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Kelly Conklin, MSN, CENP, VP and Chief Clinical Officer, PerfectServe

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Fighting Alarm Fatigue for Nurses in 5 Steps | PerfectServe

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Kelly Conklin, MSN, CENP explains how integrating disparate, noisy alert and alarm systems can help fight alarm fatigue for nurses and care teams.

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Home health care agencies provide skilled nursing care, as well as other care services such as physical and occupational therapy, speech-language therapy, and medical social services. The demand for home health care has grown as medical science and technology has improved and treatments that could previously only be completed in a hospital can now be administered at home.

Home health care staff provide therapy as directed by the patient's physician and assist with the coordination of care. The staff will typically create a plan of care and keep the physician updated on the patient's status, potential issues, and concerns.

Home health care can consist of short- and long-term care. The goal of short-term home health care is to provide treatment for an illness or injury. Long-term home health care aims to maintain the patient's highest level of ability or health, and assist in helping the patient learn to live with the illness or disability within the comfort of their home.

Challenge

Home health caregivers are tasked with ensuring that the directives of patients' physicians are carried out, and helping patients be as self-sufficient as possible. To that end, maintaining swift and accurate communication with physicians is vital to help the caregiver do their job correctly.

Many home health care agencies still rely on fax machines, phone calls, and personally hand delivering critical medical documents between the hospital or health center and the agency, and the patient's provider and the caregiver.

These labor-intensive methods can be challenging, and faxing documents can be prone to transmission errors. Medical documents are often time sensitive and engaging in phone calls can result in long wait times for responses. Home health caregivers may not receive necessary documents such as physician orders, progress notes, and summaries in a timely fashion.

Solution

Developing a secure Direct Messaging connection between hospitals, medical practices, community health centers, and home health agencies can mitigate many of these concerns. Setting up this connection can be achieved by using the organizations' Direct Messaging solution within their EHR system or by utilizing Mass HIway webmail.

Implementing an electronic workflow between the hospital and the home health agency allows the agency and its caregivers to receive accurate documents more quickly than faxing or phone calls. Caregivers are able to receive the relevant home health care documents in a timely fashion, allowing them to act swiftly and according to the provider's instructions.

This electronic process eliminates the need for caregivers to pick up hard copies from the home health care agency's office or to go directly to the provider, and ensures that all information is accurate and received in a timely and secure manner.
Benefits
Home health care agencies offer many benefits both for the patients as well as the providers. Patients are more comfortable at home, and family caregivers are more easily able to support them. Delivering important medical care in the home also frees up hospital and health center resources that may be needed to provide care to other patients. Having a well prepared caregiver visiting the patient in the home also ensures that the proper medical procedures will be followed, and reduces the likelihood of re-admittance to a hospital.
Implementation
Home health care agencies work diligently to provide clients with high-quality and cost-effective care. Many agencies would like to collaborate with hospitals, medical practices and community health centers to design and implement electronic flows of information to further enhance patient care.

Boston Home Health Aides, a Medicare/Medicaid certified home health care agency, accredited through the Community Health Accreditation Program (CHAP), is actively pursuing such collaboration with healthcare organizations. The agency offers in-home services including skilled nursing, physical and occupational therapy, and other care.

Currently, the agency must first collect the necessary documents from clinicians through faxing, phone calls, and physical office visits, before they can disseminate the relevant documents to each caregiver. The organization is using its existing Mass HIway Webmail connection to transition to an electronic workflow which would allow them to receive the documents directly. This would reduce travel and time delays, and fax or phone transmission errors. Getting the documents in a more timely fashion would allow the caregivers to spend more time working directly with their clients.

Moving Forward

Boston Home Health Aides is working with the HIway Adoption and Utilization Support (HAUS) services team to initiate HIway connections with key healthcare organization partners. The HAUS team provides project management and HIway expertise at no cost to the healthcare organization as part of the Mass HIway's commitment to increasing the secure and efficient exchange of health information in the Commonwealth.

Boston Home Health Aides, and other home health care agencies, may need assistance in identifying new trading partners with whom to trade. If your healthcare organization is interested in working with these home health care agencies to set up an electronic exchange of care documents, please contact the Mass HIway at masshiway@state.ma.us.

