health issues. It is crucial now, more than ever, that epublic health staff are available at various levels of the health system to develop skills and knowledge to better utilize existing datasets. Adair (2012) identified guidelines to help public health officials understand, interpret, and best utilize this influx of information. "These guidelines were developed to assist public health officials assess the quality of existing health data, and effectively utilize such data to compute indicators to inform health sector policy-making" (p. 53). If policymakers are not able to thoroughly understand data derived from the utilization of electronic health records, patient portals, and other data sources, then people cannot expect them to make sound judgments when voting on new laws.

In this age of technology it is important that healthcare providers, managers, and informaticists are mindful of adhering to HIPAA regulations to ensure that growing technology is incorporated in current policies. With the use of EHRs and mobile health applications there is an increased "liability for healthcare organizations if there is a breach in patient confidentiality or privacy, which is why organizations must have policies in place that guide the use of telehealth and communication" (Biddle & Milstead, 2016). Ensuring proper policy is in place and that changes are made as needed when new technology is introduced is crucial.

McGowan, Cusack, and Bloomrosen (2012) wrote, "since 2006, the American Medical Informatics Association (AMIA) has convened an annual investigational Health Policy meeting to examine Cutting edge issues in healthcare and health information technology (health IT) policy" (p. 460). These meetings will identify and discuss potential issues with health IT and informatics as well as develop a plan and report to present to our nation's policymakers so they are well informed when making and voting for pertinent policies and laws (McGowan, Cusack, & Bloomrosen, 2012). It is important that health professionals, information technology developers, and policymakers are able to communicate and work together in the interest of delivering the most efficient and safest patient care.

Meaningful Use is one example of health policy and health informatics working together. The initiative behind Meaningful Use was to encourage the use of Electronic Health Records in all health systems nationwide. However with this initiative, some providers may find themselves not being truthful in their documentation in order to meet the incentives set by the HITECH legislation. McGowan, Cusack, and Bloomrosen (2012), identified that "with the mandate for meaningful use of health IT by providers, there is ample opportunity for inappropriate and even fraudulent or illegal activities, ranging from lack of oversight to deliberate misrepresentation" (p. 461). Although dishonorable, some providers may lie to ensure their practice is able to receive the best monetary incentive by adhering to Meaningful Use measures and may alter their documentation to fit the criteria.

McGowan, Cusack, and Bloomrosen (2012) also identified policy concerns with health IT and informatics when discussing federal and state roles. Often federal and state regulations and health IT initiatives lack coordination. "Without explicit guidelines, proprietary state systems may be created, with many not being able to connect to the national health information infrastructure and some not acknowledging the healthcare systems that cross state lines" (McGowan, Cusack, and Bloomrosen, 2012, p.462). It is crucial that not only health professionals and government coordinate their objectives and policy, but also governing officials of the state and federal government. Conflicting or uncoordinated health initiatives may lead to patient and public mistrust as well as financial complications with both the government and health organizations.

Summary

Health informatics and nursing informatics are very relevant in evolving health systems. New technology and initiatives are constantly being developed. These new innovations do not go without implications in the clinical setting, managerial setting, and the policy setting. It is crucial that all participants whether it is the nurse, manager, provider, politician, lobbyist, or President of the United States remain coordinated. Multidisciplinary unity is crucial to ensure public trust in our health systems and to provide safe and effective patient care.

Citation: Sweeney, J. (Feb, 2017). Healthcare Informatics. Online Journal of Nursing Informatics (OJNI), 21(1).
The views and opinions expressed in this blog or by commenters are those of the author and do not necessarily reflect the official policy or position of HIMSS or its affiliates.
Online Journal of Nursing Informatics

Powered by the HIMSS Foundation and the HIMSS Nursing Informatics Community, the Online Journal of Nursing Informatics is a free, international, peer reviewed publication that is published three times a year and supports all functional areas of nursing informatics.
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This paper explores the implications that are most notable in today's healthcare world within the nursing and healthcare informatics fields.

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I thought this comparison was interesting. To be completely honest, I haven't thought much about health informatics, let alone know that all of these existed. 

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American Association of Nurse Practitioners

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Integrating health care sciences, computer science, information science and cognitive science to assist in the management of health care information.

Remember the first day you were required to document your patient's care on an Electronic Health Record (EHR)?

For years, health care providers produced expertly charted SOAP (subjective, objective, assessment and plan) notes and handwritten plans of care compiled in heavy file folders. Thus, transitioning to the EHR was not always a comfortable process. However, the field has now adapted and found value in its use. As nursing leadership expanded in scope and responsibility, the EHR capabilities supported this growth. Individuals who became RNs or NPs after EHRs became the standard quickly became superusers and technology leaders.

That said, few opportunities have existed for providers to advance further in Health Informatics. Despite the introduction of "health care disruption," introduced as early as 2000 by innovation and growth expert Clayton M. Christensen, it has only been over the past several years that we have begun to look to disruption for ways to deliver high-quality care for patients at a lower cost while working to satisfy both patients and clinicians.

"As we're working toward meeting the quadruple aim in health care, we need to consider tools that will help us help both our patients and ourselves," said Susan Conaty-Buck, DNP, APRN, FNP-C, health care informatics researcher at the University of Delaware and family NP at Nurse Managed Primary Care Clinic. "NPs are leaders in our profession, trained to be able to look for solutions to care for people, whether that's the best medicine, best tests or best procedures. We should owe it to our patients and ourselves to investigate resources that present opportunities for patients to work with us on their quest for health."

In addition to an exploding array of technologies available within the health care setting-including telehealth, artificial intelligence, robotics, virtual and augmented reality, interoperability, nutrigenomics and big data analytics-tech giants such as Apple, Google and IBM have also developed products to not only benefit from sales but also from the capture of personal and population health data. This has increased the commercialization of health care by allowing a third partner in the delivery of care.

As patients experience frustration with the lack of access to affordable, patient-centered care, more are adopting corporate solutions, such as wearable health technology, to provide self-care. These can be highly successful at helping patients manage their health, but teaming these with clinician care can significantly boost effectiveness.

"As practitioners, we can help patients stay safe and understand how their data is going to be used, whether we simply accept this data and scan it into their records or update our equipment to seamlessly receive that data to coordinate care," said Dr. Conaty-Buck. "Ultimately, the goal is to use technology appropriate for what you're trying to accomplish for care of the patient. For some, that's what helps you to choose the best care options for your individual patients. For others, it's how you and your practice will be collecting and using big data to come up with predictive analytics to see trends in patient populations. We entered the health care field to provide exemplary care to our patients, and health informatics is one avenue contributing to making this happen."
Are you interested in learning more about health informatics?
The Health Informatics and Telehealth Specialty Practice Group (SPG) offers a unique opportunity to interactively collaborate with colleagues who share interest or clinical expertise in health information technology (IT). As an SPG member, you'll have access to a cutting-edge, online forum where you can engage in discussions, document sharing and knowledge exchange with fellow NPs.

Are You Joining AANP at HIMSS19?

Working to fulfill the mission of empowering all NPs to advance quality health care through practice, education, advocacy, research and leadership, AANP members will soon have even more opportunities to use advanced health informatics to improve evidence-based patient care!

brings together world-class education and cutting-edge solutions to help solve your health technology challenges. AANP is a collaborator of HIMSS19, meaning you can receive a discount to attend the conference in Orlando February 11-15, 2019.

To use the discount, select AANP from the "Conference Collaborating Organizations" drop-down menu during the registration process and enter the code COLLABH19.
Are you attending #HIMSS19? Let us know on social media (Facebook or Twitter)! Thank you to Dr. Conaty-Buck for her invaluable assistance with this article.

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Going Beyond the EHR: Health Care Informatics

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Integrating health care sciences, computer science, information science and cognitive science to assist in the management of health care information. Remember the first day you were required to document your patient's care on an Electronic Health Record (EHR)? For years, health care providers...

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in 2011, the TIGER Initiative Foundation was formed. It's an organization operating a charitable education and scientific purposes. As the TIGER Initiative evolves, it's committed to engaging diverse stakeholders to further its mission of advancing the integration of health informatics to transform practice, education, and consumer engagement. It was also initially funded by a grant from the Robert Wood Johnson Foundation.

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OVERVIEW

The overall goal for the Quality and Safety Education for Nurses (QSEN) project is to meet the challenge of preparing future nurses who will have the knowledge, skills and attitudes (KSAs) necessary to continuously improve the quality and safety of the healthcare systems within which they work.

Using the Institute of Medicine competencies, QSEN faculty and a National Advisory Board have defined quality and safety competencies for nursing and proposed targets for the knowledge, skills, and attitudes to be developed in nursing pre-licensure programs for each competency. These definitions are shared in the six tables below as a resource to serve as guides to curricular development for formal academic programs, transition to practice and continuing education programs .

For information on applying the competencies at a graduate level, see the Graduate KSAs page.

Note: This content is reprinted with permission from the " Quality and Safety Education for Nurses" article originally printed in Nursing Outlook Special Issue: Quality and Safety Education. For guidelines on use of this material, please read our terms and conditions.

DEFINITIONS AND PRE-LICENSURE KSAS

Click here to download the PDF version

REFERENCES

¹ Institute of Medicine. Health professions education: A bridge to quality. Washington DC: National Academies Press; 2003.

² Cronenwett, L., Sherwood, G., Barnsteiner J., Disch, J., Johnson, J., Mitchell, P., Sullivan, D., Warren, J. (2007). Quality and safety education for nurses. Nursing Outlook, 55(3)122-131.

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The overall goal for the Quality and Safety Education for Nurses (QSEN) project is to meet the challenge of preparing future nurses who will have the knowledge, skills and attitudes (KSAs) necessary to continuously improve the quality and safety of the healthcare systems within which they work.

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What is Interoperability?

It is the ability of different information systems, devices and applications (systems) to access, exchange, integrate and cooperatively use data in a coordinated manner, within and across organizational, regional and national boundaries, to provide timely and seamless portability of information and optimize the health of individuals and populations globally.

Health data exchange architectures, application interfaces and standards enable data to be accessed and shared appropriately and securely across the complete spectrum of care, within all applicable settings and with relevant stakeholders, including the individual.

Four Levels of Interoperability

Foundational (Level 1): Establishes the inter-connectivity requirements needed for one system or application to securely communicate data to and receive data from another

Structural (Level 2): Defines the format, syntax and organization of data exchange including at the data field level for interpretation

Semantic (Level 3): Provides for common underlying models and codification of the data including the use of data elements with standardized definitions from publicly available value sets and coding vocabularies, providing shared understanding and meaning to the user

Organizational (Level 4): Includes governance, policy, social, legal and organizational considerations to facilitate the secure, seamless and timely communication and use of data both within and between organizations, entities and individuals. These components enable shared consent, trust and integrated end-user processes and workflows

What is Health Information Exchange and Data Sharing?

Health information exchange, or HIE, provides the capability to electronically move clinical information among disparate healthcare information systems and maintain the meaning of the information being exchanged. The goal of health information exchange is to facilitate access to and retrieval of clinical data to provide safe, timely, efficient, effective and equitable patient-centered care. HIE can also be used by public health authorities to assist in the analysis of the health of populations.

The term HIE is generally used as either a verb or a noun.

Verb: The electronic sharing of health-related data between two or more organizations facilitated by applied standards for use by a variety of stakeholders to inform health and care.

Noun: Organizations within the United States that provide health information exchange technology and services at a state, regional or national level and often work directly with communities to promote secure sharing of health data.

In the European Union, the act of information exchange is often referred to as data sharing. The European Commission's Support Centre for Data Sharing uses the term to refer to the collection of practices, technologies, cultural elements and legal frameworks that are relevant to transactions in any kind of information digitally, between different kinds of organizations. This term is more broadly applied to all types of data sharing, but can also be specifically used for health-related data.

Interoperability Standards

Standards provide a common language and a common set of expectations that enable interoperability between systems and/or devices. In order to seamlessly digest information about an individual and improve the overall coordination and delivery of healthcare, standards permit clinicians, labs, hospitals, pharmacies and patients to share data regardless of application or market supplier.

Standards Development

A standards organization, standards body, standards development organization (SDO) or standards setting organization is focused on developing, coordinating, promulgating, revising, amending, reissuing, interpreting, or otherwise producing standards that are intended to address the needs of some relatively wide base of affected adopters.

In order to be recognized as an SDO, an organization may be accredited by the American National Standards Institute (ANSI) or the International Organization for Standardization (ISO). Standards are also developed by other groups such as trade unions or associations. The development and adoption of open, consensus-based standards can be a complex process involving many different stakeholders and subject matter experts.

There are over 40 different SDOs in the health IT arena. Some entities create standards, such as Health Level Seven (HL7), Systematized Nomenclature of Medicine (SNOMED) International, and the Clinical Data Interchange Standards Consortium (CDISC). Others, like Integrating the Healthcare Enterprise (IHE), do not develop new standards, but rather bundle complementary base standards into IHE profiles that are used to define a specific function or use case, and then are balloted. This creates a scenario that helps drive adoption of the base standards by providing implementation guidance that describes how multiple standards can be used together to support interoperable health information exchange.

The different SDOs and profiling organizations have varying compositions and processes, but generally follow shared principles based on developing standards through a multi-stakeholder, consensus-based process to respond to specific industry or market needs.

Types of Standards

In order to understand the types of health data standards available for use, informatics professionals organize these standards into the following specific categories: vocabulary/terminology, content, transport, privacy and security, and identifiers.

Vocabulary/Terminology Standards

Vocabulary/terminology standards address the ability to represent concepts in an unambiguous manner between a sender and receiver of information, a fundamental requirement for effective communication. Health information systems that communicate with each other rely on structured vocabularies, terminologies, code sets and classification systems to represent health concepts. Some common vocabulary standards currently used in the marketplace include:

Current Procedural Terminology (CPT®): A code set, maintained by the American Medical Association (AMA), used to bill outpatient and office procedures.

Healthcare Common Procedure Coding System: A set of healthcare procedure codes based on CPT that is used for Medicare reimbursement.

ICD-10 and ICD-11: The International Statistical Classification of Diseases and Related Health Problems (ICD) is a medical classification list by the World Health Organization (WHO). It contains codes for diseases, signs and symptoms, abnormal findings, complaints, social circumstances, and external causes of injury or diseases. The 11th revision will replace the ICD-10 in January 2022.

Logical Observation Identifiers Names and Codes (LOINC®): A universal code system for identifying health measurements, observations and documents. These codes represent the "question" for a test or measurement. LOINC codes can be grouped into laboratory and clinical tests, measurements and observations.

National Drug Code (NDC): Maintained by the U.S. Food and Drug Administration, NDC provides a list of all drugs manufactured, prepared, propagated, compounded or processed for commercial distribution.

RadLex: A unified language of radiology terms for standardized indexing and retrieval of radiology information resources, managed by the Radiological Society of North America. It unifies and supplements other lexicons and standards, such as SNOMED-Clinical Terms and DICOM.

RxNorm: A terminology used to normalize names for clinical drugs and links its names to many of the drug vocabularies commonly used in pharmacy management and drug interaction software. By providing links between these vocabularies, RxNorm can mediate messages between systems not using the same software and vocabulary.

Systematized Nomenclature of Medicine-Clinical Terms (SNOMED-CT): A comprehensive clinical health terminology product. It enables the consistent, processable representation of clinical content in electronic health records (EHRs). These codes often represent the "answer" for a test or measurement to the LOINC "question" code.

The Centers for Disease Control and Prevention (CDC) provide a number of code sets for vaccines (Vaccines Administered (CVX)) and manufacturers (Manufacturers of Vaccines (MVX)). These codes can be used in immunization messages.

The Unified Code for Units of Measure: A code system intended to include all units of measures used in international science, engineering and business to facilitate unambiguous electronic communication of quantities together with their units.

Content Standards

Content standards relate to the data content within exchanges of information. They define the structure and organization of the electronic message or document's content. This standard category also includes the definition of common sets of data for specific message types.

Consolidated CDA (C-CDA): A library of CDA templates, incorporating and harmonizing previous efforts from HL7, IHE, and Health Information Technology Standards Panel (HITSP). It represents harmonization of the HL7 Health Story guides, HITSP C32, related components of IHE Patient Care Coordination and Continuity of Care Documents, or CCD.

HL7's Version 2.x (V2): A widely implemented messaging standard that allows the exchange of clinical data between systems. It is designed to support a central patient care system as well as a more distributed environment where data resides in departmental systems.

HL7 Version 3 Clinical Document Architecture (CDA®): An XML-based document markup standard that specifies the structure and semantics of "clinical documents" for the purpose of exchange between healthcare providers and patients. It defines a clinical document as having the following six characteristics: persistence, stewardship, potential for authentication, context, wholeness and human readability.

Transport Standards

Transport standards address the format of messages exchanged between computer systems, document architecture, clinical templates, user interface and patient data linkage. Standards center on "push" and "pull" methods for exchanging health information.

Digital Imaging and Communications in Medicine (DICOM): The standard for the communication and management of medical imaging information and related data. DICOM enables the transfer of medical images across systems and facilitates the development and expansion of picture archiving and communication systems.

Direct StandardTM: Defines a set of standards and protocols to allow participants to send authenticated, encrypted health information directly to known, trusted recipients over the internet. Two primary specifications are the Applicability Statement for Secure Health Transport v1.2 and the XDR and XDM for Direct Messaging.

Fast Healthcare Interoperability Resources (FHIR®): An HL7 standard for exchanging healthcare information electronically. The basic building blocks of FHIR are "resources," which describe exchangeable health data formats and elements. FHIR also provides standardization for application programming interfaces (APIs). FHIR provides a number of benefits and improvements as a modern healthcare standard including facilitating interoperable exchange with legacy standards, lower overhead, shorter learning curve, an ability to transmit only the necessary pieces of information, potential for patient mediated data, and an energized community of supporters and implementers.

IHE provides a number of specifications that can be used in the exchange of health information.

PCHAlliance co-sponsors the Personal Health Device (PHD) efforts within the IHE Devices Domain, which are focused on developing IHE profiles that leverage and build upon the Continua Design Guidelines and are also updated to support FHIR. These profiles provide guidance to implement globally recognized, consensus-based approaches to connect and test both personal and clinical devices and integrate them into health information systems. The specifications being developed support both medical devices and mainstream consumer facing apps to enable scalable interoperability of the rapidly expanding connected health ecosystem.

Privacy and Security Standards

Privacy standards aim to protect an individual's (or organization's) right to determine whether, what, when, by whom and for what purpose their personal health information is collected, accessed, used or disclosed. Security standards define a set of administrative, physical and technical actions to protect the confidentiality, availability and integrity of health information.

In the U.S., the Health Insurance Portability and Accountability Act (HIPAA) outlines standards that safeguard the privacy and security of protected health information.

HIPAA Privacy Rule: Establishes national standards to protect individuals' medical records and other personal health information. It applies to health plans, healthcare clearinghouses, and healthcare providers that conduct certain healthcare transactions electrically. The rule applies safeguards to protect the privacy of personal health information, and sets limits and conditions on the uses and disclosures of such information without patient authorization. The rule also gives patients rights over their own health information, including the right to examine and obtain a copy of their records, and to request corrections.

HIPAA Security Rule: Sets national standards for protecting the confidentiality, integrity, and availability of electronically protected health information. Compliance with the Security Rule was required as of April 20, 2005 (April 20, 2006 for small health plans). The rule addresses the technical and non-technical safeguards that "covered entities" must have in place to secure an individual's electronic protected health information. Prior to HIPAA, there were no generally accepted requirements or security standards for protecting health information.

In Europe, the General Data Protection Regulation (GDPR) outlines privacy and security regulations for all processing and storage of data relating to data subjects-or people-in the European Union (EU). This regulation extends to health information and any organization that may process or store data on these subjects, meaning it has extensive reach to many organizations worldwide and related to the sharing of data across organizations.

Identifier Standards

Entities use identifier standards to uniquely identify patients or providers.

Enterprise Master Patient Index (EMPI): A data registry used across a healthcare organization to maintain consistent and accurate data on the patients treated and managed within its departments.

Medical Record Number (MRN): An organization specific code used as a systematic documentation of a patient's history and care during a hospital stay.

National Council of State Boards of Nursing ID (NCSBN ID): A unique identifier automatically generated for each registered nurse and licensed practical/vocational nurse, freely available via the Nursys database and maintained by NCSBN.

National Provider ID (NPI): A unique 10-digit number for a healthcare provider to create a standard identification. These NPIs are included in the free NPI Registry.

Object ID (OID): A globally unique ISO identifier and a preferred scheme for unique identifiers in HL7.

Standards, Implementation Guides and Profiles

Standards on their own are helpful because they describe and constrain what data moves.

Implementation guides describe how to implement these standards for a specific use case. An implementation guide is a companion to a standard that describes how to use a standard to satisfy a specific healthcare use case. It should specify which way a standard is to be applied in a particular use case-how to structure the data consistently and what vocabulary to use. While you can implement a standard in a number of different ways, when using an implementation guide to integrate a standard into a health IT system, the guide directs one way to constrain the standard for a particular situation, removing ambiguity and achieving consistency.

A benefit to the use of implementation guides like IHE Profiles is that they provide a common language for purchasers and market suppliers to discuss the integration needs of healthcare sites and the integration capabilities of health IT products. They provide precise definitions of how to implement standards to meet specific clinical needs. They offer developers a clear implementation path for standards that has been carefully documented, reviewed and tested as well as supported by industry partners. They give purchasers a tool that reduces the complexity, cost and anxiety of implementing interoperable systems. IHE profiles, in particular, organize and leverage the integration capabilities that can be achieved by coordinated implementation of communication standards, such as DICOM, HL7 W3C and security standards.

Testing and Conformance Efforts

The adoption and implementation of standards involves a testing ecosystem that spans standards development, implementation and feedback from real-world testing into the development process to support continuous improvement. There are various different testing and compliance efforts that support advancing health IT interoperability, including:

Conformity Assessment: IHE International administers the IHE Conformity Assessment Scheme, which forms the basis for IHE Conformity Assessment Programs and any official certification of conformance to IHE Profiles associated with such testing programs. IHE International authorizes designated test laboratories accredited under this standard to assess the conformity of products with selected IHE profiles.

eHealth Exchange Testing Program: The program was developed to test compliance for HIE standards as required by the eHealth Exchange Coordinating Committee for onboarding to the eHealth Exchange network. Its purpose is to enable organizations that wish to participate in the exchange to validate the compliance of their health IT with the eHealth Exchange Performance and service specifications.

HL7 FHIR® Connectathons: These events provide hands-on FHIR development and testing opportunities held in conjunction with working group meetings. Participants engage in development and testing, working directly with other FHIR developers and senior members of the FHIR standards development team.

IHE Connectathons: A cross-vendor, supervised and structured testing event where the industry tests implementation of IHE Profiles and other standards to successful standards implementation and use. All tests are evaluated on interoperability and conformance to IHE Profiles found in IHE's technical frameworks. The test floor is overseen by IHE's technical project managers, providing a safe, neutral test environment and an opportunity for industry collaboration and problem resolution.

Office of the National Coordinator for Health IT Health IT Certification Program: A voluntary certification program established by the Office of the National Coordinator for Health IT (ONC) to provide for the certification of health IT. Requirements for certification are established by standards, implementation specifications and certification criteria adopted by the ONC secretary. The program supports the availability of certified health IT for its encouraged and required use under other federal, state and private programs. The program is run as a third-party product conformity assessment scheme for health IT based on the principles of the ISO and International Electrotechnical Commission framework.

Organizations like the Global Consortium for eHealth Interoperability, which was co-founded by HIMSS, IHE International and HL7 International, work to amplify and align the work of organizations like IHE and HL7 to increase adoption of emerging and mature health IT standards. The primary goal of the consortium is to coordinate work with governments and national ministries of health to further align existing and emerging standards and implementation guidance with strategic healthcare policy goals to achieve improved health outcomes for people everywhere.

The Interoperability Ecosystem

The health interoperability ecosystem comprises individuals, systems and processes that want to share, exchange and access all forms of health information, including discrete, narrative and multimedia. Individuals, patients, providers, hospitals/health systems, researchers, payers, suppliers and systems are potential stakeholders within this ecosystem. Each is involved in the creation, exchange and use of health information and/or data.

An efficient interoperability ecosystem provides an information infrastructure that uses technical standards, policies and protocols to enable seamless and secure capture, discovery, exchange and utilization of health information.

Exchange Partners and Data Sources

Historically, the focus was on the exchange of clinical information between providers and hospitals. As the technology has progressed, these exchange partners have broadened to include individuals, such as patients and caregivers, and organizations, such as long-term care, public health departments, community-based organizations and payers. With this expansion, along with the shift to value-based care, has also come a broader range of data types that are available to inform health and care, including device data, patient-generated data and data pertaining to the social determinants of health. Social determinants of health can include data points such as an individual's housing status, access to reliable transportation and level of food security.

Network Architectures

There are three primary types of exchange network architecture used to coordinate the exchange of health information across entities.

Centralized: Patient data are collected and stored in a centralized repository, data warehouse or other databases. The exchange organization has full control over the data, including the ability to authenticate, authorize and record transactions among participants.

Federated (Decentralized): Interconnected but independent databases allow for data sharing and exchange, and grant users access to the information only when needed.

Hybrid: Incorporates variations of federated and centralized architectures to harness the advantages of both. These are becoming common as various combinations of available services are implemented.

An important technical development that has expanded exchange partners and data sources is the emergence of healthcare APIs. APIs outline a set of clearly defined specifications to allow for one software application to build on the data and functionality of another application, without needing to understand its system design. APIs are already ubiquitous in today's web economy, and will play an essential role with respect to fueling healthcare interoperability for person centered care.

Types of HIE Organizations

An HIE organization oversees and governs the exchange of health-related information among organizations according to nationally recognized standards. The purpose of a health information exchange organization is to perform oversight and governance functions for information exchange. There are several different types of HIEs currently operating across the U.S. and its territories:

1. Hybrid HIEs are often collaborations between organizations, such as an ACO and a vendor network, within a state or region. The Kentucky Health Information Exchange is an example of a hybrid model.

2. Private/Proprietary HIEs concentrate on a single community or network, often based within a single organization, and include overall management, finance and governance. Examples may include hospital/integrated delivery system networks, payer-based HIEs and disease-specific HIEs. Some software vendors have also established an HIE network for their clients across the U.S. Additionally, the industry may see other evolving entities such as Accountable Care Organizations (ACOs) supporting information exchange.

3. Regional/Community HIEs are inter-organizational and depend on a variety of funding sources. Most are not-for-profit. Indiana Health Information Exchange and Chesapeake Regional Information System for Our Patients are examples of a regional HIEs.

4. State-wide HIEs are run by the governments of their respective states or maybe the state's designated entity. Some state-wide (and regional) HIEs use an umbrella approach and serve as the aggregator for disparate private health information exchanges. Statewide Health Information Network for New York and Arizona's Health Current are examples of a state-wide HIEs.

U.S. Exchange Initiatives: An Environmental Scan

Many organizations are actively working to achieve ubiquitous exchange. This environmental scan provides a curated review of the current landscape of the U.S.-based networks and frameworks which enable interoperable, nationwide health information exchange via a variety of methods and collaborations.

International Networks Enabling Exchange

Beyond the examples outlined above, various exchange networks and infrastructure have been established globally.

The eHealth Digital Service Infrastructure (EHDSI) facilitates the two primary building blocks of cross-border digital health services in Europe: ePrescription and Patient Summary. ePrescription (and eDispensation) allows EU citizens to obtain their medication in a pharmacy located in another EU country. Patient Summary provides information on important health related aspects such as allergies, current medication, previous illness, surgeries, etc. The digital Patient Summary is meant to provide doctors with essential information in their own language concerning the patient, when the patient comes from another EU country and there may be a linguistic barrier.

An important precursor of major exchange initiatives in Europe have been the European Reference Networks. These are virtual networks involving healthcare providers across Europe. They aim to facilitate discussion on complex diseases and conditions that require highly specialized treatment, providing concentrated knowledge and resources. Coordinators convene virtual advisory panels of medical specialists across different disciplines, using a dedicated IT platform and telemedicine tools.

Argentina's National Interoperability Network uses standards to enable communication between participating health information systems, providing a bridge for identity management across systems, sharing documents and patient summaries, managing ePrescriptions and supporting national registries.

Australia's My Health Record is an opt-out, CDA-based document repository used to store shared health summaries, eReferrals, specialist letters, discharge summaries, event summaries, prescription records, diagnostic imaging and pathology reports. Once patients have their record, they are able to manage access and permissions.

Canada's Health Infoway manages an ePrescribing service, PrescribeIT, electronically shares prescription information from clinicians with pharmacies for dispensing. Another Infoway initiative, ACCESS Health, aims to accelerate citizen access to personal health information and digital health services. The exchange initiative is expected to use a cloud-based infrastructure, a FHIR-based API service and a blockchain-enabled consent service.

Hong Kong's Electronic Health Record Sharing System is the country's equivalent to a regional HIE, managing patient data on care episodes, lab results, radiology studies and drug items. The Hong Kong Hospital Authority launched eHRSS to public and private health sectors using an opt-in patient consent model.

Uses of Information Exchange
Watch the HIMSS TV deep-dive on interoperability.
Ultimately, the goal is to be able to share and access information that informs an individual's full, longitudinal health story. By having and understanding the complete and accurate picture of an individual's health-including their preferences and other determinants of health-includes a number of benefits. Clinicians can better inform care and decision making, patients can become active participants in their care plans, and health IT developers and implementers can leverage evidence to create and adopt systems that support clinical processes and improve care delivery.

A number of benefits can be realized for exchange stakeholders, including:

1. Care coordination: Care coordination often involves a variety of stakeholders, including patients, caregivers and care teams across settings in the management of a patient's health. This is a dynamic process that requires data movement across platforms and among service providers in real time to successfully manage care.

2. Improving business and administrative processes: Having access to health information can eliminate time-consuming tasks within a health system, including processing of intake information, coordination across care teams and reporting needs associated with various regulatory requirements.

3. Increased patient safety and satisfaction: Having a full picture of a patient's health through access to disparate data can better inform clinicians on the patient's medical history, preferences and past encounters. This can help avoid duplicate testing, reduce adverse events, inform care decisions and conduct appropriate follow-up to ensure adherence with care management.

4. Value-based care: Population-level data analysis is an essential component of health systems managing high-risk patient populations, public health systems and other stakeholders working to coordinate care needs for a community. Understanding population data can enable better risk analysis and more cost-effective care. At an individual level, having access to longitudinal health data on a patient can reduce duplicative testing and close gaps in information. A key consideration in the shift to value-based care is the incorporation of non-traditional health data, such as an individual's social determinants of health data (e.g. food security status, housing stability and access to reliable transportation) into patient records. This data can be leveraged by health systems to proactively identify a health risk within a target population and intervene to work toward positive outcomes.

With these benefits in mind, taking a use-case driven approach can frame how information sharing may inform care or business practices and can help frame the potential benefits and drive interoperability forward. The following examples provide a snapshot at how interoperable exchange may occur.

Care delivery data: A physician can query a network or external health system for the appropriate patient data from other sources, such as other clinicians and hospitals. The physician can then select certain patient documents or resources (such as medication lists and reconciliations, allergy lists or lab reports) that they want to access during a patient visit.

Consent management: Where consent is not governed by other laws or regulations, a centralized consent management process can enable consensus among the community's stakeholders for a patient consent model, and provide awareness of a patient's willingness to participate in data sharing activities.

Lab results: HIE services provide physicians with the ability to get results directly via their exchange networks.

Medication reconciliation: Having a longitudinal view of all of a patient's encounters can raise awareness of the potential medications a patient was previously or is currently using. This can allow for appropriate follow up with the patient to reconcile differences in records to produce an accurate picture of their medication lists for future encounters.

Patient intake: Using a query and response process, the patient's health record can be acquired ahead of an appointment for clinician review.

Patient preferences: Exchange networks can enable access to information about a patient's care preferences, such as advance directives, empowering patients to make decisions in their care.

Provider alerts: HIE organizations are able to provide alerts to a clinician on their patients when one presents to the emergency department or is admitted to or discharged from a hospital, to ensure appropriate follow-up can be conducted.

Query services: Physicians may want to obtain more information about a patient that is referred to their office. They can query an exchange network which can in turn query other networks or health systems to assemble a complete, longitudinal health record.

Record locator services/master patient indexes: Many exchange networks enable creation of one continuous community record or search capabilities for patient records, facilitating patient identification across multiple provider settings.

Referrals: Organizations have the ability to send referrals directly through HIE services, along with all appropriate documentation.

Secure messaging: Many use cases require the exchange of patient records or related health information between clinicians in different healthcare settings with different EHRs or IT systems. For instance, providers needing to send referrals can electronically share data, eliminating paper documents and expediting the patient's treatment.

Transitions of care: As patients make the transition from one care setting to another (such as from hospital to a primary care provider or skilled nursing facility), interoperable exchange of relevant documentation from one care setting can be easily accessed at the next.

Workflow Considerations and Guidance

As the HIMSS definition suggests, interoperability goes beyond its technical requirements. Often there are policy changes influencing the value proposition and therefore willingness to exchange. Organizational considerations must align with external organizations and work toward achieving trust across stakeholders to enable meaningful data sharing. The following outlines some of these considerations and the approaches that may be leveraged.

Obtaining Patient Consent

Ensuring and communicating patient consent for data sharing activities is critical to abide by regulatory requirements and maintain the trust of the patient. There are several consent models used in information exchange, dependent on regulatory requirements within a jurisdiction.

No consent: Patient health information at a participating healthcare organization is automatically included in and available for exchange.

Opt-in: No patient data sets are made available for electronic exchange until patients actively express whether they would like to make all, or a pre-defined set, of their information available.

Opt-in with restrictions: No patient data sets are made available for electronic exchange until patients actively give their consent to participate. Patients have the option to:

Allow information to flow only to specific providers/li>

Include only specific categories of data or data elements

Make all of their information available for exchange

Opt-out: All or some predefined data sets are qualified to be included and available for exchange, after patients are given the opportunity to opt out in full.

Opt-out with exceptions: All or some predefined data sets are qualified to be included and available for exchange after patients are given the opportunity to:

Allow the exchange of their information only for specific purposes

Limit exchange of their information only for specific purposes

Limit exchange of their information to specific providers or provider organizations

Opt out in full

Selectively exclude categories of data or specific data elements from the exchange

Regardless of the model leveraged, a patient has the right to revoke their consent at any time, and processes to ensure that preference is communicated and enforced is important for data sharing processes.

Ensuring Privacy and Security

Regulations such as GDPR in the EU, HIPAA in the U.S., and various other national and state laws and regulations set forth privacy and security requirements to safeguard health information when it is created, received, transmitted, or maintained by health systems and other organizations. Privacy and security considerations may include the following:

Executing and implementing data use and reciprocal support agreements (DURSA)-also known as trust agreements-with the health systems and other stakeholders. These agreements set forth who may access and exchange the data. Additionally, such agreements set forth standard privacy and security policies and procedures that govern data protection and use. Further, data is typically encrypted when data is exchanged from one organization to another.

While specific security requirements may differ depending upon the DURSA, a typical DURSA may call for each participant to maintain appropriate administrative, physical and technical safeguards to ensure the confidentiality, integrity and availability of the data, aligned with the HIPAA requirements and any other applicable laws and/or regulations.

Producing High-Quality Data

In order to ensure that the information received from disparate sources can be trusted that it is accurate and complete, there needs to be efforts by organizations to maintain high-quality data. This can be achieved by ensuring that:

1. Data are structured and coded with standardized terminology and styles where possible to be leveraged in a machine-readable format for analysis and interpretation

2. Documentation is robust and complete to communicate the full patient story in a way that provides meaning to the human end user of the information.

Public Policy and Government Efforts

Achieving interoperability to date has required government involvement, guidance and regulation. Public policy efforts have the potential to drive forward frameworks for trusted exchange, align and educate stakeholders on existing and emerging standards and broaden stakeholder participation to ensure an inclusive exchange ecosystem for care coordination and continuity. The following outlines examples of how governments have worked to facilitate exchange both within the U.S. and in countries around the world.

United States Policies and Initiatives

With the goal of achieving ubiquitous, interoperable, nationwide exchange, the U.S. Department of Health and Human Services (HHS) has enacted and implemented a variety of legislation, regulations and guidance to further the adoption of standards-based approaches to interoperability.

21st Century Cures: Key Provisions

Among the health IT provisions outlined in 21st Century Cures, many sections provide directives to the ONC, Centers for Medicare and Medicaid Services (CMS) and other agencies related to improving interoperability.

The draft Trusted Exchange Framework and Common Agreement includes the policies, procedures and technical standards that build from existing health information network capabilities and enables them to work together to provide that single on-ramp to electronic health information regardless of the developer, health information exchange, or where a patient's records are located.

The U.S. Core Data for Interoperability is a "standardized set of health data classes and constituent data elements for nationwide, interoperable health information exchange." A "Data Class" is an "aggregation of various Data Elements by a common theme or use case."

The ONC Interoperability and Information Blocking Final Regulation implements key provisions of the 21st Century Cures Act focused on advancing interoperable exchange; supporting the access, exchange and use of electronic health information; and addressing occurrences of information blocking. The rule calls for accelerated use of healthcare APIs. ONC Certified Health IT must have standardized APIs for patient and population services, and meet specific requirements called out in the API Conditions and Maintenance of Certification. Most importantly, the regulation requires that United States Core Data for Interoperability is available for healthcare providers and patients "without special effort."

The Cures Act tasks ONC with the creation of an EHR Reporting Program as a condition of their certification and maintenance of certification. ONC has a process to solicit feedback from the industry on what criteria and methods of collection should be explored for this program. The program's reporting criteria aims to address the following five categories: security, interoperability, usability and user-centered design, conformance to certification testing, and other categories as appropriate to measure the performance of certified EHR technology.

In parallel with the release of the ONC Interoperability and Information Blocking Final Regulation, CMS also outlined an Interoperability and Patient Access Final Regulation, as part of their MyHealthEData Initiative, focused on driving exchange of and patient access to health information by liberating patient data using CMS authority to regulate Medicare Advantage, Medicaid, Children's Health Insurance Plan, and Qualified Health Plan issuers on the federally facilitated exchanges.

Interoperability Standards Advisory

The Interoperability Standards Advisory (ISA) process represents a single, public list of standards and implementation specifications published by ONC. ONC coordinates the identification, assessment and determination of these recommended standards and implementation specifications for industry use to fulfill specific clinical health IT interoperability needs. Stakeholders are encouraged to implement and use these recommended standards as applicable to their needs.

The ISA is organized into sections with related interoperability needs, each including one or more standards or implementation specifications to accomplish the listed need. Additional characteristics on these standards and implementation specifications are listed to inform stakeholders on maturity and adoption. These include:

1. Adoption Level: Approximate, average adoption level for that specific use case in healthcare within the United States.

2. Cost: Conveys whether a fee is involved to purchase, license, or obtain membership for access or use of the recommended standard or implementation specification.

3. Federally Required Status: Conveys whether a standard or implementation specification has been adopted in regulation, referenced as a federal program requirement, or referenced in a federal procurement (i.e., contract or grant).

4. Implementation Maturity: Conveys maturity based upon its implementation state.

5. Standards Process Maturity: Conveys maturity in terms of its stage within a particular organization's approval/voting process.

6. Test Tool Availability: Conveys whether a test tool is available to evaluate health IT's conformance to the standard or implementation specification.

Advancing Standards for Precision Medicine

This ONC project aims to standardize data found outside of traditional care delivery settings. As part of the precision medicine initiative, it focuses on standards in two areas:

1. Mobile health, sensor and wearable data

2. Social determinants of health data

Interoperability Proving Ground

ONC hosts an open, community platform where individuals and organizations can share and learn from projects occurring in the United States and internationally.

Data: Elemental to Health Campaign

HIMSS and PCHAlliance strongly urge prioritization of necessary funding and technical assistance to implement modern health information and technology strategies to ensure timely interoperability and sharing of public health surveillance data. An important component is the work in partnership with the Association of Public Health Laboratories, Council of State and Territorial Epidemiologists and the National Association for Public Health Statistics and Information Systems that support this multi-year effort to modernize public health data systems, surveillance and analytics at the CDC, and state, local and tribal health departments. Funding included in the Fiscal Year 2020 Appropriations Package and the Coronavirus Aid, Relief and Economic Security (CARES) Act, will help address some of the challenges, but in the long term, we need a predictable and sustained funding source to fully tackle this issue.

State-Level Interoperability Initiatives

In the U.S., state public health agencies manage and track information for immunizations, infectious diseases and vital statistics, however, they often lack adequate funding to ensure their information systems are updated and conform to national standards and profiles. Health information exchanges -which are public or private entities, depending on the state and region-aim to ensure there is robust and standardized data exchange between and among public health entities and state and local health departments.

Global Governmental Policies and Initiatives

At an international level, there are a variety of initiatives being led by health ministries and governments to further country-level and cross-border interoperability efforts.

The Global Digital Health Partnership (GDHP) is a collaboration of over 40 governments and territories, government agencies and the World Health Organization, formed to support the effective implementation of digital health services. Interoperability is one of the various work streams explored by GDHP. The proposed work stream activities focus on the evolving challenges of sharing patient data between care providers, organizations, caregivers and patients. These challenges are partly a technical problem for health systems, clinicians and patients, however they also pose significant risks for patient safety, and detract from high-quality coordinated care and the efficient delivery of services. A GDHP report outlines a variety of government efforts from 15 participating countries.

In Europe, the EU EHR Exchange Format developed the EU's eHealth Network to support the digital transformation of health and care in the EU by seeking to unlock the flow of health data across borders, and support implementation of the Cross-Border Health Care Directive. The recommendation seeks to facilitate the cross-border exchange of EHRs in the EU by supporting member states in their efforts to ensure that citizens can securely access and exchange their health data wherever they are in the EU. It will help citizens to quickly access and share their health data with healthcare professionals, for example, when consulting a specialist or receiving emergency treatment in another EU country.

The backbone infrastructure for the EHR exchange is the EHDSI. The EHDSI consists of services and infrastructure that use information and communication technologies to enable cross-border healthcare services. Initial focus is on enabling the exchange of baseline health information, including:

ePatient Summary to provide access to verified key health data of a patient during an unplanned care encounter while abroad

ePrescriptions to enables patients to receive equivalent medication treatment while abroad to what they would receive in their home country

A number of EU countries are already exchanging the ePatient summaries and ePrescriptions. As part of the EHDSI, Finland and Estonia were among the first to implement infrastructures to support the exchange of patient summaries, leveraging the X-Road platform, a free and open-source data exchange layer solution that enables organizations to exchange information securely over the internet. Future phases will include lab results, medical imaging and reports, and hospital discharge letters.

The European Commission also outlined priorities for 2019-2024 EU Digital Strategy, which included the creation of a European Health Data Space (EHDS) to foster the exchange and sharing of different kinds of health data (electronic health records, genomics, registries, etc.) in Europe. It aims to not only support exchange for the delivery of primary care, as well as, the development of new treatments, medicines, medical devices and services. Beyond the citizens, this would help meet the needs of different users and actors in the health system, while simultaneously protecting citizens' data. The commission is currently working with the member states and stakeholders to define the best governance structure and set up the appropriate infrastructure for the EHDS. Additionally, some existing regulatory gaps regarding member states implementation of the GDPR for the secondary use of data are being addressed.

The European Interoperability Framework (EIF) gives specific guidance on how to set up interoperable digital public services. It offers public administrations 47 concrete recommendations on how to improve governance of their interoperability activities, establish cross-organizational relationships, streamline processes supporting end-to-end digital services, and ensure that both existing and new legislation do not compromise efforts.

In an effort to ensure secure and federated access to genomic data, the European 1+ Million Genomes Initiative aims to create a technical infrastructure to improve disease prevention, allow for more personalized treatments and provide a sufficient scale for new clinically impactful research. In addition to the technical foundation for this effort, the signatory countries will also assess ethical and legal implications of genomics, and data storage, security and ethical uses.

Beyond these broader initiatives occurring across Europe, many countries are also leading specific regional and national efforts to expand exchange abilities./p>

The Nordic Interoperability Project is a consolidated grassroots effort to demonstrate the value for the patient from a seamless cross-border exchange of health data. The consortium includes organizations from all five Nordic countries-Denmark, Finland, Iceland, Norway, Sweden-and works to enable patients to live and act in an open, seamless, cross-border healthcare ecosystem, by showcasing and implementing solutions and innovations from the Nordics. The goal is to enable the Nordics by 2030 to be the most sustainable and integrated health region in the world, providing personalized healthcare for all its citizens.

In Portugal, the Shared Services of the Ministry of Health (SPMS) is a state-owned enterprise that manages the Portuguese Health Ministry Central Purchasing and serves as the national IT authority and the Portuguese representative for eHealth cross-border affairs. SPMS provides shared services in the areas of purchasing and logistics, financial services, human resources, and information and communication technologies to health and care entities to centralize and optimize the procurement of goods and services within the Portuguese National Health System.

In Asia and the South Pacific, many governments are also adopting digital health strategies to encourage interoperable exchange. For example, India's The Ministry of Health and Family Welfare (MoHFW) publishes EHR standards and created a Centre for Health Informatics under the eHealth Division of MoHFW as part of their Digital Health Strategy to initiate several digital health initiatives in the country, and started the National Health Portal as a citizen portal with the objective of improving the health literacy of the masses in India. New Zealand's health agencies, organizations and individuals signed a Commitment to New Zealand Health Interoperability to outline principles to drive the adoption of exchange infrastructures and processes in their health sector.

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Discover key insights about interoperability and better understand how data sharing can make a difference in healthcare.

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The Impact of Electronic Health Record Interoperability on Safety and Quality of Care in High-Income Countries: Systematic Review

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The Impact of Electronic Health Record Interoperability on Safety and Quality of Care in High-Income Countries: Systematic Review

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Review

Edmond Li, MB BCh BAO, MPH;

Jonathan Clarke, MA MB BChir, MPH, PhD;

Hutan Ashrafian, MBBS, MBA, PhD;

Ara Darzi, OM, KBE, PC;

Ana Luisa Neves, MBChB, MSc, PhD

National Institute for Health and Care Research (NIHR) Imperial Patient Safety Translational Research Centre, Institute of Global Health Innovation, Imperial College London, London, United Kingdom

Corresponding Author:

Edmond Li, MB BCh BAO, MPH

National Institute for Health and Care Research (NIHR) Imperial Patient Safety Translational Research Centre

Institute of Global Health Innovation

Imperial College London

Room 1035/7, QEQM Wing

St Mary's Hospital Campus

London, W2 1NY

United Kingdom

Phone: 44 02033127259

Fax:44 02033126309

Email: edmond.li19@imperial.ac.uk

Background: Electronic health records (EHRs) and poor system interoperability are well-known issues in the use of health information technologies in most high-income countries worldwide. Despite the abundance of literature exploring their relationship, their practical implications on patient safety and quality of care remain unclear.

Objective: This study aimed to examine how EHR interoperability affects patient safety, or other dimensions of care quality, in high-income health care settings.

Methods: A systematic search was conducted using 4 web-based medical journal repositories and grey literature sources. The publications included were published in English between 2010 and 2022, pertaining to EHR use, interoperability, and patient safety or care quality in high-income settings. Screening was completed by 3 researchers in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Risk of bias assessments were performed using the Risk of Bias in Non-randomized Studies of Interventions and the Cochrane Risk of Bias 2 tools. The findings were presented as a narrative synthesis and mapped based on the Institute of Medicine's framework for health care quality.

Results: A total of 12 studies met the inclusion criteria to be included in our review. The findings were categorized into 6 common outcome measure categories: patient safety events, medication safety, data accuracy and errors, care effectiveness, productivity, and cost savings. EHR interoperability positively influenced medication safety, reduced patient safety events, and reduced costs. Improvements in time saving and clinical workflow are mixed. However, true measures of effect are difficult to determine with certainty because of the heterogeneity in the outcome measures used and notable variation in study quality.

Conclusions: The benefits of EHR interoperability on the quality and safety of care remain unclear and reflect extensive heterogeneity in the interventions, designs, and outcome measures used. The establishment of common health information technology research outcome measures would support higher-quality research on the topic. Future research efforts should focus on both the positive and negative impacts of interoperable EHR interventions and explore patient perspectives, given the growing trend for patient involvement and stewardship over their own electronic clinical data.

Trial Registration: PROSPERO CRD42020209285; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=209285

International Registered Report Identifier (IRRID): RR2-10.1136/bmjopen-2020-044941

J Med Internet Res 2022;24(9):e38144

doi:10.2196/38144

Keywords

Background

The Healthcare Information and Management Systems Society defines interoperability as "the ability of different information systems, devices and applications (systems) to access, exchange, integrate and cooperatively use data in a coordinated manner, within and across organizational, regional and national boundaries, to provide timely and seamless portability of information and optimize the health of individuals and populations globally" []. However, this may have drastically different implications depending on one's role and perspective in a health system.

From a technological standpoint, EHR interoperability can be defined as "the ability of two or more applications to communicate effectively without compromising the content of the transmitted EHR " []. However, barriers such as hardware, syntax, and system usability often hinder the implementation of this vision [-]. The adoption of common standards in terminology, content, and security has been proposed to facilitate different levels of interoperability within or across health care settings [,].

From a public health, administrative, or policy-making perspective, EHR interoperability may entail "electronic health information that is shared appropriately between healthcare and public health partners in the right format, through the right channel at the right time " []. For end users such as health care providers and patients, the notion of interoperability often focuses more on the practical functionalities of EHR interoperability. For health care providers, this may include being able to remotely access care records from another health care setting, electronically correspond with other providers, and coordinate complex care plans with external health care organizations [,,-]. For patients, interoperability may mean a more seamless experience when seeking care from various health care providers or feeling an increased sense of empowerment by having greater access to their health records [].

Aims

This systematic review aims to evaluate the impact of EHR interoperability on the IoM's 6 domains of health care quality in HICs [].

Search Strategy

A literature search was conducted for publications published between 2010 and 2020 on 4 databases (PubMed, MEDLINE, Embase, and PsycINFO). Publications from grey literature sources and relevant papers identified from the references of the screened articles were also included. A more thorough description of the search strategy and inclusion criteria was previously published as a study protocol [].

We performed an additional search for publications published between March 2020 and June 2022. As the onset of COVID-19 has had profound implications on many aspects of health care technologies and policies, this supplementary search was conducted to account for any new studies published during that period. Otherwise, there were no further deviations from the methods described in the previously published protocol.

Study Selection Criteria

This systematic review included studies fulfilling the following criteria: (1) studies took place in HICs as defined by the World Bank where "the gross national income (GNI) per capita is higher than $13,205 USD " [,], (2) investigated EHRs or other health information technologies (HITs) that facilitate the sharing of clinical information between health care providers, and (3) contained outcomes concerning patient safety or quality of care. Only the studies published in English were included. No other filters, such as the study design, type, or publication country of origin, were used. Screening and selection of publications were performed by a total of 3 reviewers. Two reviewers initially independently screened the body of articles derived from the database searches to be considered for inclusion. This was performed iteratively at the title, abstract, and full-text levels in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) diagram []. Cohen κ statistic was used to measure interrater reliability []. Discrepancies in article selection were arbitrated by a third reviewer.

Data Extraction

Data extraction was completed by the first reviewer using a standardized Microsoft Excel spreadsheet. The content was then reviewed by the other 2 reviewers to ensure data quality and consistency. The characteristics and data extracted from each study included the name of the authors, year of publication, study design, study setting, study population and size, outcome measures, and general findings.

Risk of Bias and Quality Assessment

The Cochrane Risk of Bias Tool was used to assess randomized control trials for bias, whereas the "Risk of Bias in Non-Randomized Studies-of Interventions" tool was used for nonrandomized trials []. Risk of bias assessments were conducted by 2 reviewers and any disagreements were resolved by a third investigator.

Data Synthesis

A narrative synthesis of the findings was conducted. Relevant findings and outcome measures were grouped into subcategories and organized based on the 6 domains found in the IoM health care quality framework. Given the variety of outcome measures used in the included studies, a meta-analysis was not performed.