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Utilizing Electronic Information Flows to Improve Communication between Home Health Care Agencies and Hospitals, Community Health Centers, and Medical Practices

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Home health care agencies provide skilled nursing care, as well as other care services such as physical and occupational therapy, speech-language therapy, and medical social services. The demand for home health care has grown as medical science and technology has improved and treatments that could previously only be completed in a hospital can now be administered at home.

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To Err is Human: A Patient Safety Documentary (trailer)

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To Err is Human: A Patient Safety Documentary (trailer)

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This documentary emphasizes the importance in addressing mistakes in healthcare in order to prevent these mistakes in the future.

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To Err Is Human: A Patient Safety Documentary | IMDb

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Apple will let you keep your medical records on your iPhone

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Apple COO Jeff Williams tells CNBC the company is testing a service that lets users store and view their health data.

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An informative visual representation of the QSEN Competencies

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An article which expands on the importance of TIGER, giving additional background on how the world can collaborate to join in with this initiative.

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by Jennifer H. Garvin, PhD, RHIA, CPHQ, CCS, CTR, FAHIMA; Karen S. Martin, RN, MSN, FAAN; Debee L. Stassen, RN, PHN; and Kathryn H. Bowles, PhD, RN, FAAN
Electronic records are introducing HIM professionals to new coding schemas. One is the Omaha System, used by a variety of caregivers including nurses.

Every day HIM professionals work with coded data. Most commonly these include ICD-9-CM and CPT codes. With the development of a full electronic health record (EHR), they will come into contact with additional coding schemas available to clinicians to describe patient care. An example of this is the Omaha System. The Omaha System provides a structured approach to document patient problems, multidisciplinary interventions, and outcomes at the point of care.

The Omaha System is a research-based taxonomy (classification) designed to enhance practice, documentation, and information management across settings. HIM professionals may encounter the Omaha System in home care, hospice, long-term care and assisted living, public health,

schools, chronic illness hospitals, and hospital-based and other case-management settings.

Users include nurses, physicians, occupational therapists, physical therapists, registered dieticians, recreational therapists, speech and language pathologists, and social workers.

When multidisciplinary health teams use the Omaha System accurately and consistently, they have an effective basis for documentation, communication, coordination of care, and outcome measurement.

Omaha System Overview

Components

Terms

Purpose

Problem Classification Scheme

4 domains

42 problems

2 sets of modifiers

Clusters of problem specific signs/symptoms

Intervention Scheme

4 categories

75 targets and 1 other

Client-specific information

Problem Rating Scale for Outcomes

3 concepts

5-point Likert-type scale

Bringing IT to Bear on Key HIM Functions

Increasingly, entries in contemporary EHRs consist of structured text intended to promote interoperability, the ability to exchange coded data among computerized documentation systems. Structured clinical data are not buried in data cemeteries such as file folders or storage media; instead, they can be mined to produce valuable reports and research. Ideally an EHR system is designed to facilitate data capture that promotes these multiple, subsequent uses.

Learning about the Omaha System brings new insights and a fresh perspective to documentation processes that can lead to improved data capture and require less intensive efforts by HIM professionals to extract meaningful data. Focusing attention on information management that enhances practitioner workflow and supports quality data capture is the foundation for a well-designed EHR and is a shared value of the Omaha System.

A main purpose of the problem-oriented medical record developed by Lawrence Weed is to manage the complexity of medical knowledge and clinical data while increasing consistency of structure and content of progress notes. The problem list was envisioned as the central place for clinicians to maintain a concise view of a given patient's medical problems in the health record and to facilitate an orderly process of medical problem solving and clinical judgment.

The Omaha System helps clinicians build upon these concepts by focusing attention on the patient problems, supporting a description of the care provided, and then offering a quantifiable method to rate the outcomes achieved.

Capturing Data for Automated Reuse

Technology assists communication by automating the multidisciplinary problem list via embedded clinical terminology standards and data-mapping tools. For example, once a problem list is developed, mapping allows the capture of data in the EHR with the terminology best suited to the needs of the healthcare professional. The resulting automated reuse of data assists the clinician or researcher in using the data for multiple subsequent purposes.