Overview

The initial search using computerized databases yielded 299 publications (). After screening the titles, 173 publications were excluded as they did not meet the inclusion criteria. Upon further screening of the abstracts, 45 articles were rejected because they were either not relevant or were not studies, but rather commentaries or opinion pieces. Following full-text screening and agreement among the 3 reviewers, 53 publications were excluded because they did not satisfy all the requirements of the PICO (population, intervention, control, and outcomes) inclusion criteria.

A final total of 12 papers were selected for this systematic review.

κ statistic for full-text screening was 0.52, indicating fair agreement [].

‎
Figure 1. PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) systematic review search strategy and screening process flow diagram. Search terms appear as used in Li et al [].
View this figure

Description of Included Studies

Table 1. Summary characteristics of included studies.

Study

Publication year

Journal

Study type

Stated aims or objectives

Date or duration of intervention

Study population or settings

Wong et al []

2020
Journal of the American Medical Directors Association
Observational study

2020

HOPE b SNF c Collaborative of 25 nursing facilities working with 3 hospitals in a local health network.

Biltoft et al []

2018
American Journal of Health-System Pharmacy
Case study

To improve IVd infusion:
‎

medication safety

accuracy, timeliness, and efficiency of IV medication documentation

Free up pharmacist and nurse time for direct patient care

Increase revenue by improving reimbursement for IV medications in outpatient areas

‎

October 2013, lasting for 7 months

Regional health system consisting of 8 hospitals, excludes NICUs e

D'Amore et al []

2018
AMIAfsymposium
Cross-sectional study

January 2018

854 C-CDA g documents were selected from the Office of the National Coordinator for Health Information Technology publicly available repository. After screening for duplicates, invalid XML, and documents not confirming to C-CDA 2.1 standards, 401 C-CDA documents were examined

Adams et al []

2017
Applied Clinical Informatics
Retrospective analysis of patient safety reports

Overall study was to understand patient safety consequences resultant from interoperability issues between EHRs and HITh. Specific objectives were:

To identify patient safety incident reports that reflect EHR interoperability challenges with other health IT.

To perform a detailed analysis of these reports to understand the health IT systems involved, the clinical care processes impacted, whether the incident occurred within or between provider organizations, and the reported severity of the patient safety events.

‎

2009-2016

1.735 million PSE i reports from the Pennsylvania Patient Safety Authority's Pennsylvania Patient Safety Reporting System, attained through the ISMP j, and a large health care system in the Mid-Atlantic United States; 209 (8%) PSE reports of the 2625 health IT reports were determined to be related to interoperability between the EHR and another health IT system.

Elysee et al []

2017
Medicine (United States)
Observational study

To empirically examine how the 3 capabilities (HIEk, interoperability, medication reconciliation) influence one another so the appropriate policy can be applied where it can have the greatest impact.
‎

2013

AHA l Annual IT Survey responses; 1330 hospitals were included. 2013 AHA Annual Survey IT Supplement database to obtain a nationally representative sample of nonfederal acute care hospitals that (1) include acute care general medical and surgical, general children's, and cancer hospitals (2) use any type of electronic exchange or sharing of care summaries with other providers

Koldby et al []

2013
Studies in Health Technology and Informatics
Simulation study

N/A m

Three doctors (2 surgeons, one pediatrician) and 3 medical secretaries, Herlev Hospital in Copenhagen, Denmark

a EHR: electronic health record.

b HOPE: Health Optimization for Elders.

c SNF: Skilled Nursing Facility.

d IV: intravenous.

e NICU: neonatal intensive care unit.

f AMIA: American Medical Informatics Association.

g C-CDA: consolidated clinical document architecture.

h HIT: health information technology.

i PSE: patient safety event.

j ISMP: Institute for Safe Medication Practices.

k HIE: health information exchange.

l AHA: American Hospital Association.

m N/A: not applicable.

Description of Interventions in Included Studies

Other studies included in this review encompassed interventions that enabled interoperability between hospital-based EHRs, primary care databases, as well as medical devices such as infusion pumps []. Across the reviewed studies, interoperability ranged from bidirectional reading and writing of data to unidirectional writing of clinical information to a medical device or record.

Outcomes

Summary of Outcome Measures

Table 2. Outcome measures explored by the included studies, mapped onto the Institute of Medicine health care quality framework [].

Safety

Effectiveness

Efficiency

Study, year

Patient safety events

Medication safety

Data sharing, accuracy, and errors

Care effectiveness

Productivity

Cost savings

Reed et al [], 2020

✓ a

✓

Wong et al [], 2020

✓

Howe et al [], 2018

✓

✓

Biltoft et al [], 2018

✓

✓

✓

✓

D'Amore et al [], 2018

✓

✓

Adams et al [], 2017

✓

✓

Elysee et al [], 2017

✓

Motulsky et al [], 2016

✓

✓

Akbarov et al [], 2015

✓

Munck et al [], 2016

✓

✓

Koldby et al [], 2013

✓

✓

Lee et al [], 2013

✓

✓

a ✓: denotes that the specified outcome measure was present and explored in the study.

Patient Safety Events

Medication Safety

Medication-related measures are some of the most used outcome measures for assessing the impact of EHR interoperability on patient safety.

Data Sharing, Accuracy, and Errors

The relationship between EHR interoperability and its impact on data accuracy, sharing, and errors was explored in 5 studies.

Taking a more longitudinal perspective, D'Amore et al [] investigated how data quality in EHRs changed in American Veterans Affairs (VA) hospitals through the increasing use of systems with greater interoperability. Using 3 independent evaluation tools, the authors found a general increase in the scope and accuracy of the clinical data being shared []. With HL7 Schematron testing, 86.3% (346/401) of electronic health documents contained 1695 errors, averaging 4.9 errors per clinical document []. Finally, using a data quality algorithm, 21,304 alerts (indicating issues in either the completeness or syntax of the record) were generated from the 401 documents examined, averaging 53.1 alerts per document []. 57% of these alerts were triggered due to issues surrounding data completeness, and 43% from syntax [].

A more recent study by Wong et al [], performed during the initial months of the COVID-19 pandemic, explored the introduction of a novel workflow process using software to integrate outpatient e-fax data inputs to inpatient EHRs to improve data sharing between transitions of care and limit disease transmission. Nine weeks after debuting their new process, the authors reported that it was utilized in 287 instances across the three-hospital system, with uptake and use trending positively []. Feedback from hospital staff was also largely positive, with only minor instances of data entry errors such as the system not handling double-sided documents properly and delays taking more than the usual hour []. However, the authors provided little detail as to why the number of e-faxes received, and the timeframe of nine weeks post-implementation was selected as the outcome measure for assessing the effectiveness of their intervention. As such, their purported findings must be interpreted with circumspection, given the limitations of the study design and likely biases present.

Care Effectiveness

Productivity

Of the 12 studies, 3 investigated the impact of interoperability on the efficiency of clinicians [,,]. Of these, one study quantified the exact time saved by the clinicians []. Munck et al [] primarily assessed the time spent by clinicians to perform medication reconciliation. They found that the time expended per patient using an EHR interoperable with patients' historical medication records vs. a standalone EHR system was not significantly lengthened or statistically significant (5 minutes 27 seconds vs 4 minutes 15 seconds) [].

A simulation study conducted by Koldby et al [] primarily examined whether integrating a digital dictation system into an EHR system would reduce unintended clinical incidents. While the authors expected improvements in this domain, they also hypothesized the simultaneous emergence of some unintended consequences, such as clinical documentation storage errors resulting from the novel intervention being introduced []. The participants reported notable improvements in their clinical workflows and time savings, as access to dictation services, transcripts, and medical records can be made via one click in the EHR itself. However, this benefit is ultimately offset by the limited functionality of the EHR system owing to the suboptimal integration of the dictation system []. Frequent system lockups, inability to open windows to other commonly used applications when dictations were being performed, and poor interoperability with other hospital systems (eg, retrieving data from laboratory information systems) all contribute to curtailing potential workflow benefits []. Furthermore, no workflow-related benefits were observed for other supporting clinical staff, such as medical secretaries [].

Cost Savings

Risk of Bias Assessment

‎
Figure 2. Risk of Bias in Non-Randomized Studies-of Interventions traffic lights plot for domain-level risk of bias judgments for nonrandomized studies.
View this figure

‎
Figure 3. Risk of Bias in Non-Randomized Studies-of Interventions summary plot of biases present in nonrandomized studies included in the review [-].
View this figure

‎
Figure 4. Risk of Bias 2 traffic lights plot for domain-level risk of bias judgments in randomized studies.
View this figure

Summary of Principal Findings

Altogether, EHR interoperability appeared to have some beneficial effects on both PSEs and medication safety or reconciliation. Implementing interoperable EHRs was also associated with reduced data entry errors and improved overall data quality and scope [,]. However, the impact of EHR interoperability on timeliness and improvements to clinical workflow remains inconclusive; as described by Munck et al [], where the time expended to complete the clinical task increased following EHR interoperability with a community medication list.

Strengths and Limitations

However, there are notable limitations to our systematic review. The first is the decision to restrict the review to include only publications completed in English and focused on HICs. This limits the ability of this review to capture the experiences of EHRs in non-English health care settings or low- and middle-income countries. Although EHR systems are commonly found in HICs, and thus would likely have more extensive experience regarding the problem of interoperability, the authors acknowledge that these inclusion criteria can introduce a risk for bias.

Second, our study highlights the positive impact of interoperable EHRs on various outcome measures; we did not identify many negative findings. This relative lack of reported negative impact can likely be attributable to publication bias, where studies with negative findings are less likely to be published, and thus not available to be included in our review. Health IT interventions with positive outcomes also often garner more robust follow-up research efforts and deflect resources away from exploring interventions that demonstrate negative results, thus further reducing the visibility of studies investigating the latter [].

Finally, the studies included in our review had an overall high risk of bias. Even more robust studies that manage to provide greater specificity in their methods and results are often limited in scope or outcomes assessed. We examined these in detail using recognized tools and provided a comprehensive summary, so that the results can be interpreted despite these limitations. Together, these limitations made it challenging to draw any definitive conclusions regarding the overall magnitude of the effect that EHR interoperability has on improving patient safety or care quality, as well as its generalizability to other health systems.

Comparison With Prior Work

A systematic review by Chaudhry et al [] explored the effects of HIT adoption on quality of care and efficiency []. This study concluded that the introduction of HITs resulted in greater adherence to guidelines or protocols and had a positive effect on improving medication safety (ie, higher rates of adverse drug event identification and reduction in adverse drug events) []. Chaudhry et al [] also described some negative consequences of their adoption and use, including mixed effects on health care provider time use and a general lack of reliable data to determine financial costs. The authors emphasized the urgent need for research into commercial EHR systems and the adoption of common standards for use in HIT research [].

Although the review by Jones et al [] review covered HITs instead of solely EHRs, the authors described a similar impression of the HIT literature landscape as that observed in our study: diverse outcome measures, unreliable reporting of findings, and inconsistent study quality, resulting in an unclear understanding of the impact on the quality of care that patients received.

Another noteworthy review was that by Rahurkar et al [], who examined how HIE use affected health care measures such as costs, use of services, and care quality. The review included 27 articles, with the majority focusing on HIE use in the United States (70%), emergency department settings (52%), and hospitals (26%) []. The authors noted that many observational studies reported HIEs as having a positive effect on their outcome measure of interest, with a large proportion of those reporting on quality of care (80%). However, when accounting for study design, especially those using methods with higher internal validity (eg, randomized control trials), the authors found no strong evidence supporting HIE use being causally related to any purported benefits [].

Implications for Policy and Further Research

As demonstrated in the literature, piecemeal interventions aimed at linking EHR systems at a technical level may alleviate bottlenecks in one area of the clinical pathway but only to have the gains undermined elsewhere and culminate in not meaningfully improving the patient's care during their clinical encounter.

Given that interoperable EHR use by health care workers has already been extensively researched, similar efforts must be devoted to investigating the exact benefits to patient safety or quality of care from the perspective of patients and caregivers. In addition, further research efforts should be devoted to exploring EHR interoperability interventions that do not yield positive results. Understanding these failed interventions and their underlying causes can help better inform our insight into interventions that have shown greater promise.

The adoption of common technical standards and support for regulatory and legislative alignment will prove valuable in further ushering in greater interoperability in the coming years. Likewise, unifying a common set of key performance indicators is also needed to allow for more transparent and comparable metrics for the continued monitoring and evaluation of new policies. Outcomes such as PSEs, medication reconciliation, and time savings may be obvious starting points, but a more consolidated list of universal outcome measures is essential to accurately quantify the effect of these complex interventions.

Conclusions

Our systematic review found that interoperable EHRs have had a positive impact on certain aspects of patient safety, such as medication reconciliation, reducing PSEs, lowering the risk of data errors, and improving data quality. However, a reliable determination of their true measure of effect with the available assortment of evidence remains difficult. Current evidence underscores the value and importance of continuing to implement greater interoperability in the upgrading of existing EHR systems and the procurement of new ones. In practice, however, relying on present findings to inform exact outcome measure improvements to expect, may prove challenging.

As clinical data increase both in volume and complexity, EHR interoperability will become indispensable for realizing a more streamlined and sustainable workflow for clinicians. Standardizing outcome measures, examining EHR interoperability through the lens of systems complexity, and greater inclusivity of patient perspectives in EHR-related research will be necessary to better evaluate the growing importance of interoperable EHRs in high-income health care settings in the foreseeable future.

Acknowledgments

This work was supported by the Imperial College National Institute for Health Research Patient Safety Translational Research Centre (reference no PSTRC-2016-004). The funders or sponsors have had no role in the development and drafting of this manuscript.

Conflicts of Interest

HA is chief scientific officer of Preemptive Health and Medicine at Flagship Pioneering. AD is executive chair of Preemptive Health and Medicine at Flagship Pioneering.
‎ ‎
‎

EHR: electronic health record

HIC: high-income country

HIE: health information exchange

HIT: health information technology

IoM: Institute of Medicine

PICO: population, intervention, control, and outcomes

PRISMA: Preferred Reporting Items for Systematic Reviews and Meta-Analyses

PSE: patient safety event

Edited by A Mavragani; submitted 21.03.22; peer-reviewed by Z Reis, C Sullivan, Z Mahmoudvand, K Matthias; comments to author 15.06.22; revised version received 18.07.22; accepted 23.08.22; published 15.09.22

©Edmond Li, Jonathan Clarke, Hutan Ashrafian, Ara Darzi, Ana Luisa Neves. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 15.09.2022.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

Background: Electronic health records (EHRs) and poor system interoperability are well-known issues in the use of health information technologies in most high-income countries worldwide. Despite the abundance of literature exploring their relationship, their practical implications on patient safety and quality of care remain unclear.

Objective: This study aimed to examine how EHR interoperability affects patient safety, or other dimensions of care quality, in high-income health care settings.

Methods: A systematic search was conducted using 4 web-based medical journal repositories and grey literature sources. The publications included were published in English between 2010 and 2022, pertaining to EHR use, interoperability, and patient safety or care quality in high-income settings. Screening was completed by 3 researchers in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Risk of bias assessments were performed using the Risk of Bias in Non-randomized Studies of Interventions and the Cochrane Risk of Bias 2 tools. The findings were presented as a narrative synthesis and mapped based on the Institute of Medicine's framework for health care quality.

Results: A total of 12 studies met the inclusion criteria to be included in our review. The findings were categorized into 6 common outcome measure categories: patient safety events, medication safety, data accuracy and errors, care effectiveness, productivity, and cost savings. EHR interoperability positively influenced medication safety, reduced patient safety events, and reduced costs. Improvements in time saving and clinical workflow are mixed. However, true measures of effect are difficult to determine with certainty because of the heterogeneity in the outcome measures used and notable variation in study quality.

Conclusions: The benefits of EHR interoperability on the quality and safety of care remain unclear and reflect extensive heterogeneity in the interventions, designs, and outcome measures used. The establishment of common health information technology research outcome measures would support higher-quality research on the topic. Future research efforts should focus on both the positive and negative impacts of interoperable EHR interventions and explore patient perspectives, given the growing trend for patient involvement and stewardship over their own electronic clinical data.

Trial Registration: PROSPERO CRD42020209285; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=209285

International Registered Report Identifier (IRRID): RR2-10.1136/bmjopen-2020-044941

J Med Internet Res 2022;24(9):e38144

doi:10.2196/38144

Keywords

Background

The Healthcare Information and Management Systems Society defines interoperability as "the ability of different information systems, devices and applications (systems) to access, exchange, integrate and cooperatively use data in a coordinated manner, within and across organizational, regional and national boundaries, to provide timely and seamless portability of information and optimize the health of individuals and populations globally" []. However, this may have drastically different implications depending on one's role and perspective in a health system.

From a technological standpoint, EHR interoperability can be defined as "the ability of two or more applications to communicate effectively without compromising the content of the transmitted EHR " []. However, barriers such as hardware, syntax, and system usability often hinder the implementation of this vision [-]. The adoption of common standards in terminology, content, and security has been proposed to facilitate different levels of interoperability within or across health care settings [,].

From a public health, administrative, or policy-making perspective, EHR interoperability may entail "electronic health information that is shared appropriately between healthcare and public health partners in the right format, through the right channel at the right time " []. For end users such as health care providers and patients, the notion of interoperability often focuses more on the practical functionalities of EHR interoperability. For health care providers, this may include being able to remotely access care records from another health care setting, electronically correspond with other providers, and coordinate complex care plans with external health care organizations [,,-]. For patients, interoperability may mean a more seamless experience when seeking care from various health care providers or feeling an increased sense of empowerment by having greater access to their health records [].

Aims

This systematic review aims to evaluate the impact of EHR interoperability on the IoM's 6 domains of health care quality in HICs [].

Search Strategy

A literature search was conducted for publications published between 2010 and 2020 on 4 databases (PubMed, MEDLINE, Embase, and PsycINFO). Publications from grey literature sources and relevant papers identified from the references of the screened articles were also included. A more thorough description of the search strategy and inclusion criteria was previously published as a study protocol [].

We performed an additional search for publications published between March 2020 and June 2022. As the onset of COVID-19 has had profound implications on many aspects of health care technologies and policies, this supplementary search was conducted to account for any new studies published during that period. Otherwise, there were no further deviations from the methods described in the previously published protocol.

Study Selection Criteria

This systematic review included studies fulfilling the following criteria: (1) studies took place in HICs as defined by the World Bank where "the gross national income (GNI) per capita is higher than $13,205 USD " [,], (2) investigated EHRs or other health information technologies (HITs) that facilitate the sharing of clinical information between health care providers, and (3) contained outcomes concerning patient safety or quality of care. Only the studies published in English were included. No other filters, such as the study design, type, or publication country of origin, were used. Screening and selection of publications were performed by a total of 3 reviewers. Two reviewers initially independently screened the body of articles derived from the database searches to be considered for inclusion. This was performed iteratively at the title, abstract, and full-text levels in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) diagram []. Cohen κ statistic was used to measure interrater reliability []. Discrepancies in article selection were arbitrated by a third reviewer.

Data Extraction

Data extraction was completed by the first reviewer using a standardized Microsoft Excel spreadsheet. The content was then reviewed by the other 2 reviewers to ensure data quality and consistency. The characteristics and data extracted from each study included the name of the authors, year of publication, study design, study setting, study population and size, outcome measures, and general findings.

Risk of Bias and Quality Assessment

The Cochrane Risk of Bias Tool was used to assess randomized control trials for bias, whereas the "Risk of Bias in Non-Randomized Studies-of Interventions" tool was used for nonrandomized trials []. Risk of bias assessments were conducted by 2 reviewers and any disagreements were resolved by a third investigator.

Data Synthesis

A narrative synthesis of the findings was conducted. Relevant findings and outcome measures were grouped into subcategories and organized based on the 6 domains found in the IoM health care quality framework. Given the variety of outcome measures used in the included studies, a meta-analysis was not performed.

Overview

The initial search using computerized databases yielded 299 publications (). After screening the titles, 173 publications were excluded as they did not meet the inclusion criteria. Upon further screening of the abstracts, 45 articles were rejected because they were either not relevant or were not studies, but rather commentaries or opinion pieces. Following full-text screening and agreement among the 3 reviewers, 53 publications were excluded because they did not satisfy all the requirements of the PICO (population, intervention, control, and outcomes) inclusion criteria.

A final total of 12 papers were selected for this systematic review.

κ statistic for full-text screening was 0.52, indicating fair agreement [].

‎
Figure 1. PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) systematic review search strategy and screening process flow diagram. Search terms appear as used in Li et al [].
View this figure

Description of Included Studies

Table 1. Summary characteristics of included studies.

Study

Publication year

Journal

Study type

Stated aims or objectives

Date or duration of intervention

Study population or settings

Wong et al []

2020
Journal of the American Medical Directors Association
Observational study

2020

HOPE b SNF c Collaborative of 25 nursing facilities working with 3 hospitals in a local health network.

Biltoft et al []

2018
American Journal of Health-System Pharmacy
Case study

To improve IVd infusion:
‎

medication safety

accuracy, timeliness, and efficiency of IV medication documentation

Free up pharmacist and nurse time for direct patient care

Increase revenue by improving reimbursement for IV medications in outpatient areas

‎

October 2013, lasting for 7 months

Regional health system consisting of 8 hospitals, excludes NICUs e

D'Amore et al []

2018
AMIAfsymposium
Cross-sectional study

January 2018

854 C-CDA g documents were selected from the Office of the National Coordinator for Health Information Technology publicly available repository. After screening for duplicates, invalid XML, and documents not confirming to C-CDA 2.1 standards, 401 C-CDA documents were examined

Adams et al []

2017
Applied Clinical Informatics
Retrospective analysis of patient safety reports

Overall study was to understand patient safety consequences resultant from interoperability issues between EHRs and HITh. Specific objectives were:

To identify patient safety incident reports that reflect EHR interoperability challenges with other health IT.

To perform a detailed analysis of these reports to understand the health IT systems involved, the clinical care processes impacted, whether the incident occurred within or between provider organizations, and the reported severity of the patient safety events.

‎

2009-2016

1.735 million PSE i reports from the Pennsylvania Patient Safety Authority's Pennsylvania Patient Safety Reporting System, attained through the ISMP j, and a large health care system in the Mid-Atlantic United States; 209 (8%) PSE reports of the 2625 health IT reports were determined to be related to interoperability between the EHR and another health IT system.

Elysee et al []

2017
Medicine (United States)
Observational study

To empirically examine how the 3 capabilities (HIEk, interoperability, medication reconciliation) influence one another so the appropriate policy can be applied where it can have the greatest impact.
‎

2013

AHA l Annual IT Survey responses; 1330 hospitals were included. 2013 AHA Annual Survey IT Supplement database to obtain a nationally representative sample of nonfederal acute care hospitals that (1) include acute care general medical and surgical, general children's, and cancer hospitals (2) use any type of electronic exchange or sharing of care summaries with other providers

Koldby et al []

2013
Studies in Health Technology and Informatics
Simulation study

N/A m

Three doctors (2 surgeons, one pediatrician) and 3 medical secretaries, Herlev Hospital in Copenhagen, Denmark

a EHR: electronic health record.

b HOPE: Health Optimization for Elders.

c SNF: Skilled Nursing Facility.

d IV: intravenous.

e NICU: neonatal intensive care unit.

f AMIA: American Medical Informatics Association.

g C-CDA: consolidated clinical document architecture.

h HIT: health information technology.

i PSE: patient safety event.

j ISMP: Institute for Safe Medication Practices.

k HIE: health information exchange.

l AHA: American Hospital Association.

m N/A: not applicable.

Description of Interventions in Included Studies

Other studies included in this review encompassed interventions that enabled interoperability between hospital-based EHRs, primary care databases, as well as medical devices such as infusion pumps []. Across the reviewed studies, interoperability ranged from bidirectional reading and writing of data to unidirectional writing of clinical information to a medical device or record.

Outcomes

Summary of Outcome Measures

Table 2. Outcome measures explored by the included studies, mapped onto the Institute of Medicine health care quality framework [].

Safety

Effectiveness

Efficiency

Study, year

Patient safety events

Medication safety

Data sharing, accuracy, and errors

Care effectiveness

Productivity

Cost savings

Reed et al [], 2020

✓ a

✓

Wong et al [], 2020

✓

Howe et al [], 2018

✓

✓

Biltoft et al [], 2018

✓

✓

✓

✓

D'Amore et al [], 2018

✓

✓

Adams et al [], 2017

✓

✓

Elysee et al [], 2017

✓

Motulsky et al [], 2016

✓

✓

Akbarov et al [], 2015

✓

Munck et al [], 2016

✓

✓

Koldby et al [], 2013

✓

✓

Lee et al [], 2013

✓

✓

a ✓: denotes that the specified outcome measure was present and explored in the study.

Patient Safety Events

Medication Safety

Medication-related measures are some of the most used outcome measures for assessing the impact of EHR interoperability on patient safety.

Data Sharing, Accuracy, and Errors

The relationship between EHR interoperability and its impact on data accuracy, sharing, and errors was explored in 5 studies.

Taking a more longitudinal perspective, D'Amore et al [] investigated how data quality in EHRs changed in American Veterans Affairs (VA) hospitals through the increasing use of systems with greater interoperability. Using 3 independent evaluation tools, the authors found a general increase in the scope and accuracy of the clinical data being shared []. With HL7 Schematron testing, 86.3% (346/401) of electronic health documents contained 1695 errors, averaging 4.9 errors per clinical document []. Finally, using a data quality algorithm, 21,304 alerts (indicating issues in either the completeness or syntax of the record) were generated from the 401 documents examined, averaging 53.1 alerts per document []. 57% of these alerts were triggered due to issues surrounding data completeness, and 43% from syntax [].

A more recent study by Wong et al [], performed during the initial months of the COVID-19 pandemic, explored the introduction of a novel workflow process using software to integrate outpatient e-fax data inputs to inpatient EHRs to improve data sharing between transitions of care and limit disease transmission. Nine weeks after debuting their new process, the authors reported that it was utilized in 287 instances across the three-hospital system, with uptake and use trending positively []. Feedback from hospital staff was also largely positive, with only minor instances of data entry errors such as the system not handling double-sided documents properly and delays taking more than the usual hour []. However, the authors provided little detail as to why the number of e-faxes received, and the timeframe of nine weeks post-implementation was selected as the outcome measure for assessing the effectiveness of their intervention. As such, their purported findings must be interpreted with circumspection, given the limitations of the study design and likely biases present.

Care Effectiveness

Productivity

Of the 12 studies, 3 investigated the impact of interoperability on the efficiency of clinicians [,,]. Of these, one study quantified the exact time saved by the clinicians []. Munck et al [] primarily assessed the time spent by clinicians to perform medication reconciliation. They found that the time expended per patient using an EHR interoperable with patients' historical medication records vs. a standalone EHR system was not significantly lengthened or statistically significant (5 minutes 27 seconds vs 4 minutes 15 seconds) [].

A simulation study conducted by Koldby et al [] primarily examined whether integrating a digital dictation system into an EHR system would reduce unintended clinical incidents. While the authors expected improvements in this domain, they also hypothesized the simultaneous emergence of some unintended consequences, such as clinical documentation storage errors resulting from the novel intervention being introduced []. The participants reported notable improvements in their clinical workflows and time savings, as access to dictation services, transcripts, and medical records can be made via one click in the EHR itself. However, this benefit is ultimately offset by the limited functionality of the EHR system owing to the suboptimal integration of the dictation system []. Frequent system lockups, inability to open windows to other commonly used applications when dictations were being performed, and poor interoperability with other hospital systems (eg, retrieving data from laboratory information systems) all contribute to curtailing potential workflow benefits []. Furthermore, no workflow-related benefits were observed for other supporting clinical staff, such as medical secretaries [].

Cost Savings

Risk of Bias Assessment

‎
Figure 2. Risk of Bias in Non-Randomized Studies-of Interventions traffic lights plot for domain-level risk of bias judgments for nonrandomized studies.
View this figure

‎
Figure 3. Risk of Bias in Non-Randomized Studies-of Interventions summary plot of biases present in nonrandomized studies included in the review [-].
View this figure

‎
Figure 4. Risk of Bias 2 traffic lights plot for domain-level risk of bias judgments in randomized studies.
View this figure

Summary of Principal Findings

Altogether, EHR interoperability appeared to have some beneficial effects on both PSEs and medication safety or reconciliation. Implementing interoperable EHRs was also associated with reduced data entry errors and improved overall data quality and scope [,]. However, the impact of EHR interoperability on timeliness and improvements to clinical workflow remains inconclusive; as described by Munck et al [], where the time expended to complete the clinical task increased following EHR interoperability with a community medication list.

Strengths and Limitations

However, there are notable limitations to our systematic review. The first is the decision to restrict the review to include only publications completed in English and focused on HICs. This limits the ability of this review to capture the experiences of EHRs in non-English health care settings or low- and middle-income countries. Although EHR systems are commonly found in HICs, and thus would likely have more extensive experience regarding the problem of interoperability, the authors acknowledge that these inclusion criteria can introduce a risk for bias.

Second, our study highlights the positive impact of interoperable EHRs on various outcome measures; we did not identify many negative findings. This relative lack of reported negative impact can likely be attributable to publication bias, where studies with negative findings are less likely to be published, and thus not available to be included in our review. Health IT interventions with positive outcomes also often garner more robust follow-up research efforts and deflect resources away from exploring interventions that demonstrate negative results, thus further reducing the visibility of studies investigating the latter [].

Finally, the studies included in our review had an overall high risk of bias. Even more robust studies that manage to provide greater specificity in their methods and results are often limited in scope or outcomes assessed. We examined these in detail using recognized tools and provided a comprehensive summary, so that the results can be interpreted despite these limitations. Together, these limitations made it challenging to draw any definitive conclusions regarding the overall magnitude of the effect that EHR interoperability has on improving patient safety or care quality, as well as its generalizability to other health systems.

Comparison With Prior Work

A systematic review by Chaudhry et al [] explored the effects of HIT adoption on quality of care and efficiency []. This study concluded that the introduction of HITs resulted in greater adherence to guidelines or protocols and had a positive effect on improving medication safety (ie, higher rates of adverse drug event identification and reduction in adverse drug events) []. Chaudhry et al [] also described some negative consequences of their adoption and use, including mixed effects on health care provider time use and a general lack of reliable data to determine financial costs. The authors emphasized the urgent need for research into commercial EHR systems and the adoption of common standards for use in HIT research [].

Although the review by Jones et al [] review covered HITs instead of solely EHRs, the authors described a similar impression of the HIT literature landscape as that observed in our study: diverse outcome measures, unreliable reporting of findings, and inconsistent study quality, resulting in an unclear understanding of the impact on the quality of care that patients received.

Another noteworthy review was that by Rahurkar et al [], who examined how HIE use affected health care measures such as costs, use of services, and care quality. The review included 27 articles, with the majority focusing on HIE use in the United States (70%), emergency department settings (52%), and hospitals (26%) []. The authors noted that many observational studies reported HIEs as having a positive effect on their outcome measure of interest, with a large proportion of those reporting on quality of care (80%). However, when accounting for study design, especially those using methods with higher internal validity (eg, randomized control trials), the authors found no strong evidence supporting HIE use being causally related to any purported benefits [].

Implications for Policy and Further Research

As demonstrated in the literature, piecemeal interventions aimed at linking EHR systems at a technical level may alleviate bottlenecks in one area of the clinical pathway but only to have the gains undermined elsewhere and culminate in not meaningfully improving the patient's care during their clinical encounter.

Given that interoperable EHR use by health care workers has already been extensively researched, similar efforts must be devoted to investigating the exact benefits to patient safety or quality of care from the perspective of patients and caregivers. In addition, further research efforts should be devoted to exploring EHR interoperability interventions that do not yield positive results. Understanding these failed interventions and their underlying causes can help better inform our insight into interventions that have shown greater promise.

The adoption of common technical standards and support for regulatory and legislative alignment will prove valuable in further ushering in greater interoperability in the coming years. Likewise, unifying a common set of key performance indicators is also needed to allow for more transparent and comparable metrics for the continued monitoring and evaluation of new policies. Outcomes such as PSEs, medication reconciliation, and time savings may be obvious starting points, but a more consolidated list of universal outcome measures is essential to accurately quantify the effect of these complex interventions.

Conclusions

Our systematic review found that interoperable EHRs have had a positive impact on certain aspects of patient safety, such as medication reconciliation, reducing PSEs, lowering the risk of data errors, and improving data quality. However, a reliable determination of their true measure of effect with the available assortment of evidence remains difficult. Current evidence underscores the value and importance of continuing to implement greater interoperability in the upgrading of existing EHR systems and the procurement of new ones. In practice, however, relying on present findings to inform exact outcome measure improvements to expect, may prove challenging.

As clinical data increase both in volume and complexity, EHR interoperability will become indispensable for realizing a more streamlined and sustainable workflow for clinicians. Standardizing outcome measures, examining EHR interoperability through the lens of systems complexity, and greater inclusivity of patient perspectives in EHR-related research will be necessary to better evaluate the growing importance of interoperable EHRs in high-income health care settings in the foreseeable future.

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The Impact of Electronic Health Record Interoperability on Safety and Quality of Care in High-Income Countries: Systematic Review

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Background: Electronic health records (EHRs) and poor system interoperability are well-known issues in the use of health information technologies in most high-income countries worldwide. Despite the abundance of literature exploring their relationship, their practical implications on patient safety and quality of care remain unclear.

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Converting data into information and knowledge

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Converting data into information and knowledge

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The purpose of the anesthetic record has always been to "accurately and comprehensively capture a patient's anesthetic experience in a succinct format."[ 1] Like the rest of medical documentation, it is used to facilitate future patient care, support accurate billing, provide a legal record, as well as serving as source material for research, quality improvement, and population health management. A serendipitous result in the case of the anesthesia record has been that its format facilitated the synthesis of large volumes of data on single patients, enabling visual comparisons and identification of trends and relationships that suggested changes in clinical care and workflows, as well as potential directions for future research. Early anesthesia records (pre-1890s), like nearly all medical records, were ledger-based rather than individual patient-based; literally, they were contained in ledger books with descriptive entries handwritten in for each successive anesthetic or patient visit. This changed in the decades around 1900, with a switch to individual patient-based records, both in anesthesia specifically (E.A. Codman and Harvey Cushing at the Massachusetts General Hospital)[ 2] and medical records more generally (Henry Plummer at the Mayo Clinic).[ 3] Since this time, when Codman and Cushing first utilized a time-based graph of vital signs and anesthetic events, the anesthesia record has in fact changed very little in its basic format. The switch to this "modern" anesthesia record was significant because it facilitated the visual comparisons of data mentioned above to take place. When these pieces of "data" could be compared (e.g., drugs administered and vital signs measured), patterns of interactions could be observed producing "information" (e.g., blood pressure is frequently reduced after thiopental administration); further observation of and research into these interactions eventually resulted in the creation of "knowledge" (e.g., the pharmacodynamic effects of thiopental can produce significant hypotension, especially in hypovolemic patients or those with limited cardiac reserve).

Over a century later, we are in the midst of the next significant change in anesthesia records, and medical records as the conversion from paper to electronic records takes place. This has facilitated the next quantum change in knowledge generation. Now bringing previously unimaginable volumes of data into databases allows for potential improvements and efficiencies in patient care, billing, research, quality improvement, as well as offering clinical decision support and resource allocation in ways that were simply not possible on paper.[ 4] However, despite all the potential advantages, adoption of electronic records in the US and worldwide is still not universal. While assessments of anesthesia information management system (AIMS) usage in the US vary widely, from about 30% in all hospitals up to 75% in academic centers, it is clear that most anesthetics performed in the US in 2015 are still charted on paper.[ 5, 6] This compares with up to 82% of all US physicians using electronic health records (EHR) to enter and read clinical notes.[ 7] Moreover, in spite of the promise, there is very little evidence to show that the potential, economic value, and patient care benefits of AIMS or EHR have been realized. To quote an expert, "systems that are in use in multiple locations, that have satisfied users, and that effectively and efficiently contribute to the quality and safety of care are few and far between."[ 8]

Why does this situation exist? The answers are complex and controversial, but it is important to note that EHR vendors have traditionally focused on reimbursement, regulatory, and medical-legal requirements, not usability (user interface or "user experience [UX]") or interoperability. These same vendors have also largely marketed integrated, enterprise-wide "one size fits all" systems to hospital leadership, not the end clinical users of their systems, depriving those same clinical users of much say in their adoption.[ 9] And in cases where end users, specifically anesthesiologists do have a say, they have often not embraced electronic systems due to actual and perceived problems with usability, data accuracy, as well as the difficulty with showing positive return on investment.[ 10]

A realistic assessment of AIMS in 2015 shows strengths and weaknesses. What an AIMS does well - it collects physiologic data from a multitude of devices autonomously, accurately, rapidly, and seamlessly, and translates them into a legible record, freeing the anesthetist to monitor and treat the patient rather than record vital signs. What an AIMS does not do well in many cases is integrate easily into existing workflows without producing delay, distracting, and disruption (poor "UX"). Data entry that requires user input is often nonintuitive, and while every AIMS is capable of producing a nice legible static output of a single case, many are challenged when other types of output, say to an external database or reporting system, is required. To paraphrase many frustrated users, "our AIMS is okay, except for the fact that it's hard to get data into it and hard to get data back out again."

The future potential of an AIMS that interacts with a well-designed EHR is undeniable. The large volume data collection referenced above, made possible by electronic systems, will be crucial in providing data about potential effects of anesthetic choices not only on perioperative morbidity and mortality, but on long-term outcomes as well.[ 11] There is now a body of evidence suggesting possible effects of anesthetic choices on cancer recurrence; more robust and comprehensive data from well-designed electronic databases will help convert this from early information about possible associations into practice-changing knowledge.[ 12]

It is clear, however, that a promising future has not been enough to make the adoption of AIMS and EHRs universal. Obviously, it needs to become easier and more intuitive to get data into and out of these systems. While open source EHRs exist, the market in the US has been dominated by proprietary vended systems. However, there is an emerging movement toward open application programming interfaces on the part of large EHR vendors that may make data input and output easier and more user-friendly. The two largest integrated EHR vendors in the US market, Epic[ 13] and Cerner,[ 14] have begun to take significant steps in this direction. Still in its early stages, this movement facilitates the development of custom applications by third parties (or users themselves) for data entry, reporting, patient access, or whatever else a particular user might require. As a gross oversimplification, this in a sense turns the integrated EHR into an "operating system" (think iOS or Android) and allows users to buy/build custom "apps" to perform specific tasks. In the not so distant future, the large vended EHR products may become the transactional base system, running in the background, and the end user (clinician, researcher, billing personnel, and even patient) may be using any one (or several) out of thousands of apps to "get stuff into the record" and "get it back out again." At that point, users may not even really be aware of which "EHR" or "AIMS" they are using; they will only know the tools they need to know in order to take care of patients, generate bills, explore new relationships between data, enter data from their own wearable activity monitor, etc. It is then that the promise of electronic records will be realized.

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Converting data into information and knowledge: The promise and the reality of electronic medical records

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The purpose of the anesthetic record has always been to "accurately and comprehensively capture a patient's anesthetic experience in a succinct format."[ 1] Like the rest of medical documentation, it is used to facilitate future patient care, support accurate billing, provide a legal record, as well as serving as source material for research, quality improvement, and population health management.

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Abstract

Use of electronic health records (EHRs)is becoming more and more common. It is anticipated that their use will improve patient care, decrease practice costs, and increase provider productivity and revenue. The State of Hawai'i has received funding to assist Hawai'i and Pacific providers in their selection of qualified EHR products that will meet federal Medicare and Medicaid requirements for "Meaningful Use." This article describes federal recommendations and resources available to Hawai'i providers. While it can be fairly overwhelming for small practices to convert to EHRs, there is technical assistance available to support Hawai'i providers in meeting the Meaningful Use requirements of EHR usage in order to become eligible for financial incentives and to improve positive health outcomes for patients. On October 31st, 2011, there were 138,570 eligible professionals and eligible hospitals registered for the Medicare and Medicaid EHR Incentive Programs nationally. These providers have received $1,238,000,000 in incentives since January 2011. In Hawai'i there are approximately 400 active registrations for the Medicare program, of which 95 have been successful at meeting Meaningful Use criteria and have received $8.1 million in incentive payments from Medicare. The State of Hawai'i is preparing to launch its Medicaid EHR incentive program in mid-to-late 2012, which should produce additional payments to Hawai'i's health care providers.

Background

The Institute of Medicine estimated that approximately 98,000 Americans die each year from preventable medical errors.1 Medical errors are killing more people per year in the United States than breast cancer, AIDS, and motor vehicle accidents combined. Eighty percent of errors were initiated by miscommunication.2 A major transformation in the current 2A American health care system is underway, and the demand for more accountable care and better communication is creating the momentum to address the need for improvement in clinical care management, the capture of data for continuity of care, and the definition of measures to monitor costs and quality of care.

The Office of the National Coordinator for Health Information Technology (ONC) was created in 2004 to be a resource for the entire US health care system.3 In 2009, the ONC was mandated through the Health Information Technology for Economic and Clinical Health (HITECH) Act to support and coordinate efforts to improve health care through the adoption of health information technology (HIT) and the development of a nationwide health information exchange (HIE). This is the HIT component of the American Recovery and Reinvestment Act (ARRA) signed into law on February 17, 2009, with provisions of 17.2 billion dollars for EHR use and HIE development. The vision of the HITECH Act is to furnish tools to begin a transformation in our nation's health care system so that each patient can receive optimal care. The adoption of Meaningful Use of electronic patient data and the development of a nationwide exchange of health information over the next several years will serve to optimize and align our combined efforts to improve health outcomes, reduce costs, and increase patient, staff, and provider health care experience and satisfaction.

In 2011, the National Center for Health Statistics estimated that the rate of providers using any EHR was between 40% in Louisiana to 84% in North Dakota, with Hawai'i having a 70.5% adoption rate.4 Any EHR system was defined as an affirmative response to the question, "Does this practice use electronic health records (not including billing records)?"

In comparison, a "basic EHR system" is one that has all of the following functionalities: patient history and demographics, patient problem list, physician clinical notes, comprehensive list of patient's medications and allergies, computerized orders for prescriptions, and ability to view laboratory and imaging results electronically. This may or may not include certified electronic health record technology as specified by ONC Certified Health IT Product List.5 Nationally, about 34% of physicians reported having a system that met the criteria for a basic system, up from 25% average in 2010. The lowest state was New Jersey at 16% and the highest was Minnesota at 61%. In Hawai'i, the EHR adoption rate for a basic system is 46.3%.

Nationwide, approximately 52% of physicians reported intending to apply for Meaningful Use incentives, up from 41% in 2010. While the barriers to the implementation of EHRs include potentially high financial investments, an increase in initial physician and staff training time, workflow redesign efforts, and the need to hire new staff for HIT support, an EHR creates a database of information that will assist in the coordination of patient care and improvement of communication about shared patients among health care providers.

Benefits of using EHRs can include, but are not restricted to, reduced paperwork for patients and doctors, expanded access to affordable care, improved patient quality of care, prevention of medical errors, decrease in health care costs, increase in administrative efficiencies, and engagement of patients/families in their own health care.

An EHR converts a paper medical record into electronic format for faster communication, recall, and clinical decision-making. The goal of clinical decision support is designed to deploy electronic and non-electronic tools to effectively make use of best practices and evidence-based guidelines to help guide care in a more timely manner. Furthermore, the use electronic health records fundamentally changes a practice's communications by redesigning workflows that allow for better access to patient information almost immediately. Transcription costs will be reduced, patients with specific conditions can be more quickly identified and clinical decision support tools will be in place.

The What and Why of Meaningful Use

The term "Meaningful Use" applies to the extent to which a health care provider or organization utilizes electronic health records. The American Recovery and Reinvestment Act (ARRA) defines Meaningful Use as the following:

1. Use of certified EHR in a meaningful manner (eg, E-prescribing);

2. Use of certified EHR technology for electronic exchange of health information to improve quality of health care; and,

3. Use of certified EHR technology to submit clinical quality reporting and other measures.

The government has identified three stages toward meeting their definition of Meaningful Use. The Stage 1 goal is to capture data electronically. The Stage 2 goal is to expand upon Stage 1 criteria and to report health information and track key clinical conditions. The Stage 3 goal is to improve quality, safety, effi ciency, and performance and health outcomes. Meaningful Use includes both a core set and a menu set of objectives that are specific to eligible professionals or eligible hospitals and critical access hospitals. All of the metrics are described in detail at the Centers for Medicare and Medicaid Services (CMS) website.6

An example of a core objective is for an eligible professional to "generate and transmit permissible prescriptions electronically (eRx)." The definition of e-prescribing is a prescriber's ability to electronically send an accurate, error-free, and understandable prescription directly to a pharmacy from the point-of-care. This is an important element in improving the quality of patient care. In Stage 1, this requirement is successfully measured when more than 40% of all permissible prescriptions written by the eligible professional are transmitted electronically using certified EHR technology. In Stage 2, the suggestion is for the percentage requirement to increase to 50% of orders transmitted as eRx. In Stage 3, the suggested threshold for this measure is to increase to 80% of orders transmitted as eRx. See Table 1 .

Table 1

Stage 1

Proposed Stage 2

Proposed Stage 3

More than 40% of permissible prescriptions are transmitted electronically.

More than 50% of orders (outpatient and hospital discharge) transmitted as eRx.

More than 80% of orders (outpatient and hospital discharge) transmitted as eRx.

This HIT-enabled health reform approach aims to capture data in a coded format and expand exchange of information in the most structured format possible in order to advance clinical processes and improve health outcomes by placing emphasis on high priority clinical conditions, patient self-management, and access to comprehensive data. Therefore, the focus is not just about technology, but about improving health and transforming health care through Meaningful Use of HIT.

Financial Incentives for Achieving Meaningful Use

Through the Centers for Medicare and Medicaid Services (CMS) EHR Incentive Programs, Medicare and Medicaid are providing financial incentives for "eligible professionals" (EPs) who have adopted a certified EHR and can demonstrate "Meaningful Use." Participating eligible professionals must choose to be reimbursed either through Medicare or Medicaid and cannot receive incentive payments from both programs at the same time. Below is a description of each program.

Medicare EHR Incentive Program

The Medicare eligibility criteria for EPs include doctors of medicine, osteopathy, dental surgery, podiatry, optometry or chiropractic care. Excluded from this list are EPs who furnish 90% or more of their services in either the inpatient or emergency department of a hospital. (Eligible hospitals include subsection (d) hospitals and Critical Access Hospitals).

Beginning January 2011, EPs who adopt and demonstrate Meaningful Use of an EHR may be eligible for up to $44,000 in Medicare incentive payments spread out over five years. (This amount is increased by 10% for EPs who predominantly furnish services in a health professional shortage area.) The last year to initiate the Medicare program is 2014 with the last payment in 2016. It is important to note that from 2013, the incentive payments start to decrease every year thereafter. Medicare payment reductions begin in 2015 for providers that do not demonstrate Meaningful Use. The reporting period for meeting Meaningful Use the first year is 90 continuous days within the calendar year and for subsequent years it is the full calendar year.

Medicaid EHR Incentive Program

The Medicaid eligibility criteria for EPs are inclusive of physicians, dentists, certified nurse-midwives, nurse practitioners, and also for physician assistants working in a federally qualified health center / rural health center that is physician-assistant led. Under Medicaid, EPs must practice in a setting where 30% of patient encounters are attributed to Medicaid patients, with the exception of pediatricians who must have a minimum volume of 20% Medicaid patients. However, their incentive payments are limited to two-thirds of the maximum amount or $42,500. Pediatricians with 30% Medicaid population are eligible for the maximum payout of $63,750. (Eligible hospitals include acute care hospitals and critical access hospitals with at least 10% Medicaid patient volume, as well as children's hospitals with no Medicaid volume requirement.) As with Medicare eligibility, Medicaid EPs who furnish 90% or more of their services in either the inpatient or emergency department of a hospital are excluded from the list.

In some states, eligible professionals have begun receiving Medicaid payments as early as January 2011. Unlike Medicare, EPs can participate in the Medicaid program and receive the maximum payment through 2016. This means that the last year for eligible professionals to both initiate and register for the Medicaid EHR incentive program is 2016. EPs may receive up to a maximum of $63,750, with the first year payment being $21,250 for providers who are engaged in efforts to "adopt, implement or upgrade" (AIU) to certified EHR technology. The Medicaid program payments are made over a six-year time period. The last Medicaid EHR payment year is 2021.

An eligible professional has "adopted" a certified EHR when they have evidence of acquiring and installing the technology. When the EP has "implemented" a certified EHR, then he/she has commenced utilization of the technology, eg, staff training and data entry of patient demographic information into the EHR. The EP has "upgraded" the existing EHR by expanding its current technology or adding new functionality to meet the definition of certified EHR technology. In their first year, the eligible professional will not have to demonstrate Meaningful Use in order to receive the payment; he/she just needs to adopt, implement, or upgrade certified EHR technology. In the second year, he/she will have to demonstrate Meaningful Use for a 90-day period only. An EP who has already achieved Meaningful Use will need to demonstrate Meaningful Use of their certified EHR for a 90-day period the first year, and then in subsequent years, he/she will need to demonstrate Meaningful Use of their certified EHR for the full calendar year.

Medicaid Meaningful Use requirements are reported through a means that is developed by each individual state that chooses to participate. The final Meaningful Use rule mandates that the objectives of Meaningful Use for Medicare be the minimum for Medicaid. The State of Hawai'i Medicaid Program (Department of Human Services, Med-QUEST Division) received funds to begin development of the advanced implementation plan and reporting process for Hawai'i's eligible professionals to submit documentation to CMS, with the intent of administering the Medicaid EHR incentives in the fall of 2012. The purpose of the advance planning document is to describe the Hawai'i Department of Human Services, Med-QUEST Division's (DHS-MQD) implementation activities for the development of a State Medicaid Health Information Technology Plan (SMHP) for Hawai'i. The SMHP will serve as a strategic vision to enable the DHS-MQD to achieve its future vision by moving from the current "As-Is" HIT landscape to the desired "To-Be" HIT landscape, including a comprehensive HIT road map and strategic plan over the next five years. The plan will also describe the processes, procedures and resources required to implement and oversee the incentive payments to be paid to eligible Hawai'i Medicaid providers for the Meaningful Use of certified EHRs. It is encouraged that Hawai'i EPs begin preparing now to achieve Meaningful Use, regardless of the program chosen, in order to prepare for future financial incentive opportunities.

Differences in Medicare and Medicaid EHR Incentive Programs

Eligible professionals (EPs) must select only one method to receive the financial incentives, and are allowed to switch programs one time prior to 2015 after they have completed their attestation. Providers practicing in multiple states can only participate in one state's program. Table 2 below shows the notable differences between the Medicare and Medicaid EHR Incentive programs.

Table 2

Medicare

Medicaid

Federal Government will implement (will be an option nationally)

Voluntary for states to implement (may not be an option in every state)

Payment reductions begin in 2015 for providers that do not demonstrate Meaningful Use

No Medicaid payment reductions

Must demonstrate Meaningful Use in Year 1

Adopt/Implement/Upgrade option for 1st participation year

Maximum incentive is $44,000 for EPs (10% bonus for EPs in HPSAs)

Maximum incentive is $63,750 for EPs

Meaningful Use definition is common for Medicare

States can adopt certain additional requirements for Meaningful Use

Last year a provider may initiate program is 2014
Last year to register is 2016; Payment adjustments begin in 2015

Last year a provider may initiate program is 2016
Last year to register is 2016

Only physicians, subsection (d) hospitals and Critical Access Hospitals

5 types of EPs, acute care hospitals (including critical access hospitals) and children's hospitals

Demonstrating and Reporting Meaningful Use

For 2011, results for all objectives and measures, including clinical quality measures, is reported via attestation to the Centers for Medicare and Medicaid Services (CMS). For 2012, measures must be submitted via certified EHR technology. In the provider's first year of participation, the reporting period will be any 90-day period that occurs within the calendar year. For subsequent years of participation, the reporting period will be the entire calendar year.