Mapping develops links between concepts within one data set (e.g., a classification or terminology) to the same or substantially similar concepts in another data set. Use of a standard clinical terminology facilitates sharing of problem lists within a healthcare enterprise or with other providers. The mapping also allows the linkage of problems with their interventions and supports the analysis of their relationship to outcomes.

The benefits of using controlled vocabularies, mapping, and data content standards are many. Consistent, standardized naming of terms facilitates the reporting of laboratory data; use of order sets, clinical guidelines, clinical system alerts and reminders; and the automated retrieval of relevant medical literature within an EHR.

Using a classification system such as the Omaha System at the point of care enables practitioners to focus on relevant information in an objective way. The Omaha System helps document and evaluate aspects of care including problems, interventions, and outcomes in practice, education, and research settings.

The Problem-Solving Process

Describing the System: Components and Terms

The Omaha System consists of three components: the Problem Classification Scheme, the Intervention Scheme, and the Problem Rating Scale for Outcomes (see "Omaha System Overview" on page 45). The components offer users a relational, reliable, and valid structure and set of terms that can link clinical data to demographic, financial, administrative, and staffing data.

A model of the system (shown above) reflects the circular, interactive nature of the process from data collection, critical thinking, and clinical decision making through to evaluation and quality improvement. This process occurs within the dynamics of the practitioner-client relationship. The center of the model identifies the pivotal position of the individual, family, and community and the partnership with multidisciplinary practitioners.

The Problem Classification Scheme is equivalent to the leading two wedges of the model's circle, "collect and assess data" and "state problem." The scheme is a comprehensive, orderly, nonexhaustive, mutually exclusive taxonomy designed to identify diverse health-related concerns. Its simple and concrete terms are used to organize assessment at four levels, flowing from general to specific.

The Intervention Scheme is equivalent to the wedge "plan and intervene." It has a hierarchical structure similar to that of the Problem Classification Scheme. Its three levels of actions or activities flow from general to specific and are used to describe care plans and services provided by multidisciplinary health professionals.

The Problem Rating Scale for Outcomes is equivalent to three wedges: "identify admission problem rating," "identify interim/dismissal problem rating," and "evaluate problem outcome." It measures the entire range of severity for the concepts of knowledge, behavior, and status. Each subscale provides a continuum for examining problem-specific ratings for individuals, families, or communities at regular or predictable times.

Years in the Making, Use Increasing

Practitioners developed the Omaha System as part of four federally funded research projects conducted between 1975 and 1993. Staff and managers at the Visiting Nurse Association of Omaha and seven additional test sites revised and refined the structure and terms and established reliability, validity, and usability.

The Omaha System was intended to be as intuitive, brief, and flexible as possible. Its structure, terms, definitions, and codes have not been copyrighted so that they are available for use without permission. Although the Omaha System exists in the public domain, it is necessary to maintain its integrity and identify a reference in publications and software.

Initial adopters of the Omaha System included US community settings. Use has expanded across the continuum of care and internationally. Approximately 8,000 practitioners use point-of-care Omaha System software at 300 locations, and 2,000 practitioners use paper-and-pen records. The number and type of vendors who base their clinical documentation software on the Omaha System is increasing rapidly.

The Omaha System was one of the first terminologies recognized by the American Nurses Association in 1992. It is integrated into SNOMED CT, LOINC, and the National Library of Medicine's Metathesaurus; it is indexed in CINAHL and recognized by standards organization Health Level Seven. In 2007 the Omaha System passed the Healthcare Information Technology Standards Panel tier 2 selection criteria for a use case.

Teaching through Case Studies

The case of Ander M., presented on the following pages, demonstrates the value of a standardized language that describes care needs and treatments provided. The case study consists of two sections: a story and answers.

The story depicts a fictional client, Ander M., and the services he received from a home care nurse. Included are referral details, data that the nurse obtained during the visit, and clues for identification of Omaha System problems, interventions, and ratings.

The answers are terms from the Problem Classification Scheme, the Intervention Scheme, and the Problem Rating Scale for Outcomes. Brief comments placed in parentheses clarify selected answers.

Omaha System case studies have proven to be an effective teaching tool because they offer practice opportunities for new learners and refresher opportunities for experienced users. When used in a group setting with a leader experienced in the Omaha System, case studies facilitate discussion that promote inter-rater reliability.

Promoting Good Documentation at the Time of Care

The effort made by HIM professionals to evaluate documentation after its creation is an important function. The Omaha System offers help at the point of care by facilitating good documentation during its creation. The Omaha System can influence the quality of documentation and data integrity, which results in improved care. HIM professionals can benefit from understanding how the system intertwines patient care and data integrity seamlessly.

The system facilitates innovation as it makes use of many potential health IT benefits, including incorporating data standards and terminologies, supporting evidence-based practice, and developing outcomes data. Because documentation is at the heart of the HIM practice, it is important that HIM professionals become familiar with methods that help practitioners increase data integrity in the EHR at the point of care.

Case Study: Ander M.

Application of the Omaha System

DOMAIN: ENVIRONMENTAL
Problem Classification Scheme
Modifiers: Family and Actual
Signs/Symptoms of Actual:

Inadequate/obstructed exits/entries

Unsafe mats/throw rugs

Structural barrier

Intervention Scheme
Category: Teaching, Guidance, and Counseling
Targets and Client-specific Information:

Safety (develop plan to remove throw rugs, clutter, toys; add rail; improve lighting)
Category: Surveillance
Targets and Client-specific Information:

Safety (completed safety checklist and noted needed modifications)

Problem Rating Scale for Outcomes

Knowledge: 2-minimal knowledge (family now concerned about increasing falls, need to increase safety)

Behavior: 2-rarely appropriate behavior (family did not modify environment/residence for 6 months)

Status: 3-moderate signs and symptoms (moderate clutter, throw rugs, toys, poor lighting)

DOMAIN: PHYSIOLOGICAL
Problem Classification Scheme
Modifiers: Individual and Actual
Signs/Symptoms of Actual:

Difficulty seeing small print/calibrations

Difficulty seeing distant objects

Difficulty seeing close objects

Absent/abnormal response to visual stimuli

Problem Classification Scheme
Modifiers: Individual and Actual
Signs/Symptoms of Actual:

Diminished judgment

Disoriented to time/place/person

Limited recall of recent events

Limited reasoning/abstract thinking ability

Intervention Scheme
Category: Teaching, Guidance, and Counseling
Targets and Client-specific Information:

Day care/respite (an option for Ander while the family was gone)

Medical/dental care (needs evaluation especially for mental status)

Safety (needs supervision while family was gone)

Stimulation (needs activities with the family and during the day)

Category: Surveillance
Targets and Client-specific Information:

Signs/symptoms-mental/emotional (cognitive changes)

Signs/symptoms-physical (vital signs, weight, general health)

Problem Rating Scale for Outcomes

Knowledge (Family): 3-basic knowledge (family concerned about cognitive changes)

Behavior (Family): 3-inconsistently appropriate behavior (family requested home visit, beginning to consider alternatives to current situation)

Status: 3-moderate signs and symptoms (Ander oriented to person/place but not to time/date; difficulty with short-term memory; dressed appropriately; reasonable discussion)

References

AHIMA. "The EHR's Impact on HIM Functions." Journal of AHIMA 76, no. 5 (May 2005): 56A-H.

American Nurses Association. "ANA Recognized Terminologies and Data Element Sets." Available online at www.nursingworld.org/npii/terminologies.htm.

Cimino, James J. "Collect Once, Use Many: Enabling the Reuse of Clinical Data through Controlled Terminologies." Journal of AHIMA 78, no. 2 (Feb. 2007): 24-29.

Foley, Margaret, et al. "Translation Please: Mapping Translates Clinical Data between the Many Languages That Document It." Journal of AHIMA 78, no. 2 (Feb. 2007): 34-38.

Home Health Care Management and Practice 16, no. 3 (Apr. 2004).