Penalties for Non-Adopters of Meaningful Use

To receive the maximum EHR incentive payment, Medicare eligible professionals must begin participation by 2012. For 2015 and later, Medicare eligible professionals who do not successfully demonstrate Meaningful Use will have a payment adjustment to their Medicare reimbursement. The payment reduction starts at 1% and increases 1% each year that a Medicare eligible professional does not demonstrate Meaningful Use, to a maximum of 5%.

There are no penalties for EPs who choose the Medicaid method, which means that there is no payment adjustment imposed if an eligible provider that qualifies for Medicaid and subsequently chooses NOT to adopt, implement, or upgrade to certified EHR. If an eligible professional adopts, implements or upgrades to certified EHR in the first year, the EP will not have to demonstrate Meaningful Use in order to receive the first year's payment.

What Can Your Practice Do Now to Achieve Meaningful Use?

For practices with no EHR, develop an EHR implementation strategy by focusing on the following:

begin pre-work by performing a financial assessment and return on investment calculation, assigning a physician as a champion and selecting staff for an EHR implementation team;

determine where you are now in the process by performing a readiness assessment, evaluate staff computer skills, analyze your current workflow, and review hardware and software needs;

start planning by deciding where you want to be in the future and how to get there, by defining EHR goals and measurements and drafting an internal project plan for implementation;

evaluate the certified vendors to see which system meets your needs by scheduling demonstrations and negotiating contracts;

begin the EHR implementation process and effectively install the EHR by working with your vendor on implementation planning, system customization/testing/practicing/backup and establishing down time procedures; and,

prepare a post go-live evaluation by reviewing goals, Meaningful Use status, clinical measure reports, and subsequent phase planning.

For practices with an EHR, start now by focusing on the following:

inquire on EHR vendor status of ARRA certification and find out when upgrades will be available;

review reporting requirements and work backwards to ensure that documentation supports required reports;

if possible, implement a lab interface and eRx with interaction checking;

begin using computerized physician order entry (CPOE);

perform gap analysis of documentation: medications, problem, and allergy lists, vitals, etc;

create lists of patients within the EHR using clinical decision support rules and send patient reminders;

implement a patient portal;

implement a medication reconciliation process;

develop a source of statewide information on community HIE and test one exchange of key health data to other providers, health department, or immunization registry; and,

conduct a HIPAA security analysis and address issues.

Overview of the Regional Extension Center Program

The HITECH Act7 has established 62 nationwide regional extension centers, including the Hawai'i Pacific Regional Extension Center (HPREC), in order to offer technical assistance, guidance and information on best practices to support and accelerate health care providers' efforts to become "meaningful users" of EHRs. The coordinated adoption and use of secure EHRs will ultimately enhance the quality and value of health care across the country.

Regional Extension Centers focus their most intensive technical assistance on priority primary care providers (PPCPs), including MDs, DOs, nurse practitioners, physician assistants, and certified nurse mid-wives furnishing primary-care services (ie, adolescent medicine, family practice, general practice, internal medicine, OB, GYN, geriatrics, or pediatrics), with a particular emphasis on individual and small group practices (fewer than ten providers with prescriptive privileges). PPCPs in such practices deliver the majority of primary care services, but have the lowest rates of adoption of EHR systems and the least access to resources to help them implement, use, and maintain such systems. Regional Extension Centers also focus rigorous technical assistance on providers providing primary care in public and critical access hospitals, community health centers, and in other settings that predominantly serve uninsured, underinsured, and medically underserved populations.

As of December 2011, over half of the individual and small group practices in Hawai'i have not yet implemented basic EHR systems. The HPREC is prepared to assist practices in Hawai'i with or without EHR systems currently in place and can support health care providers with direct, individualized, and on-site technical assistance in: (1) selecting a certified EHR product that offers the best value for the providers' needs; (2) achieving effective implementation of a certified EHR product; (3) enhancing clinical and administrative workflows to optimally leverage an EHR system's potential to improve quality and value of care; (4) observing and complying with applicable legal, regulatory, professional, and ethical requirements to protect the integrity, privacy, and security of patients' health information; and (5) preparing the required reports to become eligible to receive EHR incentive payments.

The HPREC was established to serve all of the Hawaiian Islands, as well as Guam, American Samoa, and the Commonwealth of the Northern Mariana Islands. The HPREC is a program that is managed by the Hawai'i Health Information Exchange (Hawai'i HIE).8 The Hawai'i HIE partners with health care providers to improve patient care, improve operational efficiency, reduce operating costs, receive financial incentives, and become part of a statewide information network for the exchange of electronic health records.

EHR Vendor Identification and Selection Process

Eligible professionals will need to implement an EHR technology that is certified for meeting Meaningful Use and is listed on the ONC certified health IT product list. Certification will promote EHR adoption by giving providers assurance that products and systems will help them achieve Meaningful Use. The choice of the specific, certified technology used should be driven by clinical goals and operations rather than by restrictive certification requirements.

The HPREC is open to working with any certified EHR vendor who meets the selection criteria and who is preferred by the health care provider. Notably, the vendor selection criteria developed by the HPREC goes beyond the national EHR vendor certification requirements and accounts for other requirements, including reliable on-island support needed for Hawai'i and Pacific island providers.

The HPREC has developed an analytical process utilizing vendor milestone reviews and due diligence in order to determine a list of qualified electronic health record vendors, which included the following steps:

1. Identifying EHR vendors with installations in Hawai'i;

2. Identifying other national vendors to participate in process;

3. Creating pre-screening list of questions;

4. Creating survey for interested vendors to complete which helps to profile their business in Hawai'i;

5. Holding meetings or sent out correspondence explaining our process and asked all interested vendors to respond by completing pre-screening questions and survey;

6. Reviewing detailed Request for Proposal (RFP) and provided input regarding any Hawai'i specific requirements;

7. Identifying short list of vendors to receive detailed RFI;

8. Selecting pre-qualified vendors,

Furthermore, the selection process consisted of meeting the following milestones:

1. Reviewing and analyzing EHR vendor pre-screen responses;

2. RReviewing and analyzing EHR vendor survey data;

3. Reviewing and analyzing EHR vendor Request for Information responses;

4. Vendor reference checking;

5. Vendor price negotiating and structuring;

6. Vendor contract negotiating and structuring.

These milestones encompass analysis of Hawai'i specific functionality and requirements, clinical features and functionality, vendor business and organizational profiles, implementation and training models, support and maintenance models, and technology platform, framework, and scalability of systems.

The Hawai'i Health Information Exchange

In September 2009, the Hawai'i HIE was recognized as the State Designated Entity (SDE) through a cooperative agreement from the State Health Information Exchange Cooperative Agreement Program funded by the ONC. The mission of the Hawai'i HIE is to facilitate the exchange of health information that enables quality health care statewide and to provide reliable health information when and where it is needed.

This will be accomplished by:

Achieving widespread and sustainable HIE within and among states through the Meaningful Use of electronic health records (EHRs);

Establishing and implementing appropriate governance, policies, and network services within the national frame work to build connectivity between and among health care providers;

Improving the capability of providers to actively exchange health care data focusing specifically on electronic orders and receipt of labs and test results as well as e-prescribing;

Developing and implementing up-to-date privacy and security requirements for HIE;

Organizing directories and technical services to enable interoperability within and across states and remove barriers that may hinder effective HIE;

Coordinating with Medicaid and state public health programs to enable information exchange and support monitoring of provider participation in the HIE;

Convening health care stakeholders who can provide support for a statewide approach to HIE.

Implications and Conclusion

According to a press release on November 30, 2011 by the US Department of Health and Human Services (HHS),9 Secretary Kathleen Sebelius released a new report revealing that doctors' adoption of health information technology has doubled in two years. Doctors who responded quickly will qualify for CMS EHR incentive payments in 2011 as well as 2012 for meeting Stage 1 of Meaningful Use of certified EHR technology. To encourage acceleration in EHR adoption, the Secretary announced that HHS intends to continue to allow doctors and hospitals to adopt HIT in 2012 and 2013 without meeting the new Stage 2 Meaningful Use requirements until 2014.

There are many benefits for early adopters of Stage 1 Meaningful Use. Providers are given additional time to implement new software and meet the new requirements of Stage 2 and vendors are given added time to develop certified EHR technologies for Stage 2. Maintaining the current expectations for those first attesting to Meaningful Use in 2012 will mean that all providers attesting in 2011 and 2012 will not need to begin Stage 2 until 2014. There is added incentive for providers to attest as soon as possible and receive the rewards of being an early participant. Those providers attesting in 2012 will have two payment years under the less stringent Stage 1 criteria, rather than one year. The Stage 2 and Stage 3 criteria are expected to be more robust in coming years.

As EPs and health care organizations reach Meaningful Use of EHRs that ultimately enable the exchange of critical information across a health care system, the health care system will realize improvements in individual and population health outcomes, increased transparency and efficiency, and improved ability to study and improve care delivery.

With advances in recent HIT, workforce development roles across the nation are being created and new skill sets are being developed for health care professionals to support the implementation of EHRs, practice workflow redesign, and information management training.

Meaningful Use of EHRs is an initiative to expand as well as standardize, streamline, and improve the use of health information technology in our country. The target for nationwide health care reform is to improve the quality of health of our patient population which leads to more patient-centered as well as transparent and accountable care. This can be accomplished effectively through the wide-spread use of EHRs and exchange of health information.

Health care providers in Hawai'i will benefit from HIT because they will have accurate and complete information about a patient's health, thus enabling the provider to give the best possible care, whether during a routine visit or a medical emergency. Providers will also have the ability to better coordinate the care they give, which is especially important if a patient has a serious medical condition. They will have a way to share information with patients and their family caregivers through secure information technology network communication. This means patients and their families can more fully participate in decisions about their own health care. In addition, health care providers will have information and data necessary to diagnose health problems sooner, reduce medical errors, and provide safer care at lower costs. Communication between health professionals and health entities will become more efficient and effective.

Acknowledgements

The authors wish to acknowledge the assistance of Alan Ito, Hawai'i Pacific Regional Extension Center Project Director, Gregory Suenaga, Health Information Exchange Project Director, and Beth Siegfried, Hawai'i Health Information Exchange Communications and Marketing Manager, for their contribution to the preparation of this manuscript.

Conflict of Interest

None of the authors identify any conflict of interest.

References

1. Kohn L, Corrigan J, Donaldson M. To Err Is Human: Building a Safer Health System. Washington, D.C.: Institute of Medicine & Committee on Quality of Health Care in America; 1999. [ Google Scholar]

2. Increase in the US Medication-Error Deaths between 1983 and 1993. The Lancet. 1998;351:643-644. [[ PubMed] Google Scholar]

4. Chun-Ju Hsiao, PhD, Esther Hing, MPH, Socey Tomas C, Bill Cai., MA Sci . Electronic Health Record Systems and Intent to Apply for Meaningful Use Incentives Among Office-based Physician Practices: United States, 2001-2011. 79. National Center for Health Statistics (NCHS) Data Brief; 2011. Nov, [[ PubMed] Google Scholar]

Articles from Hawai'i Journal of Medicine & Public Health are provided here courtesy of University Health Partners of Hawaii

# data[].attributes.attachment_link.title
Transforming and Improving Health Care through Meaningful Use of Health Information Technology

# data[].attributes.attachment_link.description
Use of electronic health records (EHRs)is becoming more and more common. It is anticipated that their use will improve patient care, decrease practice costs, and increase provider productivity and revenue. The State of Hawai'i has received funding to assist Hawai'i and Pacific providers in their selection of qualified EHR products that will meet federal Medicare and Medicaid requirements for "Meaningful Use."

# data[].attributes.attachment_link.title
Health Information Exchange | HealthIT.gov

# data[].attributes.attachment_link.description
The demand for electronic health information exchange among care professionals is growing along with nationwide efforts to improve the quality, safety, and efficiency of health care delivery. Meaningful use requirements, new payment approaches that stress care coordination, and federal financial inc…

# data[].attributes.attachment_link.description
Telehealth includes health care services provided using audio and video technology. Telehealth was originally developed to provide basic care to rural and underserved patients. Higher rates of use of telehealth are now standard in many practices since…

# data[].attributes.attachment_link.title
Consumer Health Informatics: The Application of ICT in Improving
Patient-Provider Partnership for a Better Health Care

# data[].attributes.attachment_link.description
There is a growing interest concerning the potential of ICT solutions that
are customized to consumers. This emerging discipline referred to as
consumer health informatics (CHI) plays a major role in providing
information to patients and the public, and…

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Original Paper

¹ College of Nursing, University of Florida, Gainesville, FL, United States

² School of Nursing, Columbia University, New York, NY, United States

Corresponding Author:

Rebecca Schnall, PhD, MPH

School of Nursing

Columbia University

560 West 168th Street

New York, NY, 10032

United States

Phone: 1 2123426886

Email: rb897@columbia.edu

Background: Mobile health (mHealth) apps have the potential to be effective tools for encouraging patients with chronic diseases to self-manage their health. The success of mHealth apps is related to technology acceptance and its subsequent use by intended consumers. Therefore, it is essential to gain insights from consumers' perspectives about their use of mHealth apps in daily life.

Objective: The purpose of this work was to understand consumers' perspectives on use of a self-management app following completion of a clinical trial that tested the efficacy of the app for improving health outcomes.

Methods: We conducted five focus groups with paricipants of a clinical trial (NCT03182738) who were randomized to use the video information provider (VIP) for HIV-associated nonAIDS (HANA) conditions app (VIP-HANA) or an attention control app. Thematic analysis was conducted, and the themes were organized according to the two key constructs of the technology acceptance model framework: perceived usefulness and perceived ease of use.

Results: Thirty-nine people living with HIV (20 from the intervention group and 19 from the control group) participated in the focus group sessions. Of the eight themes identified from focus group data, the five themes related to perceived usefulness were: (1) self-monitoring HIV-related symptoms of HANA conditions, (2) enhanced relationship with clinical providers, (3) improvement in physical and emotional health, (4) long-term impact of self-care strategies on improvement in symptoms of HANA conditions, and (5) inspired lifestyle changes to manage symptoms. The three themes related to perceived ease of use were: (1) easy to navigate, (2) avatar personalization, and (3) privacy/confidentiality maintained even when changing the location of app use.

Conclusions: Perceived ease of use was similar in both study groups but perceived usefulness differed between study groups. Participants in both study groups found the VIP-HANA app to be useful in monitoring their symptoms and enhancing communication with their clinical care providers. However, only intervention group participants perceived the app to be useful in improving overall health and long-term symptom management. Findings from this study highlight factors that are essential to ensure the usefulness of self-management apps and facilitate sustained use of mHealth apps for people living with chronic illnesses.

J Med Internet Res 2020;22(6):e17708

doi:10.2196/17708

Keywords

With the widespread proliferation of smartphones, the use of mobile technology has rapidly increased in health care [,]. Consumer health informatics tools such as mobile health (mHealth) apps have the potential to be effective tools to encourage patients with chronic diseases to self-manage their health [-]. The video information provider (VIP) for HIV-associated nonAIDS (HANA) conditions app (VIP-HANA) is an mHealth app that was developed to help people living with HIV self-manage their symptoms by providing self-care strategies []. Development of the VIP-HANA app is described elsewhere []. In short, the VIP-HANA app is comprised of 728 self-care strategies for the following 28 HANA condition-related symptoms: fatigue, depression, muscle aches, difficulty falling asleep, anxiety, difficulty staying asleep, difficulty remembering, neuropathy, difficulty concentrating, decreased sex drive, diarrhea, maintaining an erection, shortness of breath, constipation, dry mouth, clumsiness, weight loss, dizziness, heartburn, dry eyes, changes in appetite, ringing in ear, cough, nausea/vomiting, fever, difficulty with urination, speech difficulties, and pain during sex []. The 728 self-care strategies were tailored based on the symptom, gender, race/ethnicity, and HANA condition of each user. An avatar guides users through the app. Patients then receive self-care strategies with animated videos for ameliorating their symptoms. Users review their symptom experience over time with symptom reports, set a weekly email reminder to complete sessions in the app, and email/download the history of their reported symptoms and suggested self-care strategies [].

Two versions of the app were created to assess the efficacy of the VIP-HANA app in improving the symptom burden in a 6-month randomized controlled trial (RCT) (NCT03182738) that included 100 people living with HIV with HANA conditions (50 randomly assigned to the intervention group, 50 assigned to the control group). All participants were asked to log in at least once per week. Both study groups received weekly symptom assessment questions through the VIP-HANA app, yet only the intervention group participants were provided with the self-care strategies. presents a comparison of the two versions of the VIP-HANA app features. Participants in both groups were given US $5 per week for their time and data costs associated with completing the weekly symptom assessments.

According to a recent US national survey of mHealth app use, approximately half (46%) of the respondents reported downloading a health app but were no longer using it or had uninstalled it []. Given that the success of mHealth apps is related to the acceptance of technology and its subsequent use by intended consumers, there is a need to gain in-depth insights from consumers' perspectives about use of mHealth apps in their daily life. Accordingly, the purpose of this study was to understand consumers' perspectives on mHealth use (VIP-HANA app) following an RCT of an HIV symptom self-management intervention for people living with HANA conditions.

Sampling and Recruitment

Procedures

The Institutional Review Board of Columbia University Medical Center reviewed and approved all research activities. We conducted three focus group sessions with intervention group participants (N=20) and two focus groups sessions with control group participants (N=19). Prior to the focus group sessions, all participants were provided an explanation of the study procedures and completed a signed consent form. Using open-ended questions via a semistructured focus group guide designed based on the app's features/functionality (), a moderator (a faculty member at Columbia University School of Nursing) facilitated the focus group sessions, and participants were encouraged to discuss issues regarding their perceptions related to use of the VIP-HANA app (intervention/control group versions). All focus group sessions were audio-recorded. Data collection continued until saturation of themes was reached. Participants were compensated with US $30 for their time.

Focus group guide based on 6-month experience of using the video information provider for HIV-associated nonAIDS conditions (VIP-HANA) app.
Experience Using VIP-HANA

Please describe your experience using the VIP-HANA app in social settings.
Probe: where and when did you use it most (house/workplace/clinic/café; after breakfast/when commuting, etc) and why?

How did this fit into your lifestyle and schedule?

Please describe your experience navigating the app pages.
Probe: basic structure of the app

After your first app use, how easy/difficult was it for you in follow-up uses?

What are your thoughts about the design of the VIP-HANA app?
Probe: main logo/avatars; font/color; progress bar; Remember Me (save ID/password); Log-In Help; Continue/Skip button

Please describe your experience with any technical issues.
Probe: Log-In, crash, Back button, Continue/Skip button, How Our App Works, reminder/email, error message

(Intervention group only)

What are your thoughts about the videos displayed in the VIP-HANA app?
Probe: watching videos vs reading the content; videos with sounds, the type of sounds; effect of data plan on watching the videos

What was your experience using the app to review your symptoms and self-care strategies (=Your History)?

Impact of VIP-HANA

How did this app help you gain information about relieving your symptoms?

How do you think that your symptoms changed after using the VIP-HANA app for 6 months?

How confident are you in your ability to self-manage your symptoms?

What are some of the ways that your health might benefit through the use of VIP-HANA?
Probe: changes of current personal, professional, or health care provider relationships

Please describe how the app did/can change your quality of life.

Textbox 1. Focus group guide based on 6-month experience of using the video information provider for HIV-associated nonAIDS conditions (VIP-HANA) app.

Data Analysis

‎
Figure 1. Technology acceptance model (TAM).
View this figure

Audio recordings from the focus groups were transcribed. Thematic analysis was used to explore significant themes with similar patterns across focus group sessions. Research team members (HC, TP, and GF) independently reviewed the transcripts at least twice, and two research team members (TP and GF) generated a set of codes through a combination of inductive and deductive content analysis [-]. A codebook was developed using a Microsoft Excel spreadsheet, and free text excerpted from the transcripts was entered into the codebook followed by each of the themes. Coding discordance between TP and GF was reconciled by a third researcher (HC). Guided by the TAM framework () [], each theme in the codebook was categorized into the two constructs of perceived usefulness and perceived ease of use.

Sample

A total of 39 people who completed the 6-month trial participated in the five focus group sessions. The mean age of the study participants was 55 years (SD 10.70). Additional characteristics of the study sample are presented in .

Table 1. Characteristics of the study participants (N=39).

Characteristic

All participants (N=39)

Intervention (N=20)

Control (N=19)

Hispanic/Latino ethnicity, n (%)

13 (33)

6 (30)

7 (37)

Other (eg, ADAP a, Veteran's Association, multiple)

6 (15)

3 (15)

3 (16)

a ADAP: AIDS drug assistance program.

Main Themes

A total of eight themes related to use of the VIP-HANA app were identified. The themes were organized by the two key constructs of the TAM framework [], including five themes related to perceived usefulness and three themes related to perceived ease of use. Two themes to enhance the acceptance of the app were additionally identified.

Perceived Usefulness

The five themes related to perceived usefulness were: (1) self-monitoring HIV-related symptoms of HANA conditions, (2) enhanced relationship with clinical providers, (3) improvement in physical and emotional health, (4) long-term impact of self-care strategies on improvement in symptoms of HANA conditions, and (5) inspired lifestyle changes to manage symptoms.

Self-Monitoring HIV-Related Symptoms of HANA Conditions

Participants in both the intervention and control groups felt that the app was helpful for improving self-monitoring of their symptoms. A control group participant expressed that the app helped them realize the frequency of certain symptoms they were having, by stating:

I found that it really kept me in touch with my symptoms. And a lot of things were happening that I really didn't realize until I started using the app.
[FG2]

An intervention group participant mentioned:

The app shows you certain things that you can have, and what you can do. And you work with that. And it's been great for me. I was getting more confident with tracking and managing my symptoms.
[FG3]

Moreover, intervention group participants described enjoyment in tracking their HANA conditions:

So for those of us that do have secondary illnesses such as diabetes, asthma, high blood pressure, and so forth and so on, the app was a great thing, and not just helping us navigate the system and our bodies as far as HIV is concerned, but helped us in those other issues as well.
[FG1]

And she said the app is wonderful... for diabetes, hepatitis C, and for the different harm reduction. We know what symptoms I have.
[FG3]

Enhanced Relationship With Clinical Providers

Participants in both the intervention and control groups stated that they showed the app to their doctors/clinical therapists to guide their conversations and share their symptom reports, which empowered them to better communicate with their clinical providers and enrich their relationship. An intervention group participant described:

The changes that I made were, the app was very useful in that, in conjunction with my doctor. Because I also showed this to my doctor... And they all agreed that it's a wonderful thing.
[FG1]

A control group participant stated:

I liked the app because it was times I wanted to ask my doctor questions, but I didn't. But by doing this here app, it made me take a pen and paper and write down what I want to say to her next time I see her.
[FG2]

Improvement in Physical and Emotional Health

Participants in the intervention group noted improvements in their health as a result of app use. Participants commented that the app helped them work through their emotional issues. An intervention group participant stated:

It is not just HIV health. It is diabetes, asthma, high blood pressure... and so on... Not just your physical and your medications and stuff like that. But things that you can do to make yourself happier, better, more social.
[FG1]

Long-Term Impact of Self-Care Strategies on Improvement in Symptoms of HANA Conditions

Intervention group participants stated that they continued to use the self-care strategies for their symptoms even after the study was over. They considered that the self-care strategies resulted in a lasting improvement of their symptoms and therefore they continued to use them. An intervention group participant mentioned:

When I go through stress, I get those migraine headaches. In the app, it tells you to relax, turn off the TV. Turn off everything and just be calm in a dark room. And that still helps me. I am still doing that.
[FG1]

Inspired Lifestyle Changes to Manage Symptoms

Participants in the control group noted that the symptom questions on the VIP-HANA app helped them to think about their recent symptoms and inspired lifestyle changes. For example, some control group participants stated:

I remember one time, I was really depressed that week. And I put down real severe depression. And it made me think... So I'm trying to figure out why am I depressed? What made me depressed this week? What should I do? What changes?
[FG2]

But when you're looking at, when you're pressing this button several times every, several times a month, with the same thing, it reminds you. You need to really get serious about what you've been thinking about all this time.
[FG2]

Perceived Ease of Use

The three major themes related to perceived ease of use were: (1) easy to navigate, (2) avatar personalization, and (3) privacy/confidentiality maintained even when changing the location of app use.