Institute of Medicine, Committee on Improving the Patient Record. The Computer-based Patient Record: An Essential Technology for Health Care. Revised edition. Edited by Richard S. Dick, Elaine B. Steen, and Don E. Detmer. Washington, DC: National Academy Press, 1997.

Martin, Karen S. The Omaha System: A Key to Practice, Documentation, and Information Management. Second edition. St. Louis, MO: Elsevier, 2005.

Omaha System Web site, www.omahasystem.org.

Weed, Lawrence. "Medical Records That Guide and Teach." New England Journal of Medicine 278 (1968): 593-600, 652-57.

Acknowledgment

Joyce Sensmeier, MS, RN-BC, CPHIMS, FHIMSS
Jennifer H. Garvin (jennifer.garvin@va.gov) is a postdoctoral fellow at the Center for Health Equity Research and Promotion at the Philadelphia VA Medical Center, a lecturer at the University of Pennsylvania School of Nursing, and member of the New Courtland Center for Transitions and Health. Karen S. Martin (martinks@tconl.com) is a healthcare consultant based in Omaha, NE. Debee L. Stassen is director of nursing services at the Pine Medical Center Hospital and Health Care Center, Sandstone, MN. Kathryn H. Bowles (bowles@upenn.nursing.edu) is an associate professor at the University of Pennsylvania School of Nursing and the New Courtland Center for Transitions and Health.

Article citation:
Garvin, Jennifer Hornung. "Omaha System: Coded Data That Describe Patient Care" Journal of AHIMA 79, no.3 (March 2008): 44-49.

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Omaha System: Coded Data That Describe Patient Care

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by Jennifer H. Garvin, PhD, RHIA, CPHQ, CCS, CTR, FAHIMA; Karen S. Martin, RN, MSN, FAAN; Debee L. Stassen, RN, PHN; and Kathryn H. Bowles, PhD, RN, FAAN Electronic records are introducing HIM professionals to new coding schemas. One is the Omaha System, used by a variety of caregivers including nurses.

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Something positive may come out of the havoc that COVID-19 wreaked: A patient safety boost thanks to home monitoring and hospital at-home models.

Studies found the models were showing promise before the pandemic. COVID-19 accelerated the need to monitor patients treated in standard medical units, emergency departments and patients who were at home to increase hospital capacity as beds filled up during the pandemic, patient safety and value champion Peter J. Pronovost, MD, and co-authors wrote in a recent JAMA Viewpoint essay.

"Home monitoring and hospital at-home models offer the potential to transform care and potentially allow a substantial proportion of hospitalized patients to receive care from home. Yet, health systems will need to collaborate with technology companies to accelerate learning and produce greater value for patients, clinicians, and health care organizations," says the Viewpoint, written by Dr. Pronovost, Melissa D. Cole and Robert M. Hughes, DO.

Dr. Pronovost is chief quality and clinical transformation officer at University Hospitals in Cleveland and wrote the book Safe Patients, Smart Hospitals. Cole is the vice president of integrated delivery operations at University Hospital Health System in Cleveland and Dr. Hughes is an emergency physician at University Hospitals, Cleveland Medical Center.

The AMA Remote Patient Monitoring Implementation Playbook offers a step-by-step process to help guide your practice through the implementation of remote patient monitoring. It is part of the AMA Digital Health Implementation Playbook Series that includes a Telehealth Clinical Education Playbook and a Patient Access Playbook.

A study in the Journal of Patient Safety based on pre-pandemic data suggested that routinely monitoring hospitalized patients with continuous pulse oximetry and heart rate devices was associated with reduced mortality, the viewpoint notes. But the surveillance system wasn't broadly adopted.

The pandemic led more health systems to use monitoring and therapy based on patient risk and needs. Yet, though many health systems reported expanding their monitoring use, there is limited evidence on the prevalence, safety and efficacy and types of patients who most benefit from which type of therapy, the JAMA Viewpoint authors wrote.

In their essay, they cited some promising research.

For example, a cost-utility analysis of remote pulse-oximetry monitoring of patients with COVID-19 published in Value in Health in 2021 found that daily assessment and three-week follow-up of at-home pulse oximetry monitoring was projected to be potentially associated with a mortality rate of six per 1,000 patients compared with 26 per 1,000 COVID-19 patients without at-home monitoring.

The study also projected remote monitoring could potentially be associated with 87% fewer hospitalizations, 77% fewer deaths, reduced per-patient costs of $11,472 over standard care and gains of 0.013 quality-adjusted life-years.

Centers for Medicare & Medicaid Services (CMS) payment reform helped accelerate the move to risk-based monitoring. In December 2020, CMS began requiring two days of monitoring to bill for remote patient monitoring-a drop from the 18 days previously required before.

"Before this change, most home monitoring covered was for remote physiological monitoring," the Viewpoint notes. "Also, CMS launched the Acute Hospital Care at Home program in which hospitals meeting criteria to provide hospital-level care at home are paid the full in-hospital diagnosis-related group payment."

Learn more with the AMA about navigating digital medicine coding and payment (PDF).

Despite advances, the authors wrote that health systems must overcome five barriers to start taking advantages of the monitoring services. They should:

Consider implementing continuous pulse oximetry and heart-rate monitoring for all hospitalized and ED patients. Continuous monitoring could improve patient safety and reduce clinician workload at a time when there is lower staffing for most clinical roles.

Create a service line to coordinate the work and give it a research focus to better understand whether, how, why and in whom these therapies improve quality and value.

Put together a mechanism for billing to reduce the burden associated with monitoring at-home patients.

Combine and integrate several technologies-for example, monitoring, telehealth, chat bots, triage and scheduling-to maximize value.

Establish protocols to select the right patients and enroll them in the right services based on their needs.

Learn how CPT codes set the stage for telehealth's rise amid the pandemic.

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COVID-19 brings safety boost with rise of remote patient monitoring

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Something positive may come out of the havoc that COVID-19 wreaked: A patient safety boost thanks to home monitoring and hospital at-home models. Studies found the models were showing promise before the pandemic.

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Steven Posnack | November 16, 2021

When President Obama signed the bipartisan 21 st Century Cures Act (Cures Act) into law in 2016, it marked a significant shift in health policy and health law. Not since the Health Insurance Portability and Accountability Act of 1996 (HIPAA) has there been a more noteworthy change in how electronic health information (EHI) is approached under United States federal law. Importantly, the Cures Act's information blocking provision should always be considered in the context of other laws that speak to how EHI is shared in health care.

The Cures Act's information blocking provision requires new thinking, new practices, and adjustments to prior norms. It also potentially makes the "starting line" for evaluating whether a given practice might be considered information blocking different depending on the fact pattern and the actor(s) involved ( health care providers, health IT developers of certified health IT, and health information networks or health information exchanges, as defined in the information blocking regulations). As a result, understanding how the information blocking provision may apply to particular practices must account for case-by-case details, including whether a regulatory exception has been met, what an actor's intent was, and whether an interference in the access, exchange, or use of EHI has occurred.

When it comes to the information blocking provision and its intersection with other laws, we suggest keeping in mind how other laws align and interact with three main concepts set forth in the information blocking regulations: 1) "required by law," 2) the definition of "interference," and 3) information blocking "exceptions."

"Required by Law"

The information blocking definition provided in the Cures Act did not include practices that were likely to interfere with, prevent, or materially discourage access, exchange, or use of EHI when the practice was "required by law." ONC's regulatory definition of information blocking substantially mirrors the statute, and ONC currently interprets " required by law" to include federal and state law, and tribal laws, as applicable. More specifically, ONC interprets "required by law" to include federal and state statutes, regulations, court orders, and binding administrative decisions or settlements, such as (at the federal level) those from the Federal Trade Commission or the Equal Employment Opportunity Commission. To illustrate the relevancy of this interpretation, consider the following examples: it likely would not be information blocking if an actor abided by a state court order that restricted or prohibited disclosure of certain EHI in a closed adoption, or abided by a state law that prohibits disclosure of certain parts of an adolescent's EHI to the adolescent's parent.