Easy to Navigate

Participants in both the intervention and control groups found that the VIP-HANA app was easy to navigate, and they were able to use the app without assistance and did not experience technical issues. An intervention group participant stated:

Nothing hard at all. I am not very good to the tech... but this app, easy to get to start... easy to go to the next... very easy to move forward.
[FG3]

A control group participant described:

Yeah. The app I thought was pretty easy to navigate through. And the questions were basically always the same. I didn't need any help although I am not good at technology.
[FG2]

Avatar Personalization

Participants in the intervention and control groups noted the usefulness of an avatar to guide them through the VIP-HANA app and help them navigate through the app. They also noted that the avatar promoted their engagement with the app. Some participants in the intervention group mentioned:

I feel like I connected to it more by choosing the one that best looked like me or was fitted to me.
[FG1]

When you see it in the app, where you're actually seeing the cartoon character [avatar], get up and going. It encourages you that you can get up and go. I think it was very practical.
[FG5]

Some participants in the control group stated:

I think when you look at it [avatar], it's more personal. It's about you. And I think that it's good for your psyche to realize that you've got to really take care of yourself.
[FG2]

The app I thought was pretty easy to navigate through because of the avatar. The avatar looks like me... myself!
[FG4]

I was going to mention that the whole thing is laid out pretty attractively. The little avatar made it, to me, it made it almost like a game.
[FG2]

Privacy/Confidentiality Maintained Even When Changing Location of App Use

Participants in both the intervention and control groups felt comfortable using the app in a variety of locations ranging from their home to doctor's offices and in public spaces (ie, everywhere). They thought that the app was very discrete and did not disclose their HIV status to others if they could see the app on the screen. Several participants in the intervention group described this aspect:

To me, I feel like it was enough discretion in the app that if I wanted to explain it to somebody, because I was okay pulling out anywhere... everywhere...
[FG1]

I feel that that was really good, in the sense of you can do it really anywhere.
[FG5]

Some participants in the control group stated:

One of the good things about the program, it doesn't mention anything related to HIV. The reason... If someone want to get into your phone, nobody can access personal information related to the app.
[FG2]

I was happy because I can use it anytime and anywhere; at work, on the train.
[FG4]

Additional Comments to Enhance App Acceptance

Need for Updates of Self-Care Strategies and Videos

Participants in the intervention group expressed their desire for the app to include more self-care strategies and realistic videos. Participants felt that the app questions (symptoms) and suggestions (self-care strategies) were particularly useful for people living with HIV who were newly diagnosed with HIV/AIDS. They thought that the animated videos showing the suggestions were too simple or not serious enough in some cases. Some participants in the intervention group described:

When I look at the study you have to say that it's really geared more to like an HIV 101 plan where it's just basic stuff... For a person that's newly diagnosed and don't know a lot of things about HIV, I think it's the best thing for them.
[FG1]

Some of it [animated videos] was unrealistic. If you don't have the HIV or the pain or things that we actually experience as patients, you really couldn't understand how serious sometimes it is. It felt to me like they downplayed our experiences with those videos.
[FG3]

Request for Additional Functionality of Symptom Reports

Participants in the control group wanted the app to have an additional functionality to track their symptom reports so that they do not have to remember their weekly answers to the symptom questions (eg, what symptoms they had), as the app feature of symptom reports was limited for the intervention group participants only in this study. For example, a control group participant stated:

I don't remember there being an ability to be able to say, to run a report, so to speak, and say; between January and March, I had dry eyes this many times during the month... if I can print something out or if I can open it on the phone and show it to the doctor like that, that would be a nice addition to what you already have.
[FG2]

Principal Findings

With the exponential increase of mobile technology use in health care, mHealth apps can be a promising consumer health informatics tool for health behavior change and self-management. Although existing evidence supports the use of mHealth apps to improve health outcomes in people with chronic diseases [-], there has been limited assessment of consumers' experience following their use of these apps during a clinical trial. Given the current landscape of discontinuation of mHealth apps use [], it is imperative to understand how consumers are interacting with the apps in their daily life in an effort to accelerate acceptance of the apps by the consumers as well as ensure their subsequent use. To better understand consumers' use of a self-management app, we included both intervention and control group participants following completion of an RCT that tested the efficacy of the app for improving health outcomes. We captured valuable perspectives of both study group participants on their app use using focus groups guided by a theory-driven framework. This approach facilitated gaining an in-depth understanding of the key factors that would have a critical influence on adoption of the app [], which is a significant strength of our study.

mHealth apps should be visually appealing to consumers [], but more fundamentally should contain evidence-based health information that consumers can use to improve their health. Participants considered that the app could help improve their health and manage symptoms through the use of self-care strategies. The VIP-HANA app was developed by employing evidence from patient-centered outcomes research studies [], and the HIV symptom self-care strategies provided within the app were tailored based on symptoms, gender, race/ethnicity, and HANA condition [,]. To our knowledge, no mHealth intervention offers health information on how to ameliorate HIV symptoms specifically for people living with HIV with HANA conditions; the VIP-HANA app therefore represents a first step in the development and dissemination of a consumer health informatics tool that incorporates evidence-based self-care strategies.

Limitations

There are some limitations to the generalizability of our results. Study participants who participated in the focus groups might have been those who were most likely to be engaged with and used the app who had been retained in the 6-month trial. More than half of our study participants were men, African American/Black, those who had completed some high school or less, and those who reported an annual income of less than US $20,000. Therefore, the Hawthorne effect might influence the study findings of focus groups []. We designed the semistructured focus group guide based on our app's features and functionality, and used open-ended questions to minimize/eliminate concern about the Hawthorne effect.

Conclusions

Understanding factors influencing the use of patient-centered tools is critical. This study highlights participants' experiences, attitudes, and perceptions of their use of a behavioral intervention evaluated in a clinical trial. Participants were able to integrate the app into their daily routine, and used the app to support, track, monitor, and self-manage HIV symptoms related to HANA conditions. Although perceived ease of use was similar in both study groups, perceived usefulness differed between the groups. Participants in both groups found the VIP-HANA app to be useful in monitoring their symptoms of HANA conditions and enhancing communication with their clinical providers over time, but only the intervention group participants perceived the app to be useful for improving overall health and long-term symptom management in the HIV care continuum. Findings from this study highlight factors that are essential to ensure the usefulness of self-management apps to promote sustained use of the apps for people living with chronic illnesses.

Acknowledgments

This research was supported by the National Institute of Nursing Research, National Institutes of Health under award number R01 NR015737 (principal investigator RS) and through award K24NR018621 (principal investigator RS). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of Nursing Research, National Institutes of Health. We are grateful to Dr. Suzanne Bakken for leading the focus group sessions.

Conflicts of Interest

None declared.
‎

Multimedia Appendix 1

Comparison of the VIP-HANA app features in the intervention and control group versions.
DOCX File , 459 KB

‎

HANA: HIV-associated nonAIDS

mHealth: mobile health

RCT: randomized controlled trial

TAM: technology acceptance model

VIP-HANA: video information provider for HIV-associated nonAIDS conditions

Edited by G Eysenbach; submitted 07.01.20; peer-reviewed by YC Kato-Lin, V Strotbaum; comments to author 10.03.20; revised version received 07.04.20; accepted 21.04.20; published 15.06.20

©Hwayoung Cho, Tiffany Porras, Gabriella Flynn, Rebecca Schnall. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 15.06.2020.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.

Background: Mobile health (mHealth) apps have the potential to be effective tools for encouraging patients with chronic diseases to self-manage their health. The success of mHealth apps is related to technology acceptance and its subsequent use by intended consumers. Therefore, it is essential to gain insights from consumers' perspectives about their use of mHealth apps in daily life.

Objective: The purpose of this work was to understand consumers' perspectives on use of a self-management app following completion of a clinical trial that tested the efficacy of the app for improving health outcomes.

Methods: We conducted five focus groups with paricipants of a clinical trial (NCT03182738) who were randomized to use the video information provider (VIP) for HIV-associated nonAIDS (HANA) conditions app (VIP-HANA) or an attention control app. Thematic analysis was conducted, and the themes were organized according to the two key constructs of the technology acceptance model framework: perceived usefulness and perceived ease of use.

Results: Thirty-nine people living with HIV (20 from the intervention group and 19 from the control group) participated in the focus group sessions. Of the eight themes identified from focus group data, the five themes related to perceived usefulness were: (1) self-monitoring HIV-related symptoms of HANA conditions, (2) enhanced relationship with clinical providers, (3) improvement in physical and emotional health, (4) long-term impact of self-care strategies on improvement in symptoms of HANA conditions, and (5) inspired lifestyle changes to manage symptoms. The three themes related to perceived ease of use were: (1) easy to navigate, (2) avatar personalization, and (3) privacy/confidentiality maintained even when changing the location of app use.

Conclusions: Perceived ease of use was similar in both study groups but perceived usefulness differed between study groups. Participants in both study groups found the VIP-HANA app to be useful in monitoring their symptoms and enhancing communication with their clinical care providers. However, only intervention group participants perceived the app to be useful in improving overall health and long-term symptom management. Findings from this study highlight factors that are essential to ensure the usefulness of self-management apps and facilitate sustained use of mHealth apps for people living with chronic illnesses.

J Med Internet Res 2020;22(6):e17708

doi:10.2196/17708

Keywords

With the widespread proliferation of smartphones, the use of mobile technology has rapidly increased in health care [,]. Consumer health informatics tools such as mobile health (mHealth) apps have the potential to be effective tools to encourage patients with chronic diseases to self-manage their health [-]. The video information provider (VIP) for HIV-associated nonAIDS (HANA) conditions app (VIP-HANA) is an mHealth app that was developed to help people living with HIV self-manage their symptoms by providing self-care strategies []. Development of the VIP-HANA app is described elsewhere []. In short, the VIP-HANA app is comprised of 728 self-care strategies for the following 28 HANA condition-related symptoms: fatigue, depression, muscle aches, difficulty falling asleep, anxiety, difficulty staying asleep, difficulty remembering, neuropathy, difficulty concentrating, decreased sex drive, diarrhea, maintaining an erection, shortness of breath, constipation, dry mouth, clumsiness, weight loss, dizziness, heartburn, dry eyes, changes in appetite, ringing in ear, cough, nausea/vomiting, fever, difficulty with urination, speech difficulties, and pain during sex []. The 728 self-care strategies were tailored based on the symptom, gender, race/ethnicity, and HANA condition of each user. An avatar guides users through the app. Patients then receive self-care strategies with animated videos for ameliorating their symptoms. Users review their symptom experience over time with symptom reports, set a weekly email reminder to complete sessions in the app, and email/download the history of their reported symptoms and suggested self-care strategies [].

Two versions of the app were created to assess the efficacy of the VIP-HANA app in improving the symptom burden in a 6-month randomized controlled trial (RCT) (NCT03182738) that included 100 people living with HIV with HANA conditions (50 randomly assigned to the intervention group, 50 assigned to the control group). All participants were asked to log in at least once per week. Both study groups received weekly symptom assessment questions through the VIP-HANA app, yet only the intervention group participants were provided with the self-care strategies. presents a comparison of the two versions of the VIP-HANA app features. Participants in both groups were given US $5 per week for their time and data costs associated with completing the weekly symptom assessments.

According to a recent US national survey of mHealth app use, approximately half (46%) of the respondents reported downloading a health app but were no longer using it or had uninstalled it []. Given that the success of mHealth apps is related to the acceptance of technology and its subsequent use by intended consumers, there is a need to gain in-depth insights from consumers' perspectives about use of mHealth apps in their daily life. Accordingly, the purpose of this study was to understand consumers' perspectives on mHealth use (VIP-HANA app) following an RCT of an HIV symptom self-management intervention for people living with HANA conditions.

Sampling and Recruitment

Procedures

The Institutional Review Board of Columbia University Medical Center reviewed and approved all research activities. We conducted three focus group sessions with intervention group participants (N=20) and two focus groups sessions with control group participants (N=19). Prior to the focus group sessions, all participants were provided an explanation of the study procedures and completed a signed consent form. Using open-ended questions via a semistructured focus group guide designed based on the app's features/functionality (), a moderator (a faculty member at Columbia University School of Nursing) facilitated the focus group sessions, and participants were encouraged to discuss issues regarding their perceptions related to use of the VIP-HANA app (intervention/control group versions). All focus group sessions were audio-recorded. Data collection continued until saturation of themes was reached. Participants were compensated with US $30 for their time.

Focus group guide based on 6-month experience of using the video information provider for HIV-associated nonAIDS conditions (VIP-HANA) app.
Experience Using VIP-HANA

Please describe your experience using the VIP-HANA app in social settings.
Probe: where and when did you use it most (house/workplace/clinic/café; after breakfast/when commuting, etc) and why?

How did this fit into your lifestyle and schedule?

Please describe your experience navigating the app pages.
Probe: basic structure of the app

After your first app use, how easy/difficult was it for you in follow-up uses?

What are your thoughts about the design of the VIP-HANA app?
Probe: main logo/avatars; font/color; progress bar; Remember Me (save ID/password); Log-In Help; Continue/Skip button

Please describe your experience with any technical issues.
Probe: Log-In, crash, Back button, Continue/Skip button, How Our App Works, reminder/email, error message

(Intervention group only)

What are your thoughts about the videos displayed in the VIP-HANA app?
Probe: watching videos vs reading the content; videos with sounds, the type of sounds; effect of data plan on watching the videos

What was your experience using the app to review your symptoms and self-care strategies (=Your History)?

Impact of VIP-HANA

How did this app help you gain information about relieving your symptoms?

How do you think that your symptoms changed after using the VIP-HANA app for 6 months?

How confident are you in your ability to self-manage your symptoms?

What are some of the ways that your health might benefit through the use of VIP-HANA?
Probe: changes of current personal, professional, or health care provider relationships

Please describe how the app did/can change your quality of life.

Textbox 1. Focus group guide based on 6-month experience of using the video information provider for HIV-associated nonAIDS conditions (VIP-HANA) app.

Data Analysis

‎
Figure 1. Technology acceptance model (TAM).
View this figure

Audio recordings from the focus groups were transcribed. Thematic analysis was used to explore significant themes with similar patterns across focus group sessions. Research team members (HC, TP, and GF) independently reviewed the transcripts at least twice, and two research team members (TP and GF) generated a set of codes through a combination of inductive and deductive content analysis [-]. A codebook was developed using a Microsoft Excel spreadsheet, and free text excerpted from the transcripts was entered into the codebook followed by each of the themes. Coding discordance between TP and GF was reconciled by a third researcher (HC). Guided by the TAM framework () [], each theme in the codebook was categorized into the two constructs of perceived usefulness and perceived ease of use.

Sample

A total of 39 people who completed the 6-month trial participated in the five focus group sessions. The mean age of the study participants was 55 years (SD 10.70). Additional characteristics of the study sample are presented in .

Table 1. Characteristics of the study participants (N=39).

Characteristic

All participants (N=39)

Intervention (N=20)

Control (N=19)

Hispanic/Latino ethnicity, n (%)

13 (33)

6 (30)

7 (37)

Other (eg, ADAP a, Veteran's Association, multiple)

6 (15)

3 (15)

3 (16)

a ADAP: AIDS drug assistance program.

Main Themes

A total of eight themes related to use of the VIP-HANA app were identified. The themes were organized by the two key constructs of the TAM framework [], including five themes related to perceived usefulness and three themes related to perceived ease of use. Two themes to enhance the acceptance of the app were additionally identified.

Perceived Usefulness

The five themes related to perceived usefulness were: (1) self-monitoring HIV-related symptoms of HANA conditions, (2) enhanced relationship with clinical providers, (3) improvement in physical and emotional health, (4) long-term impact of self-care strategies on improvement in symptoms of HANA conditions, and (5) inspired lifestyle changes to manage symptoms.

Self-Monitoring HIV-Related Symptoms of HANA Conditions

Participants in both the intervention and control groups felt that the app was helpful for improving self-monitoring of their symptoms. A control group participant expressed that the app helped them realize the frequency of certain symptoms they were having, by stating:

I found that it really kept me in touch with my symptoms. And a lot of things were happening that I really didn't realize until I started using the app.
[FG2]

An intervention group participant mentioned:

The app shows you certain things that you can have, and what you can do. And you work with that. And it's been great for me. I was getting more confident with tracking and managing my symptoms.
[FG3]

Moreover, intervention group participants described enjoyment in tracking their HANA conditions:

So for those of us that do have secondary illnesses such as diabetes, asthma, high blood pressure, and so forth and so on, the app was a great thing, and not just helping us navigate the system and our bodies as far as HIV is concerned, but helped us in those other issues as well.
[FG1]

And she said the app is wonderful... for diabetes, hepatitis C, and for the different harm reduction. We know what symptoms I have.
[FG3]

Enhanced Relationship With Clinical Providers

Participants in both the intervention and control groups stated that they showed the app to their doctors/clinical therapists to guide their conversations and share their symptom reports, which empowered them to better communicate with their clinical providers and enrich their relationship. An intervention group participant described:

The changes that I made were, the app was very useful in that, in conjunction with my doctor. Because I also showed this to my doctor... And they all agreed that it's a wonderful thing.
[FG1]

A control group participant stated:

I liked the app because it was times I wanted to ask my doctor questions, but I didn't. But by doing this here app, it made me take a pen and paper and write down what I want to say to her next time I see her.
[FG2]

Improvement in Physical and Emotional Health

Participants in the intervention group noted improvements in their health as a result of app use. Participants commented that the app helped them work through their emotional issues. An intervention group participant stated:

It is not just HIV health. It is diabetes, asthma, high blood pressure... and so on... Not just your physical and your medications and stuff like that. But things that you can do to make yourself happier, better, more social.
[FG1]

Long-Term Impact of Self-Care Strategies on Improvement in Symptoms of HANA Conditions

Intervention group participants stated that they continued to use the self-care strategies for their symptoms even after the study was over. They considered that the self-care strategies resulted in a lasting improvement of their symptoms and therefore they continued to use them. An intervention group participant mentioned:

When I go through stress, I get those migraine headaches. In the app, it tells you to relax, turn off the TV. Turn off everything and just be calm in a dark room. And that still helps me. I am still doing that.
[FG1]

Inspired Lifestyle Changes to Manage Symptoms

Participants in the control group noted that the symptom questions on the VIP-HANA app helped them to think about their recent symptoms and inspired lifestyle changes. For example, some control group participants stated:

I remember one time, I was really depressed that week. And I put down real severe depression. And it made me think... So I'm trying to figure out why am I depressed? What made me depressed this week? What should I do? What changes?
[FG2]

But when you're looking at, when you're pressing this button several times every, several times a month, with the same thing, it reminds you. You need to really get serious about what you've been thinking about all this time.
[FG2]

Perceived Ease of Use

The three major themes related to perceived ease of use were: (1) easy to navigate, (2) avatar personalization, and (3) privacy/confidentiality maintained even when changing the location of app use.

Easy to Navigate

Participants in both the intervention and control groups found that the VIP-HANA app was easy to navigate, and they were able to use the app without assistance and did not experience technical issues. An intervention group participant stated:

Nothing hard at all. I am not very good to the tech... but this app, easy to get to start... easy to go to the next... very easy to move forward.
[FG3]

A control group participant described:

Yeah. The app I thought was pretty easy to navigate through. And the questions were basically always the same. I didn't need any help although I am not good at technology.
[FG2]

Avatar Personalization

Participants in the intervention and control groups noted the usefulness of an avatar to guide them through the VIP-HANA app and help them navigate through the app. They also noted that the avatar promoted their engagement with the app. Some participants in the intervention group mentioned:

I feel like I connected to it more by choosing the one that best looked like me or was fitted to me.
[FG1]

When you see it in the app, where you're actually seeing the cartoon character [avatar], get up and going. It encourages you that you can get up and go. I think it was very practical.
[FG5]

Some participants in the control group stated:

I think when you look at it [avatar], it's more personal. It's about you. And I think that it's good for your psyche to realize that you've got to really take care of yourself.
[FG2]

The app I thought was pretty easy to navigate through because of the avatar. The avatar looks like me... myself!
[FG4]

I was going to mention that the whole thing is laid out pretty attractively. The little avatar made it, to me, it made it almost like a game.
[FG2]

Privacy/Confidentiality Maintained Even When Changing Location of App Use

Participants in both the intervention and control groups felt comfortable using the app in a variety of locations ranging from their home to doctor's offices and in public spaces (ie, everywhere). They thought that the app was very discrete and did not disclose their HIV status to others if they could see the app on the screen. Several participants in the intervention group described this aspect:

To me, I feel like it was enough discretion in the app that if I wanted to explain it to somebody, because I was okay pulling out anywhere... everywhere...
[FG1]

I feel that that was really good, in the sense of you can do it really anywhere.
[FG5]

Some participants in the control group stated:

One of the good things about the program, it doesn't mention anything related to HIV. The reason... If someone want to get into your phone, nobody can access personal information related to the app.
[FG2]

I was happy because I can use it anytime and anywhere; at work, on the train.
[FG4]

Additional Comments to Enhance App Acceptance

Need for Updates of Self-Care Strategies and Videos

Participants in the intervention group expressed their desire for the app to include more self-care strategies and realistic videos. Participants felt that the app questions (symptoms) and suggestions (self-care strategies) were particularly useful for people living with HIV who were newly diagnosed with HIV/AIDS. They thought that the animated videos showing the suggestions were too simple or not serious enough in some cases. Some participants in the intervention group described:

When I look at the study you have to say that it's really geared more to like an HIV 101 plan where it's just basic stuff... For a person that's newly diagnosed and don't know a lot of things about HIV, I think it's the best thing for them.
[FG1]

Some of it [animated videos] was unrealistic. If you don't have the HIV or the pain or things that we actually experience as patients, you really couldn't understand how serious sometimes it is. It felt to me like they downplayed our experiences with those videos.
[FG3]

Request for Additional Functionality of Symptom Reports

Participants in the control group wanted the app to have an additional functionality to track their symptom reports so that they do not have to remember their weekly answers to the symptom questions (eg, what symptoms they had), as the app feature of symptom reports was limited for the intervention group participants only in this study. For example, a control group participant stated:

I don't remember there being an ability to be able to say, to run a report, so to speak, and say; between January and March, I had dry eyes this many times during the month... if I can print something out or if I can open it on the phone and show it to the doctor like that, that would be a nice addition to what you already have.
[FG2]

Principal Findings

With the exponential increase of mobile technology use in health care, mHealth apps can be a promising consumer health informatics tool for health behavior change and self-management. Although existing evidence supports the use of mHealth apps to improve health outcomes in people with chronic diseases [-], there has been limited assessment of consumers' experience following their use of these apps during a clinical trial. Given the current landscape of discontinuation of mHealth apps use [], it is imperative to understand how consumers are interacting with the apps in their daily life in an effort to accelerate acceptance of the apps by the consumers as well as ensure their subsequent use. To better understand consumers' use of a self-management app, we included both intervention and control group participants following completion of an RCT that tested the efficacy of the app for improving health outcomes. We captured valuable perspectives of both study group participants on their app use using focus groups guided by a theory-driven framework. This approach facilitated gaining an in-depth understanding of the key factors that would have a critical influence on adoption of the app [], which is a significant strength of our study.

mHealth apps should be visually appealing to consumers [], but more fundamentally should contain evidence-based health information that consumers can use to improve their health. Participants considered that the app could help improve their health and manage symptoms through the use of self-care strategies. The VIP-HANA app was developed by employing evidence from patient-centered outcomes research studies [], and the HIV symptom self-care strategies provided within the app were tailored based on symptoms, gender, race/ethnicity, and HANA condition [,]. To our knowledge, no mHealth intervention offers health information on how to ameliorate HIV symptoms specifically for people living with HIV with HANA conditions; the VIP-HANA app therefore represents a first step in the development and dissemination of a consumer health informatics tool that incorporates evidence-based self-care strategies.

Limitations

There are some limitations to the generalizability of our results. Study participants who participated in the focus groups might have been those who were most likely to be engaged with and used the app who had been retained in the 6-month trial. More than half of our study participants were men, African American/Black, those who had completed some high school or less, and those who reported an annual income of less than US $20,000. Therefore, the Hawthorne effect might influence the study findings of focus groups []. We designed the semistructured focus group guide based on our app's features and functionality, and used open-ended questions to minimize/eliminate concern about the Hawthorne effect.

Conclusions

Understanding factors influencing the use of patient-centered tools is critical. This study highlights participants' experiences, attitudes, and perceptions of their use of a behavioral intervention evaluated in a clinical trial. Participants were able to integrate the app into their daily routine, and used the app to support, track, monitor, and self-manage HIV symptoms related to HANA conditions. Although perceived ease of use was similar in both study groups, perceived usefulness differed between the groups. Participants in both groups found the VIP-HANA app to be useful in monitoring their symptoms of HANA conditions and enhancing communication with their clinical providers over time, but only the intervention group participants perceived the app to be useful for improving overall health and long-term symptom management in the HIV care continuum. Findings from this study highlight factors that are essential to ensure the usefulness of self-management apps to promote sustained use of the apps for people living with chronic illnesses.

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Usability of a Consumer Health Informatics Tool Following Completion of a Clinical Trial: Focus Group Study

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Background: Mobile health (mHealth) apps have the potential to be effective tools for encouraging patients with chronic diseases to self-manage their health. The success of mHealth apps is related to technology acceptance and its subsequent use by intended consumers. Therefore, it is essential to gain insights from consumers' perspectives about their use of mHealth apps in daily life.

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Improved Diagnostics & Patient Outcomes | HealthIT.gov

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When health care providers have access to complete and accurate information, patients receive better medical care. Electronic health records (EHRs) can improve the ability to diagnose diseases and reduce—even prevent—medical errors, improving patient outcomes.

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Data Analytics is the process of examining raw datasets to find trends, draw conclusions and identify the potential for improvement. Health care analytics uses current and historical data to gain insights, macro and micro, and support decision-making at both the patient and business level.

The use of health data analytics allows for improvements to patient care, faster and more accurate diagnoses, preventive measures, more personalized treatment and more informed decision-making. At the business level, it can lower costs, simplify internal operations and more.

What is health care data?

In order to discuss health data analytics and the role it plays in the health care sector, we must first understand the data that is being collected and analyzed. There is data being collected on the processes and procedures of the business side of health care, but there is also an enormous amount of health data being gathered, stored and analyzed.

Health data is any data relating to the health of an individual patient or collective population. This information is gathered from a series of health information systems (HIS) and other technological tools utilized by health care professionals, insurance companies and government organizations.

We are able to see a holistic view of each individual patient as well as trends tied to location, socioeconomic status, race and predisposition. The information being collected can be broken down into specific datasets that can then be analyzed.

There are a variety of tools and systems used to collect, store, share and analyze health data gathered through various means. These tools include:

Electronic Health Records (EHRs)

Personal Health Records (PHRs)

Electronic Prescription Services (E-prescribing)

Patient Portals

Master Patient Indexes (MPI)

Health-Related Smart Phone Apps and more

With digital data collection, there is more and more health care data to be analyzed every second. With the increase of electronic record keeping, applications and other electronic means of data collection and storage, there is a significant amount of data being collected in real time.

These data sets are so complex that traditional processing software and storage options cannot be used. Cloud storage is a necessity when dealing with "Big Data." Cloud storage is built to be secure, an absolute must when dealing with sensitive patient information. It is also very cost-efficient and has been helpful in lowering the increasing cost of health care.

Big Data Analytics and COVID-19

The impact COVID-19 has had on the health care industry is evident to anyone and everyone. You don't need to be plugged into the world of medicine to see what has been happening worldwide during this pandemic.

What most people don't see, though, is the impact COVID-19 has had on health care data analytics. "Big data tools have played an increasingly significant role in health care decision-making" says HealthITAnalytics. It is not just providers, but lawmakers and researchers who are turning to big data analytics and predictive models to help allocate resources, predict surges, improve patient care and outcomes and employ preventive measures.

Big data and health data analytics have played an integral role in the fight against COVID-19. The data is coming in at a near constant rate. Analyzing that health data has allowed for a better understanding of how to respond and treat patients.