"Interference"

An interference is any practice (an act or omission) that is likely to interfere with, materially discourage, or prevent access, exchange, or use of EHI. In contrast to practices required by law, if a state or federal law allows a particular interference to occur without violating that law, then an actor covered by the information blocking regulations could not solely rely on that law as justification for unnecessarily delaying or otherwise interfering with legally permissible access, exchange, or use of EHI. Interference in the form of delays that may be allowed but that are not required by other laws is not automatically exempted from implicating the information blocking definition ( see IB.FAQ26). Actors should examine their practices that may have been set up with an eye toward laws that allow particular periods of delay before information is shared (especially if those practices were established prior to April 5 th, 2021), as these once permitted practices may now interfere with EHI access, exchange, or use in a way that could constitute "information blocking" ( see also IB.FAQ22).

"Exceptions"

Information blocking "exceptions" are reasonable and necessary activities that do not constitute information blocking. There are eight regulatory exceptions for the information blocking definition. When it comes to other laws that may place preconditions on the permissibility of sharing EHI, or to situations that an actor believes call for the actor to do things that may constitute an interference, actors should particularly consider reviewing the Privacy and Preventing Harm Exceptions (45 CFR 171.202 and 45 CFR 171.201, respectively). For example, one aspect of the Privacy Exception focuses on fulfilling pre-conditions of federal and/or state law before providing access, exchange, or use of EHI as well as giving consideration to situations where an actor's operations are subject to multiple state privacy laws with inconsistent preconditions. Another aspect of the Privacy Exception focuses on situations where it may be reasonable and necessary to restrict access to a patient's EHI, such as honoring an individual's request to limit sharing of their information.

The Preventing Harm Exception, relying on similar types of harm as recognized by the HIPAA Privacy Rule for denying an individual's right of access to their own information, covers interference with a patient's access, exchange, or use of their own EHI in order to reduce a risk of physical harm to the patient or other(s). Still relying on similar types of harms identified by the HIPAA Privacy Rule, the Preventing Harm Exception also covers interference with access, exchange, or use of an individual's EHI by the patient's representative - like a pediatric patient's parent - in order to reduce a risk of substantial physical, psychological, or emotional harm to the patient or to another person ( see Information Blocking FAQs). We encourage actors to review the exception if they are concerned about some form of harm coming from the release of EHI in fulfilment of the individual's right of access under the HIPAA Privacy Rule.

Reader Feedback

We are always interested in feedback from the public, including "on-the-ground" implementation issues that actors are encountering. Are there any state or other laws that you think require an actor to deny access, exchange, or use of EHI; or require practices that may rise to the level of an interference depending on how the law is met by the actor? Feel free to let us know through the Health IT Feedback Portal.

Resources

For more resources and information associated with the Cures Act, the Cures Act Final Rule, and information blocking, please go to healthit.gov/informationblocking.

For more information on the HIPAA Privacy Rule, individual right of access, and grounds for denial of the individual right of access, please go to the Health Information Privacy section of HHS.gov.

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Information Sharing After the 21st Century Cures Act - Health IT Buzz

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When President Obama signed the bipartisan 21st Century Cures Act (Cures Act) into law in 2016, it marked a significant shift in health policy and health law. Not since the Health Insurance Portability and Accountability Act of 1996 (HIPAA) has there been a more noteworthy change in how electronic health information (EHI) is approached under United States federal law.

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Wes Barker and Christian Johnson | September 24, 2021

New ONC research shows a more than 20% rise in the number of apps that integrate with certified electronic health records (EHRs).

A manuscript highlighting the findings of this research was recently published in the Journal of the American Medical Informatics Association: "The Ecosystem of Apps and Software integrated with Certified Health Information Technology ". Our study analyzed apps that were discoverable in app galleries managed by EHR developers - Allscripts, athenahealth, Cerner Corporation and Epic Systems Corporation - as well as the SMART App Gallery. Specifically, ONC wanted to understand the growth of the app market, the variety of available app functions, how these apps connect to EHRs, and if the apps support the Health Level Seven® (HL7) Fast Healthcare Interoperability Resources® (FHIR) standard.

This research provides baseline measurement and insights into this market prior to finalization of the ONC Cures Act Final Rule and its implementation. This research continues, today, as one way to measure the impact of ONC rulemaking and understand changes in the market.