This pandemic has resulted in an enormous surge of health data being recorded and manipulated allowing for bigger and better analytics. Unfortunately, we are also seeing that COVID-19 is " shining a harsh spotlight on health care's biggest issues. " There are a lot of obstacles when it comes to sharing health data across organizations and a distinct lack of standardization in the way that data is collected and analyzed.

This widespread problem was evident in the early days of the pandemic as conflicting and ever-changing information was being presented to the public. We saw a turn towards disbelief when it came to COVID-related information with many still believing misinformation and previously held beliefs on how this virus should be handled.

The spotlight that COVID-19 shined on these problems, however, will allow for them to be rectified. The providers, researchers and policymakers can learn from these mistakes and work towards a better, more standardized solution for big data in health care.

The Importance of Health Care Data Analytics

We can collect all the data we want, but it doesn't do any good if we don't know what to do with that information. We need a centralized, systematic way of collecting, storing and analyzing data so we can use it to our advantage.

The collection of data in health care settings has become more streamlined in recent years. Not only does the data help improve day-to-day operations and better patient care, it can now be better used in predictive modeling. Instead of just looking at historical information or current information, we can use both datasets to track trends and make predictions. We are now able to take preventive measures and track the outcomes.

The fee-for-service style of health care is becoming a thing of the past. There is a growing demand for patient-centric, or value-based, medical care which has led to a considerable shift towards predictive and preventive measures in regards to public health in recent years. Data makes this possible. Instead of simply treating the symptoms as they present, practitioners are able to identify patients at high risk of developing chronic illnesses and help to treat an issue before it surfaces. This helps to lower costs for the practitioner, insurance company and patient as the preventive treatment may help to stave off long-term issues and expensive hospitalizations.

If hospitalization is necessary, data analytics can help practitioners predict risks of infection, deterioration and readmission. This too can help lower costs and improve patient care outcomes.

Consider the impact this has had on the COVID-19 pandemic. The data being collected is analyzed in real time to understand the effects of the virus better and predict future trends so we may slow the spread and prevent future outbreaks.

Types of Health Care Analytics

Not every question can be answered by using the same analysis of the data. Through the use of different types of big data analytics, we can answer many of the questions being asked in health care settings.

Descriptive Analytics

Descriptive analytics uses historical data to draw comparisons or discover patterns. This type of analysis is best for answering questions about what has already occurred. We can gain insight into the past with descriptive analytics.

Predictive Analytics

Predictive analytics uses current and historical data to make predictions about the future. The models created with this type of analytics are best for answering questions about what could happen next. We can gain insight into the future with predictive analytics.

Prescriptive Analytics

Prescriptive analytics will also make predictions about future outcomes. Machine learning is a big factor with this type of analytics. The information provided can help determine the best course of action. We can gain insight on what course of action should be taken to reach the most ideal outcome with prescriptive analytics.

How Data Analytics Leads to Health Care Solutions

Health care data management has the potential to lead to better care if used properly. With centralized datasets, there is immediate access to necessary information whenever and wherever it is needed. The addition of big data analytics improves efficiency on all fronts. Better data leads to better care.

Predictive Modeling

Predictive modeling is the process of analyzing current and historical data to predict future outcomes. Models use data mining, machine learning and statistics to identify patterns and predict outcomes. Predictive models built off of the health data being collected provide solutions on the macro and micro level.

The use of predictive analytics can alert health care professionals to potential risks. By analyzing behavioral data, we can predict treatment outcomes, potential risks for chronic illness and even predict risk of self-harm. The health data collected can be used for risk scoring, readmission prediction and prevention, predicting infection and deterioration and so much more at the individual patient level.

Predictive modeling can also be used on a much larger scale. Population health management is impossible without the use of these models. Outbreaks can be predicted, outcomes can be predicted, and in knowing what is to come, preventive measures can be taken.

Predictive modeling can even be used in administrative applications to increase efficiency and lower costs for all.

Reductions in Health Care Costs

Health care is expensive. And, those costs only continue to increase across the board. We are, however, seeing a shift from fee-for-service payment models to value-based care.

Through the use of predictive and prescriptive analytics, health care organizations and practitioners can get detailed models for lowering costs and patient risk. In addition to the patient-centric benefits mentioned above, health data analytics can reduce appointment no-shows, manage supply chain costs, prevent equipment breakdowns and decrease fraud.

The Growing Role of the Health Data Analyst

Health Data Analysts take the health care data being collected and use skills such as data acquisition, data management, data analysis and data interpretation to provide actionable insights. The role of big data in health care and the increasing need for improvement in the health care sector has led to a higher demand for qualified health data analysts.

The role of a health data analyst varies based on their position and industry of choice. Regardless of industry, a health data analyst will need to be able to work with, develop and evaluate health information technology (health IT) and other health information systems (HIS).

They may also be expected to:

Collect or mine data

Examine current and historical data

Evaluate raw data

Build predictive models

Automate reports

Required Skills for Health Data Analysts

Health data analysts solve problems for the organizations that hire them. In order to do so, they should be well versed in several essential skills:

Structured Query Language (SQL)

Excel

Statistical Programming

Data Visualization

You should also have certain soft skills as a health data analyst such as:

Analytical thinking

Creative thinking

Attention to detail

Strong communication skills

Schooling for Health Data Analysts

While it is not a requirement to become a health data analyst, many jobs will require a bachelor's degree in health information management (HIM) or a related field (data science, mathematics, health informatics, statistics, etc.).

A master's degree is, again, not a requirement to work as a health care data analyst, but it will qualify you for higher, more competitive positions. You can earn your master's in data analytics, health informatics, or another related field.

Where Will A Health Data Analyst Be Employed?

Numerous industries and organizations utilize health data and require the help of health data analysts. These include:

Hospitals, private or public

Government health care departments

Diagnostic centers

Health insurance companies

Health care consulting companies

Large medical practices

Health IT vendors

Other health organizations

Depending on the industry or role they choose to take, health care data analysts may work as part of larger teams or alone.

How Much Do Health Data Analysts Make?

Your salary, much like your role responsibilities, will vary based on the industry or organization for which you choose to work. Payscale lists the median salary for a health data analyst as $63,000 per year. The U.S. Bureau of Labor Statistics doesn't have an entry specific to Health Data Analysts, however, the Medical and Health Services Manager page does list "health information manager." The median pay listed here is closer to $100,000.

Take the Next Step in Your Career

The University of Pittsburgh School of Health and Rehabilitation Sciences offers a Master of Science in Health Informatics degree with a Data Science track. If you want to enter the field of health informatics or health data analytics or open up your career options, this master's degree can make you a competitive candidate.

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The Role of Data Analytics in Health Care

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Data Analytics is the process of examining raw datasets to find trends, draw conclusions and identify the potential for improvement. Health care analytics uses current and historical data to gain insights, macro and micro, and support decision-making at both the patient and business level.

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Abstract

Infographics are graphic visual representations of educational content, used to deliver complex information, disseminate scientific research, and drive behavioral change. Herein, we review some of the factors pertinent to designing infographics and the potential for automation in the future. To guide high-impact design, it is vital to clearly define the objectives of the infographic and its target audience. Designing an effective infographic necessitates careful consideration of the layout, colors, font, and context. More recently, technical support to develop infographics are increasingly available through online software (Canva, Adobe, and Venngage) and emerging artificial intelligence programs. References can also become a visual representation of trends in scientific discovery. It is crucial for clinicians, researchers and scientists to have the knowledge and skills to design compelling infographics. In the era of social media, the uptake and effects of infographics for disseminating scientific research and public health education need to be further studied to understand their full potential.

Keywords: Infographics, Social Media, Research, Design, Technology

INTRODUCTION

Conveying information via written text or numbers alone can be uninteresting, arduous, and have little to no impact. However, combining these with visual aids, such as images or figures, in a well-curated, aesthetically appealing format allows a message to be expressed compellingly, with greater comprehensibility. This is called an information graphic or infographic, a device to present complex informational content in a powerful visual format which supports optimal cognitive processing, learning, retention, future recollection, and subsequently drives behavioral change. Infographics have emerged as a popular digital tool to share dense or abstract messages to diverse audiences in an efficient, precise, and influential manner. Visual abstracts are a form of infographic-an image summary of research, which lend themselves to ease of dissemination, usually through social media. 1, 2

Due to their immense potential, it is crucial for researchers, clinicians and scientists to have a firm grasp on designing infographics for instructional and educational purposes, evaluate their effectiveness, and be aware of common pitfalls to avoid. In this opinion piece, we explore some of the factors that need to be considered in designing high-impact infographics to disseminate scientific educational content, and the potential role of automation in the future.

IDENTIFYING THE INFOGRAPHIC'S OBJECTIVES

Infographics can serve many different objectives. Firstly, they can provide an eagle-eye view of the latest research, show insights and emerging discussions, and improve clinical practice. 3, 4 In the era of social media, disseminating such information via an infographic on these channels may achieve more traction and engagement than journal sites. Furthermore, clinical trials have explored infographics and social media image campaigns for patient recruitment for clinical trials. Researchers could tailor their message to target specific patient groups, improving the diversity of their clinical trial participants. 4, 5, 6

As a teaching tool, infographics enhance engagement, memory retention, recall, comprehension, and development of communication skills. 7, 8 In the current climate of the coronavirus pandemic, infographics have been increasingly employed for patient education. For example, infographics were used to promote the goal of "flattening the curve" by encouraging hygienic practices such as handwashing and appropriate mask-wearing and providing information on local public health guidelines. 9, 10, 11 These have also been used to demonstrate the pathology of coronavirus and the immune response to infection. 10, 12 Infographics can help improve health literacy by combatting misinformation and encouraging patients to verify false news. 13, 14 For public health purposes, infographics should be tailored according to the target audience's current health literacy and preferred language. 15, 16

COMMUNICATING SCIENCE THROUGH ART

Communication is central to Medicine, but challenges exist in conveying scientific information via non-traditional, artistic means. Firstly, images must be chosen wisely as they can affect emotion and comprehensibility. Those that are too abstract or unfamiliar will not facilitate affinity with the selected audience. Images that some may find disturbing or inappropriate, 17 such as photos of traumatic injury or intraoperative cases, must be avoided. Care must also be taken when using images due to copyright. Many online photos are free or under the public domain as a Creative Commons license, 18 which helps creators to maintain copyright but allows non-commercial use of their artwork. Static infographic images can be fortified with animations or even interactive content, and visual abstracts can be converted to video abstracts ( Fig. 1 ). 19, 20

While online palette databases exist, Stones and Gent 21 do not recommend arbitrary selection of colors. Colors can convey subliminal messaging, such as red for emergency, urgency, or denoting an adverse outcome. Colors also affect the legibility of the accompanying text or font. As reading behavior is affected by alignment, colors, and dominant elements of the infographic, key messages should be clear, coherent, and bold ( Fig. 2 ).

Where designing infographics that are creative, effective, and aesthetic presents a challenge; Khoury et al. 22 suggest establishing a partnership with graphic designers. Collaboration with expert graphic designers can lead to the development of a high-quality infographic, which can capture the attention of a lay audience. However, it is important to consider the time commitment and additional funding required to obtain the support of a graphic designer. With growing clinicians' and scientists' workloads and times of austerity, this may be challenging to facilitate.

HARNESSING MAN AND MACHINE

There are numerous tools for anyone to start creating infographics. Canva ( www.canva.com/features/), for example, is one online tool for design and publishing which offers a free trial and more advanced creative services at a fee. 23 It has a range of templates and a database of images and illustrations, text font, audio, and videos. Copyrighted elements can be ethically unlocked with a paid subscription. Platforms also allow users to form teams, share design access and create infographics simultaneously. The final product can be synchronized with social media accounts and published afterward. Venngage ( www.venngage.com/features) and Adobe Creative Cloud ( www.adobe.com/express/features) are other examples of online platforms with similar features to create infographics. 24, 25 Many of these sites also provide educational courses on improving the quality of infographics to promote engagement.

However, despite the availability of such platforms, a less technologically proficient user can find these challenging to utilize. With this in mind, artificial Intelligence (AI) has been gaining popularity, wherein a computer is trained to recognize elements of previous infographics, and subsequently create new ones by integrating these. AI can be seen in Microsoft Powerpoint's Design Ideas features: it can suggest icon images and layouts based on the words inputted to the slide. 26 In the Text-to-Viz program by Cui et al., 27 they use natural language processing by analyzing word definitions and context, subsequently converting them into graphic suggestions. For example, where texts refer to being environmentally friendly, green or blue colors are suggested. Key facts can be given emphasis by using a highlight, increased size, or bold font. For raw data tables and statistics, Google Visualization Features 28 can convert spreadsheets into various chart types for users to select from. DataShot 29 is another system that assigns table elements a score of importance (e.g., context, significance), and then proposes layouts depending on the score.

KEEPING INFOGRAPHICS EVIDENCE-BASED

An infographic is not complete without references, as these add validity and credibility for both a lay and medical audience. In Saudi Arabia, Jahan et al. 30 assessed 297 infographics on chronic health conditions for patients created by healthcare institutions, and none had references. References provide clinicians with the option to acquire more detailed information, before making changes to clinical practice. 31 In the future, the credibility of infographics could be improved further by compiling them into a searchable platform that can be vetted by physicians and researchers. 32, 33

Infographics can also be a reference for bibliography analytics. With the growing numbers of articles published daily, it is becoming progressively more challenging to keep up with the output. Research papers can be compared and arranged in a visual network following their citation, contradicting, and supporting concepts. 34 Such aids are a feature of Connected Papers (connectedpapers.com) and in software coding tools such as VosViewer ( www.vosviewer.com). 35, 36 Open Knowledge Map (openknowledgemaps.org) is another bibliography visualization tool that provides image clusters of similar papers. 37

FACILITATING FEEDBACK

An infographic can be 'tested' on a small group of people for initial feedback and evaluation. Dependent on the target audience, infographics could be shared more broadly through social media. 38, 39 Subsequent feedback can be collated and incorporated within future iterations of the infographic. Where new information comes to light surrounding the topic, the infographic may need revision and modification. It is essential to assess and review the target audience's response and interaction with the infographic, to facilitate optimal communication and education. 39

CONCLUSION

Infographics are rapidly gaining popularity as a mode of dissemination of information for science. It is crucial for researchers, scientists, and clinicians to know the principles and impact of design as a form of health communication. Due to the rise of social media, we need to be more discerning regarding the visual material we produce, disseminate and encounter. Infographics can be formulated by members of the research team with support from graphic designers if time and funding allow. Feedback from colleagues and 'test' members of the target audience can be crucial to check comprehensibility. Finally, while technology may afford more accessible means to create elaborate infographics, it is essential to ensure critical messages remain coherent and enriching to the target audience.

Footnotes

Disclosure: The authors have no potential conflicts of interest to disclose.

Contributed by

Author Contributions:

Conceptualization: Traboco L, Gupta L.

Data curation: Traboco L, Pandian H.

Formal analysis: Traboco L, Pandian H.

Investigation: Traboco L, Gupta L.

Methodology: Traboco L, Gupta L.

Project administration: Gupta L.

Supervision: Nikiphorou E, Gupta L.

Visualization: Traboco L, Gupta L.

Writing - original draft: Traboco L, Gupta L.

Writing - review & editing: Traboco L, Pandian H, Nikiphorou E, Gupta L.

References

21. Stones C, Gent M. Principles of Public Health Infographic Design. Leeds: University of Leeds, Public Health England; 2015. [ Google Scholar]

Articles from Journal of Korean Medical Science are provided here courtesy of Korean Academy of Medical Sciences

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Designing Infographics: Visual Representations for Enhancing Education, Communication, and Scientific Research

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Infographics are graphic visual representations of educational content, used to deliver complex information, disseminate scientific research, and drive behavioral change. Herein, we review some of the factors pertinent to designing infographics and the potential for automation in the future. To guide high-impact design, it is vital to clearly define the objectives of the infographic and its target audience.

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Abstract

In today's dynamic health systems, technology plays an important role in education and nursing work. So it seems necessary to study the role of nurses and highlight the need for appropriate information technology educational programs to integrate with the ever-increasing pace of technology. A review accompanied by an extensive literature search in databases and a library search focused on the keywords were used. The criteria used for selecting studies primarily focused on nursing informatics and the importance of expertise in the effective use of information technology in all aspects of the nursing profession. In a critical assessment of emerging technologies, the key elements of nursing informatics implementation were considered as healthcare promotion, advanced systems, internet and network. In view of the nature and the development of the information age, it is required to receive necessary IT training for all categories of nurses. Due to the fast development of technology, in order to effectively take advantage of information technology in nursing outcome and quality of health care and to empower nurses; educational arrangement is recommended to set short-term and long-term specialized courses focusing on four target groups: studying, working, graduate, senior undergraduate, and graduate doctoral. The result of this study is expected to assist educational providers with program development.

Keywords: education, nursing, quality of health care, nursing informatics, technology

1. Introduction

1.1 History and Definition

Nurses has been working in the field of informatics near four decades, the term "nursing informatics" has been considered a specialization in nursing resources since 1984 ( Guenther & Peters, 2006). Many aspects such as data recovery, ethics, patient care, decision support systems, human-computer interaction, information systems, imaging informatics, computer science, information science, security, electronic patient records, intelligent systems, e-learning and telenursing have been added to the field. Hana has defined Nursing Informatics as the application of IT in the nursing duties including education, management & practice in 1985. Integration of information science, computer science and nursing science to support nursing practice and knowledge management was the definition offered in 1989 by Graves and Corcoran. The American Nurses Association (ANA) published its aim and standards in 1994-1995 and presented the Nursing Informatics as a specialty that integrates nursing science, computer and information science to provide data communication management, knowledge and nursing work in 2001. Now most of nursing professionals believe that it is defined as the integration of information technology and all aspects of nursing such as clinical nursing, management, research or education ( Guenther & Peters, 2006).

1.2 Competencies

The competency of nursing informatics specialists was determined through studying three categories including computer skills, informatics knowledge and informatics skills. It investigates four levels of nursing practice: beginning nurse, experienced nurse, informatics specialist, and informatics innovator.

The following competencies were rejected: diagnostic coding, desktop publishing, managing central facilities to enable data sharing and writing an original computer program ( Staggers et al., 2002). Some components of accepted competencies are shown below in brief.

1.2.1 Computer Skills

Selected computer skill competencies contain computerized searches and retrieving patient demographics data, the use of telecommunication devices, the documentation of patient care, the use of information technologies for improving nursing care, and the use of networks and computer technology safely.

1.2.2 Informatics Knowledge

Selected informatics knowledge competencies are the recognition of the use or importance of nursing data for improving practice, and the recognition of the fact that the computer can only facilitate nursing care and that there are human functions that cannot be performed by computers, the formulation of ethical decisions in computing, the recognition of the value of clinicians' involvement in the design, selection, implementation, and evaluation of systems in health care, the description of the present manual systems, the definition of the impact of computerized information management on the role of the nurse and the determination of the limitations and the reliability of computerized patient monitoring systems.

1.2.3 Informatics Skills

Informatics skills competencies includes the interpretation of information flow within the organization, the preparation of process information flow charts for all aspects of clinical systems, the development of standards and database structures to facilitate clinical care, education, administration or research. It also includes the development of innovative and analytic techniques for scientific inquiry in nursing informatics and new data organizing methods and research designs with the aim of examining the impacts of computer technology on nursing, and the conducting of basic science research to support the theoretical development of informatics. Information literacy skills, competencies, and knowledge are investigated among educators, administrators and clinicians of nursing groups nationally.

1.3 The Importance of Nursing Informatics

The history, definition and competencies of nursing informatics indicate the importance of this field. It shows nurses are integrated into the field of IT automatically. So they should be able to deal with it successfully to improve quality of care outcome. In this regard it is required to study the influence of nursing informatics on health care and make bold the appropriate information technology educational needs for nurses.

2. Method

An extensive literature search was performed by using databases Pubmed, Google Scholar, Ovid, Science Direct and SID. Search terms were "education, nursing"; "quality of health care"; "nursing informatics" and technology. The study was carried out from January to April, 2014. A library search was also performed. As many as 135 articles were retrieved. With a critical point of view, 40 articles in English were selected that specifically focused on nursing informatics education and its influence on nursing outcomes and the quality of health care ( Staggers et al., 2002).

3. Results

The study mentions the followings as the key elements of nursing informatics implementation:

3.1 Health Care Promotion

The advantages of applying information technology in all aspects of nursing, including clinical areas, management, education and research and its influence on health care have been reviewed. Today, the subjects of clinical nursing information systems, decision support systems and medical diagnostic systems are associated with collecting patient information. Regarding the technology-rich environment, health care and hospital information systems developers, the quality of care is improving. For increasing patient safety and its leading to an evidence-based nursing, nursing informatics has been enhanced for students and graduates by Columbia school of nursing. The study has proved that informatics competence is a prerequisite to improving patient care ( Bakken et al., 2003). Technology and using multimedia integrated into nursing curriculum can promote the use of informatics tools as an integral practice component and increase patient safety ( Norton et al., 2006). Managers can improve efficiency and performance through information systems and new technologies. In addition, several studies have confirmed the impact of careful shift planning and efficient management on nurse's work and the quality of health care. Information is the source of all management activities. Nursing care is an industry service and its product is patient care. Information technology can promote the nursing management outcome. Internet-based nurse scheduling systems are mostly designed according to the self-scheduling model and need refining by the manager who overviews proper distribution, it causes uniform resource allocation in scheduling and increases patient direct care time ( Pierce et al., 2003). Implementing information systems can provide better access to evidence; it can affect the patient care quality and support evidence-based nursing. Software tools to facilitate research are available in all medical fields (Kardan & Darvish, 2008). Nursing information system had an influence on clinical patterns and decreased the time nurses spent on indirect care ( Darvish & Salsali, 2010). This is critical to the health care professionals to assess, apply, report and manage data by the help of new tools of the information age ( Hall, 1995).

3.2 Advanced Systems

Although using decision support systems can lead to a safer care, it may impair critical thinking. The need for excessive working time could cause some delay in the nursing job and reduce the quality of health care ( Norton, 2006). Researchers recommended considering the following:

-Involving nurse managers in the system selection and designing process

-Designing a simple and efficient process

-Recording a system-compatible guideline

-Improving the system speed

-Selecting hardware which can encourage nurses to use them

-Upgrading the system through innovation in information technology

Decision support systems have been defined to assist physicians to solve problems that require specific decisions since 30 years ago. It is replacing the role of human knowledge by formulating the knowledge in the system (Ting et al., 2008). Expert systems are the most common types of clinical decision support systems and have applications in show notes, diagnostic support, critical treatment plans, decision support, prescriptions, recovery and the identification and the interpretation of pictures, however, as stand-alone tools, are not able to replace human expertise. These systems should be integrated with knowledge management. Several studies have shown that the integration of implicit and explicit knowledge and management of different types of knowledge will help to determine the best treatment plan. Logical design is required for the success of these systems and seems to hardly have been considered. The application of guides and easy access to up-to-date clinical evidence and the cutting of duplicate tests could reduce medical mistakes and improve quality of care, but there are some limitations ( Montani & Bellazzi, 2002). To increase the usefulness and acceptability of such systems, the ease of use, support and maintenance combined with the ability of systems hardware, software applications, integration with hospital information systems and patient records should be considered ( Holbrook et al., 2003). Including smart and intelligent tools in diagnosis and treatment methods can reduce medical errors and harm as well as financial loss for humans. Artificial intelligence and expert systems are used to help the diagnosis.

3.3 Internet and Network

The first internet-based Nursing Informatics courses were offered by Duck University in 1997. Represented advantages were clearly defined and measurable learning outcomes and real-world problems were introduced as the main component of instructional strategies. There were some disadvantages like hardware and software problems, deficiency in prerequisite skills, troubleshooting difficulties, and low internet access speed and poor time management to master the material ( Goodwin, 1997). The evaluation of an innovative consumer health informatics intervention proved that the patient and nurses are satisfied with the use of electronic and communication devices and home care ( Kossman et al., 2006). Nurses can have networks such as AJN and AMN now. In the world wide web, it is possible to have a common network for nursing organizations and develop a forum to discuss nursing issues and design online meetings ( Rizzolo & DuBois, 1995). The other aspects of nursing practice potential in the new decade are offering services from distance through telemedicine or telenursing. In this regard, easy remote diagnostic software and hardware are designed to facilitate E-health services. Tele-nurses can provide various services such as education, patient monitoring and counselling through Internet facilities. Telenurses are satisfied with their role. They care remotely using special skills and knowledge. It can cover nurse shortages and the global demand for nurses ( Darvish & Salsali, 2010). In a telenursing Survey most of nurses believed that it is better to design educational programs for nurses to be able to work as telenurse ( Grady, 2007). Using online library resources and outreach programs would be beneficial and produce positive outcomes for nurses ( Wozar & Worona, 2003). Information and communication technology progress provided the possibility of improving health through e-education irrespective of time and place. Patient education systems on the internet can increase patient satisfaction and influence their self-care behaviour. E-health educational programs make people aware of disease management and increase coordination with the health care professional team. It influences the life style and the prevention of diseases such as cancer, HIV and chronic diseases. On the other hand, it empowers medical groups by enhancing and upgrading their knowledge. The web-based computer simulation educational program in crisis decreased medical errors in emergency departments. Information technology application refers to providing simultaneous access to education in specified locations which require huge spending. It decreases cost loss ( Kardan & Darvish, 2007). Online access to journal articles prevents repeated research programs and makes assessment easy. Online databases provided up-to-date article access and informed nurses about new technologies, easy software and the results of investigations ( Darvish, 2010).

3.4 Related Organizations Activity

National advisory council on nurse education and practice addresses nursing practice challenges; and mentions electronic health records, patient monitoring systems, bar code medication administration, computerised provider order entry, data capture tools, care planning tools and telehealth; and the need to consider nursing shortage, reducing medical errors, improve tracking of patient data, improve efficiency of data collection, improve access to care, support national surrveilance capabillities. These are also mentioned as ways to address nursing education challenges: E-learning to deliver education, simulation to deliver education, inclusion of Health Care IT in curricula, and faculty development for health care. It centeralizes these as solutions which can improve capacity of nursing schools, reduce faculty shortage, and increase health IT skills of graduating students. There are many group projects and organizations which support nurses' involvment for optimal use of IT in their job. Some are introduced in the following:

-The National Coordinator for Health Information Technology (ONC)

-The American Health Information Community (AHIC)

-The Nationwide Health Information Network (NHIN)

-The Faculty Development: Integrated Technology into Nursing Education and Practice Initiative (ITNEP)

-National Advisory Council on Nurse Education and Practice (NACNEP)

-National League of Nursing (NLN)

-National Institute of Health (NIH)

-National Institute of Nursing Research (NINR)

-North American Nursing Diagnosis International (NANDA)

-Technology Informatics Guiding Education Reform Initiative (TIGER)

It is reported that without proper training for nurses, efforts to integrate healthcare IT with nursing practice will be hampered. It gives evidence that nurses are not getting adequate training for IT usage ( NACNEP annual report, 2009).

3.5 Need for Educational Programs

Due to the fast growth and development of technology, in order to effectively take advantage of information technology in nursing outcome and quality of health care, educational arrangement is recommended to strengthen nurses at different levels for implementing information technology tools in all aspects of their profession.

4. Conclusion

In today's world the potential for information and communication technology application is increasing so that it can enhance the quality of nursing domains outcome ( McNelis et al., 2012). Nurses have the most communication with patients, and interact with technology more frequently. Using technology should create a positive attitude in nursing productivity. It is essential for nurses to be involved in the initial design of systems to improve the quality of health care and change their culture in this regard ( Darvish & Salsali, 2010), ( Jenkins et al., 2007).

Mediating technically and technologically on the borderline between medicine and nursing, nurses have become known as the medical Goddesses in the form of Tele-nurses. Nurses have got more authority in decision-making with the use of new technologies ( Gassert, 1998). For successful implementation of the electronic health reporting system, nurses must be knowledgeable about information technology, computer skills and informatics knowledge and skills. In telenursing, the importance of data quality criteria, transparency and integrity, authenticity, confidentiality, the updating of information, accountability, productivity, standards and accessibility of health web sites should be considered (Darvish, 2008). The NACNEP recommended to prepare nurses to adopt intelligent and quality-based information technology use in patient care by implementing five strategies: providing core informatics courses to nursing schools, educating nurses specialized in informatics skills who are able to solve related issues, offering more powerful nursing care through the implication of telecommunication projects, preparing more nursing faculties in the informatics field to facilitate students skills enhancement and enhancing collaboration to advance informatics. The benefits of extending nursing informatics strategies directly and indirectly influence patient and people health positively ( Matarrese & Helwig, 2000). Courses affect nursing students' perceptions about informatics ( Jetté et al., 2010); and they may learn at the BSc level about patient-centered evidence-based care through the use of informatics tools, and get informed about benefits such as promotion of safety, quality and effective clinical decisions ( Norton et al., 2006; Ainsley & Brown, 2009). The may even learn how remote care and personal phone can improve nursing care in different areas such as psychiatric nursing ( Tseng et al., 2012; Goossen, 2007; Wittmann-Price, 2012). At the same time, nursing workers are busy in the wards giving care. If they are not alert to new technologies, it will be difficult to accept the new nurses' ideas who are educated recently with a positive attitude to the advantages of information technology. This group of nurses can be encouraged to be integrated into the potential of E-learning as well as continuing education ( Button, 2013), based on the summit of technology informatics guide education reform ( Schlak & Troseth, 2013). It seems necessary to prepare knowledgeable nurses to deal with selecting, developing, implementing and evaluating IT to interpret data as usable knowledge and information. In the nursing world, four domains should be empowered. Undergraduate and diploma programs can be integrated with courses. Graduate programs can be designed. Formal and informal continued educational programs for nurses on job and fellowships for PhD graduated nurses can be useful. Trying to make different groups of nurses ready for the ever-increasing speed of technology in the current century is possible, not only by parallel opportunities of learning, but also with the help of evaluating tools such as Self-assessment of nursing informatics competencies scale which can bring the same range of comprehention about informatics implementation ( Choi & Bakken, 2013). In conclusion, considering nursing outcomes takes advantage of information technology; educational arrangement is recommended to set short-term and long-term specialized courses focusing on the four target groups. Informatics courses for nursing students continued educational programs for registered nurses in work area, graduate programs at MSc and PhD levels for nurses and fellowship programs for doctoral graduates are recommended to be considered ( Figure 1 ).

It is essential that nurse educators incorporate the entire concept of informatics into the education of nurses.

Acknowledgements

It is hereby acknowledged colleagues and all those who assisted in conducting the study or critiquing the manuscript. The main enthusiasm of this study came out from the international symposium of telemedicine was held by Tehran University of Medical Sciences.

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Articles from Global Journal of Health Science are provided here courtesy of Canadian Center of Science and Education

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The Role of Nursing Informatics on Promoting Quality of Health Care and the Need for Appropriate Education

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In today's dynamic health systems, technology plays an important role in education and nursing work. So it seems necessary to study the role of nurses and highlight the need for appropriate information technology educational programs to integrate ...

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Summary

Contemporary bioethics was fledged and is sustained by challenges posed by new technologies. These technologies have affected many lives. Yet health informatics affects more lives than any of them. The challenges include the development and the appropriate uses and users of machine learning software, the balancing of privacy rights against the needs of public health and clinical practice in a time of Big Data analytics, whether and how to use this technology, and the role of ethics and standards in health policy. Historical antecedents in statistics and evidence-based practice foreshadow some of the difficulties now faced, but the scope and scale of these challenges requires that ethics, too, be brought to scale in parallel, especially given the size of contemporary data sets and the processing power of new computers. Fortunately, applied ethics affords a variety of tools to help identify and rank applicable values, support best practices, and contribute to standards. The bioethics community can in partnership with the informatics community arrive at policies that promote the health sciences while reaffirming the many and varied rights that patients expect will be honored.

Keywords: Artificial Intelligence, Big Data, Bioethics, Clinical Decision Support Systems, Public Policy

Introduction

Ethics is a kind of lens we use to identify issues and a lever used to formulate and motivate best practices. Applied ethics is a tool to employ widely shared if not universal values to contemporary questions and challenges in science and the professions. Bioethics is the branch of applied ethics that addresses issues in the health professions; it is often linked to other kinds of applied ethics, including business ethics, computer ethics, government ethics, and so on.

It has become commonplace to observe or argue that science usually or even always outpaces, or advances, more swiftly than applied ethics. In the case ofhealth informatics, this is a mistake. For some four decades, albeit with some exceptions, advances in biomedical informatics have been matched step for step by scholars who have identified and addressed the ethical, legal, and social issues (ELSI) raised by the expansion of a new science. This acronym, borrowed from the Human Genome Project, has ably served the informatics community as a label or guidepost for research and pedagogy.

Though bioethics has moved forward, the same cannot be said for the law which continues to lag as a source of official governance and oversight in health informatics and other domains. It might be that the relationship between ethics and public policy represents the greatest challenge faced by health informatics and the society health informaticians seek to serve.

What follows from these two observations is this: we have an extraordinary opportunity at a crucial time to try to ensure that the insights and analyses provided by ethics continue to mature and, as important, that they are taken up and incorporated by academic and health care institutions, businesses, professional organizations, and governments.

In what follows, I expand on these points by filtering them through a number of contemporary challenges. These include artificial intelligence and machine learning; Big Data, data sharing and privacy; duties to use and manage new technology; and ethics and public policy.

1 Artificial Intelligence, Machine Learning, and Ethics

The ancient fantasy of an intelligent machine or a smart homunculus became a research project in the 17 th century when Gottfried Leibniz, the philosopher and logician who, with Newton, discovered the infinitesimal calculus, suggested that human reason could be rendered in a universal language such that argumentation could be reduced to calculation. He built a primitive calculator 1 , arguably the first machine to replicate an aspect of human thinking. Leibniz thought that intelligent machines would formalize reason and end disagreements. They did not. During the greatest disagreement in the history of civilization - World War II - code-breaking machines represented nontrivial instantiations of Leibniz' aspiration: "The first digital, electronic and programmable computer was developed as an instrument of warcraft. The Colossus was a room-sized collection of racks, pulleys, wires and some 2,400 bottle-sized vacuum tubes built at Britain's Bletchley Park to decipher encrypted German messages.... It became operational in 1944 and was used to prepare the D-Day invasion of Normandy. One could argue that it eventually saved more lives than most medical inventions" 2 .

However, we want health informatics to save and improve lives, to reduce suffering, to help to achieve the larger goals of the healthcare professions. If an intelligent machine can help do that, why is there a problem or any controversy?

Indeed, there are several problems, and their identification has contributed to both greater understanding and to a period of overheated handwringing.

Key and core ethical issues in the development and use of machine learning programs have already been identified repeatedly and elsewhere. They can be framed as lessons learned or as recommendations. Some are lessons for developers, some for users. Though the context here is health and health care, the lessons may be useful in many other domains.

1.1 Quality and Standards Are Ethical Issues

Good software conforms to certain standards for quality which can be assessed in terms of trustworthiness and reproducibility. The mark or measure of good software will include accuracy of documentation and transparency about the provenance or source of any code components. This is no mere courtesy to code-writing colleagues - it is an auditable track record of what code is intended to do and how it was modified along the way. This facilitates understanding, corrections, and improvements. It follows that if one is developing or modifying machine learning software, the automated learning process itself must also be monitored and documented. Relatedly, careful software version control is an essential part ofhigh-quali-ty programming. The values of transparency, veracity, and accountability reinforce the connection among quality, standards, and ethics 3 . This has long been true across the health professions.

1.2 Prevent and Eliminate Bias

A sure way to erode confidence in artificial intelligence is to identify ways in which a deep learning algorithm embeds racial, ethnic, gender, or other biases which shape or corrupt its results. The very nature of machine learning algorithms makes plain that one might unintentionally develop a biased system or accept biased results. Though intent matters in ethics - many actions are praiseworthy or blameworthy precisely because of someone's intent - this problem makes equally clear that failure to attend to and correct or mitigate a problem can also be blameworthy; that failure to do so is a kind of negligence (no surgeon ever intends to cut off the wrong arm or operate on the wrong patient). If datasets used for training machine learning algorithms include or entail bias or foster biased interpretations, then careful scrutiny of such sets is a difficult and labor-intensive approach to take. Another is careful screening of output to identify and filter bias, and perhaps to identify ways to modify the algorithm to suppress it. It is now even possible to incorporate anti-bias features into the code itself 4 . This is an extraordinarily promising approach, and something like it might very well be the best way to ensure that public health surveillance and prediction, disease diagnosis and treatment, and health policy are not infected or corrupted by illicit bias.

1.3 Use Machine Learning Software for Good and not Evil

Current relativist trends and fashions notwithstanding, some actions are good and others are bad and there is little credible dispute about their moral status. Reducing suffering, eliminating disparities, and improving health are good; depriving people of rights, using people for political or economic purposes without permission given voluntarily, and harming people for profit are bad. If we start there, great progress can be made. It is the hard cases, or cases on which reasonable people disagree, that pose the most significant challenges. Is there a chance that a clinical decision support system will improve diagnosis, treatment, and prognosis and simultaneously erode confidence in the clinician-patient relationship? May a nurse with a good computer system undertake duties traditionally reserved for physicians? Will the use of intelligent machines in health care cause the erosion of healthcare practitioner clinical skills? Addressing these and other challenges successfully will require sustained research, education, and debate. At the least, we will make great progress by acknowledging and grappling with such challenges.

1.4 Insist on and Provide Robust Education and Evaluation

No technology will ever fulfill its promise or be used in an ethically optimized manner unless we insist on and provide robust education and evaluation. Scientists and clinicians must not only be taught empirical methods and clinical skills, but also the appropriate uses of these methods and skills. Given our concern for appropriate uses of intelligent machines in healthcare, it follows we must also identify appropriate users, and a core criterion for demonstrating such appropriateness will be a user's fitness to use the tool. Making and acting on this assessment has long been recognized as a key ethical challenge when computers are used in healthcare 5 . We are, moreover, in urgent need of a comprehensive ethical curriculum in and for health informatics. We must also not lose sight of the importance of system (algorithm, device) evaluation in the context of its actual use; this, too, is an obligation long recognized 6 . Much more recently, system validation in real-world settings has been identified as a duty for anyone who would use an intelligent system in healthcare 7 . This may be especially true and important given the rapid progress in precision medicine.

2 Big Data, Data Sharing, and Privacy

Empirical sciences have evolved and grown by using data and information to support inferences which, if correct, come to constitute knowledge. In biology and medicine, we use that knowledge to prevent, predict, mitigate, and cure maladies that sicken, disable, or kill us.

A favorite example from the history of medicine of "calculating" to improve health care is that of bloodletting, used for millennia to treat and often unintentionally kill people who, under the humoural theory of disease, had too much blood. For instance, George Washington one day in 1799 awoke with a sore throat, asked to be bled, lost some five pints of blood, and was dead four days later, probably because of the bloodletting. The work of Pierre Charles Alexandre Louis (1787-1872) was likely unknown to him. Louis, a French physician, analyzed cases from several hospitals using his "numerical method" and determined that bloodletting was not a cure but, rather, usually harmful. He wrote: "As to different methods of treatment, it is possible for us to assure ourselves of the superiority of one or other.. .by enquiring if the greater number of individuals have been cured by one means than another. Here it is necessary to count. And it is, in great part at least, because hitherto this method has not at all, or rarely been employed, that the science of therapeutics is so uncertain" 8 .

Necessary to count? Numerical method? Is it - can it really be - that simple?

Yes, sometimes. Observational science and basic arithmetic were, at first, the best tools we had to support efforts to learn how the world worked. Observe and count (or measure), find the mean, note changes over time, and then make inferences about physical reality. An early form of statistics, frequency analysis, originated in the Middle Ages with the Arab philosopher Al-Kindi as a code-breaking tool more than a millennium before Colossus. His method also required counting - of letters. With an adequate "plaintext...long enough to ill one sheet or so," one could determine the frequency of letters and combinations of letters as a baseline and use it to determine the values of letters in a coded document 9 . It was a very early anticipation or foreshadowing of the data sets used to train artificial intelligence programs.

What became clear in the 17 th and 18 th centuries was the utility of using data about observations to make predictions. In the 1660s, John Graunt of London's "Natural and Political Observations Made upon the Bills of Mortality" constituted the first actuarial tables and spurred efforts to predict and control the causes of death. It is also the basis for modern insurance, including the business of using statistics to sell health "coverage" to people in need of health care in countries with dysfunctional health care systems.

The preceding part of this section embeds several important ethical issues. Chief among them are these:

Are data reliable and who is responsible for ensuring reliability?

How exactly does the calculation work?

How should it be determined who should use these calculators, and for what purposes?

These, of course, parallel the "lessons learned" we earlier identified for appropriate use of machine learning or any other medical software.

The datasets created by Al-Kindi, Louis, and Graunt were actually tiny; we might even call this "small data." Yet their work suggests the first grains or nuclei of a vastly larger project. Framed very broadly, one might argue that the work of these three data collector-analyzers raised ethical issues that parallel those we find when collecting and analyzing some of the more than 2,000 exabytes of medical data in the world today (an exabyte, recall, is 1,000 petabytes, with one petabyte being 1,000 terabytes, and one terabyte being 1,000 gigabytes, etc.). But their work did not cause any ethical consternation at the time; it was just science advancing.

Though we ought to regard privacy and confidentiality as universal rights, they are neither absolute nor have they ever been regarded as absolute. There are tradeoffs to be made: public health surveillance gives us better public health, hospital patient monitoring gives us better hospital care, the sharing of research results and other data gives us better medical treatments. Put differently: if a physician learns something about effective practice from Patient Epsilon's response to a treatment, does the physician violate Epsilon's privacy or confidentiality six months later by using what she learned to help Patient Omega 10 ? Indeed, such a reductio ad absurdum argument makes clear that not only does the physician not violate confidentiality but also that she would be irresponsible not to use the information from one patient to help another.

With very large datasets, however, our challenge is magnified. Until recently, the ability to reconnect a datum with a person, or "re-identify" the datum, was either impossible or difficult. Now it is comparatively easy, and this entails that the tools of applied ethics must in a world of Big Data rise to the occasion. One way to put this, according to my colleague Richard Bookman in a personal communication, is that "Ethical boundaries move with scale. As little data goes to Big Data, onetime practices at small scale may be rendered offensive and unacceptable at broader scale."

The same could be said for data sharing. Few would argue that science progresses when investigators communicate openly and generously with each other. It also progresses when scientists are motivated by curiosity and service instead of profit and priority - at least historically. When the financial stakes were low, history shows discovery science was fertile and productive. That slowed when fame attached to empirical success and reputations were made by being the first to make a discovery. We then commodified science so that not only would a good scientist learn something, he could also make a lot of money. In 1955, Jonas Salk famously disdained patenting his polio vaccine ("Could you patent the sun?"); it was the same year the Dartmouth Workshop, at which Artificial Intelligence as a field was fledged, was proposed. In 2020, one would be regarded as a fool for not leveraging a discovery or method for profit. Big Data means big profit, and the ethical boundaries have correspondingly shifted.

It would perhaps not be so difficult if the accumulation of wealth from others' information could rely on their concurrence and support. Worse, the erosive use of Big Data for profit in business and industry has polluted public perceptions of all uses of Big Data. We learned earlier that applied ethics can contribute to the identification of appropriate uses of information technology. Here we have an opportunity to protect fundamental privacy rights by making a distinction between using social media, for instance, to track people's behavior to target them for political purposes, and using electronic health records to analyze their data to improve clinical outcomes. There is indeed evidence that ordinary people are prepared to accept this distinction, at least if the user is trustworthy 3 .

We have several ways to meet the privacy challenges posed by Big Data for health care. All of them should be seen as trust-enhancing.

The ffirst is security backed up by law. Make it difficult for unauthorized - inappropriate - parties to access or use the data, and punish them severely if they do. Constant and evolving research improves privacy-protecting software. The 2018 IMIA Yearbook of Medical Informatics, for instance, included a special section ("Between Access and Privacy: Challenges in Sharing Health Data") that featured a suite of articles describing research devoted to improving digital privacy protection 11 . It is clear that the good guys write better software than the bad guys. Use it.

Next, insist on accountable oversight. From bioinformatics laboratories to hospitals, those who collect, store, and analyze health information are stewards of a precious resource. They must, absolutely must, be held to trust-enhancing standards. The "trusted broker" model of scrutiny and oversight can ensure both innovative research and the protections that are demanded by the sources of health data and information, i.e., usually, patients 3 . Institutions and governments need to require and help implement mechanisms to review, scrutinize, and approve collections and analyses of health data. This builds trust and makes clear that those who use health data are motivated by values different than those who scrape online clickstreams to beguile shoppers, those who vacuum social media postings to monetize the intimate behavior of strangers, and those who weaponize the Web to undermine democracies. They wanted, rather, to heal the sick.

Finally, educate lay people to improve digital health literacy, and teach scientists and clinicians to learn to appreciate that the opportunity to study others' information is a privilege. If the former are willing to share data for better health and the latter are motivated by higher values than those that govern the marketplace, then we reinforce the foundation of a collective, international edifice that both protects legitimate privacy interests while simultaneously ensuring enjoyment of the right to benefit from science 12 .

It might be that the greatest utility of applied ethics is in the balancing of rights, in part by the identification of responsibilities.

3 Duties to Use New Technology

Information technology has transformed daily life, business, entertainment, and health care. The list could, of course, go on, and on. The proper criterion to use in determining if any new tool should be used is, generally, whether its advantages outweigh its disadvantages and, if so, whether any antecedent rights are being violated. We learned more than three decades ago that if health information technology could improve health, then, ceteris paribus, this entailed a duty to use it for that purpose 5 .

Such an insight does not of itself begin to answer the many questions that follow - that is the job of scholars and analysts who scrutinize appropriate uses, privacy implications, and governance. It nevertheless signals a powerful moral commitment to reject basic Luddism, the movement that began in the 19 th century with British workers smashing mechanical looms because the machines imperiled their jobs. The best approach is to control machines, not destroy them.

One of the most promising and systematic approaches to achieve that control at scale is the Learning Health System (LHS) paradigm 13 , an evidence-building, safety-promoting system "capable of continuous self-study and improvement" 14 . Such a system is a data-driven creature; it learns from every patient encounter, every lab result, every outcome tracked and measured; it learns from public health prevention and intervention. It captures Louis' fabulous and simple understanding that counting can count for a lot 8 . Moreover, LHS embodies core values of evidence-based practice, most especially that it is neglectful and blameworthy to lose information that could have been used to improve the wellbeing of individuals and the health of populations.

The LHS also provides an excellent example of and justifies what has been called the "secondary use" of health information. In many respects, the term "secondary use" is a misnomer: it implies that health information collected in a clinic, for instance, is and ought to be collected for a primary purpose, and that any other purpose enjoys lesser warrant. Similarly, it would be peculiar to collect public health information and then regard that information's use in a clinic as secondary and requiring additional permission. Rather, it would be irresponsible to acquire information in a clinic, research study or public health surveillance effort and not use it to support one of the others 15 16 17 . Widespread views and focused regulations regarding how best to protect these three kinds of data and information are largely historical artifacts. Clinic data is protected by privacy laws that evolved to protect, well, clinical data. Research data use is governed by rules aimed at protecting human subjects, including their privacy. Public health findings are, and always have been, acquired with the presumed or tacit consent of those who benefit from - and trust - competent public health authorities.

Developers of Learning Health Systems (or "the" system if there is one and it is global) are wise and quick to identify the values that reinforce it: person-focused, privacy, inclusiveness, transparency, accessibility, adaptability, governance, cooperative and participatory leadership, scientific integrity and value 14 . Indeed, one would be forgiven for suggesting that those values should underlie any health system.

How such values are embraced will continue to challenge our best inclinations. The effort to make the most of health data and information, however, is a collective recognition of the overarching duty to use tools that improve health. Perhaps the most difficult challenge is shaped by the values of inclusiveness and accessibility; that is, inclusiveness and accessibility for whom?

Any duty to use a tool is cheapened or diluted if the tool benefits only those in some jurisdictions or some socio-economic classes. If we intend a health system to serve all, and if we know that social determinants are key etiologic sources of illness, then perhaps a Learning Health System can be used to reduce disparities. Universalizing access to health care, which has so far eluded most countries, would require far more than data analysis - but such analysis in conjunction with tailored social commitment might at the least effect some improvement. To be sure, such a course would require the inclusion of social determinants, including poverty, in electronic health records or other repositories of patient information. This in turn could cause or magnify stigma, and it might be the case that some people would disdain being thus identiied. 1

A robust attention to ethics and informatics cannot provide a script or formula for righting wrongs. It can however usefully identify issues to be addressed, scrutinize approaches to confronting those issues, and help point the way to the best possible solutions. There is, as well, an element of professionalism in this regard, and major informatics organizations have developed codes of ethics to guide those working in the field to follow such precepts as help to secure the integrity of the profession 18 19 .

4 Ethics, Standards, and Public Policy

Once we figure out how to get something right, it can be irrational to insist it should be ignored. The evolution of standards in health care has improved quality, increased safety, and conserved resources. This is especially true for health informatics. What follows should be obvious: if continuously reined and improved standards can achieve so much, and if those achievements improve human health and welfare, then there is an ethical imperative to develop and improve them.

Any laws in the discovery and communication process - there are a lot of standards, some conflict, and others are not very good - are remediable. As was once suggested, "What is wanted here, in informatics, is in an important if vague respect what is wanted everywhere in the world of standards: we want to be as good as we can be, along with our efforts and tools, and to improve. That part of 'standard' that implies regularity or consistency captures the intuitions that order is more successful than chaos, and that clarity and order produce goods and services that are easier to explain and share. We have something very important to accomplish in health information technology, and good standards are necessary for the process" 3 .

Standards are also an important component of public policy and one of the ways by which applied ethics informs and shapes public governance.

From the foundations of software engineering to the design of electronic health records to embedded privacy protections to evaluation and interoperability, ethical principles and standards serve as both guiderails and signposts. Is there a problem with biased algorithms? Adopt standards for better software and testing. Do members of communities distrust those who collect and analyze personal information? Follow standards for trust enhancement. Confused about whether to adopt a new technology? Then turn to ethical standards for harm reduction and rights protection.

As elsewhere in life, we sometimes in health informatics are over-eager to make problems seem intractable or lose sight of the fact that we have available many tried-and-true conceptual devices and publicly accountable processes for solving these problems. To say, as some are wont, for instance, "privacy is dead; get over it," is a sad surrender to an ancient challenge tricked out with very large data bases and powerful computing tools. Such surrender is neither necessary nor apt.

If we care enough about these issues, and we should, then applied ethics emerges as the silent partner or loyal opposition to speak truth to a power whose benefits we deserve and whose harms we must protest. When appropriate, in civil society, the law itself can be a partner in ensuring that standards capture best practices. Ethics should always come ffirst, both to guide us and to reduce the likelihood of health-system laws that permit or even encourage unethical conduct.

Conclusion

Ordinary language loves the locution "ethical dilemma." But to be on the horns of an ethical or moral dilemma is to be in a position such that no matter what one does, one does something wrong. Most ethical problems or challenges are not dilemmas, and even the dilemmas, if they be such, can, as in logic, be escaped. The project here has been to argue and so to make clear that the tools of applied ethics are adequate to the task of guiding developers, users, and institutions as they adopt and try to make the most of health information technology. This has been framed as statements of issues and problems (machine learning, privacy, and duty to use the new tools) and as suggestions for addressing them. As elsewhere in the health professions, the ethical issues raised by a new technology are sources of interesting and significant challenges. These challenges are difficult, but not intractable. Finding their solutions presents opportunities to use critical thinking in the service of shared values. Chief among these values is that of health itself. Producing better and ethically optimized tools for healers is a contribution requiring the collaboration of the informatics and ethics communities. Such collaboration affords a rare and unparalleled opportunity.

Footnotes

¹ I am here again indebted to my colleague Richard Bookman for thoughts about the intersection of social determinants and health information technology.

References

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Articles from Yearbook of Medical Informatics are provided here courtesy of Thieme Medical Publishers

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Contemporary bioethics was fledged and is sustained by challenges posed by new technologies. These technologies have affected many lives. Yet health informatics affects more lives than any of them. The challenges include the development and the appropriate ...