What the Data Says

Our research showed that the total number of unique apps and developers increased from 600 to 734 and 517 to 610, respectively, during 2020.

Administrative apps that handled scheduling, check-in, and billing made up 42% of available apps. Clinical apps were also common, comprising 38% of apps; these apps perform automated tasks, population health, telehealth, and clinical decision support. Other apps focused on care management (31%), patient engagement (20%), and research (5%).

Growth of FHIR-enabled apps remained stagnant. While more apps support FHIR (161 in 2020 compared to 112 in 2019), there was not a statistically significant increase in the proportion of FHIR-enabled apps among all apps (19% in 2019 compared to 22% in 2020).

Some potential explanations for this modest growth include:

Administrative apps, the most common type of app, typically do not support FHIR; at least not at the same rate as apps for clinical use or care management.

FHIR apps typically get developed around a set of specific use cases. FHIR resources are limited to the exchange of particular data elements, which may reduce their overall prevalence. As the number of use cases grow, so should the number of applications.

The role of secure, standards-based application programming interfaces (APIs) will be one area for researchers to explore further. Standards-based APIs may help apps connect to multiple EHR data systems without the added effort of using proprietary APIs for each integration. FHIR is widely supported by health IT developers, many of which are market leaders. Tracking the support of FHIR among apps over time will inform how apps and the EHR data systems to which they connect are using standards-based APIs to enable these integrations. If standardization makes integrations easier, more end-users can connect to their data.

Looking for Continued Growth

At ONC, we work with other government, industry, and academic partners to improve access and use of electronic health information. This includes looking at ways to support competition and the use of best-of-breed functions.

In this study, the data showed that there is still room for growth and variation in the apps available. Updates to the ONC Health IT Certification Program resulting from the ONC Cures Act Final Rule are intended to support a more robust ecosystem of third-party apps. By the end of 2022 many certified health IT developers will be required to support FHIR-based, standardized APIs for patient and population level services and they will need to abide by new API "Conditions and Maintenance of Certification" requirements to promote transparent and competitive business practices. Together these updates should help improve the use of APIs by third-party app developers. The published research examined the entire ecosystem of apps - both provider and consumer-facing - that integrated with EHRs. This ongoing study shall further examine consumer-facing apps, particularly those that enable patient medical record access, in future research.

Again, the goal of this study is to set a baseline. ONC will continue to monitor this growth in the coming years to see what impacts the rule will have on app development and integration with certified health IT, and if additional policies or practices could accelerate the exchange and access of electronic health information through third-party apps.

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Research Results Published: More Apps are Integrating with EHRs - Health IT Buzz

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New ONC research shows a more than 20% rise in the number of apps that integrate with certified electronic health records (EHRs). A manuscript highlighting the findings of this research was recently published in the Journal of the American Medical Informatics Association: "The Ecosystem of Apps and Software integrated with Certified Health Information Technology".

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Patient's Experience: An Example Provided by Cerner

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Patient's Experience: An Example Provided by Cerner

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A Patient's Journey: Meaningful Use Stage 3 and MIPS

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Abstract

In recent years, more and more health data are being generated. These data come not only from professional health systems, but also from wearable devices. All these 'big data' put together can be utilized to optimize treatments for each unique patient ('precision medicine'). For this to be possible, it is necessary that hospitals, academia and industry work together to bridge the 'valley of death' of translational medicine. However, hospitals and academia often are reluctant to share their data with other parties, even though the patient is actually the owner of his/her own health data. Academic hospitals usually invest a lot of time in setting up clinical trials and collecting data, and want to be the first ones to publish papers on this data. There are some publicly available datasets, but these are usually only shared after study (and publication) completion, which means a severe delay of months or even years before others can analyse the data. One solution is to incentivize the hospitals to share their data with (other) academic institutes and the industry. Here, we show an analysis of the current literature around data sharing, and we discuss five aspects of data sharing in the medical domain: publisher requirements, data ownership, growing support for data sharing, data sharing initiatives and how the use of federated data might be a solution. We also discuss some potential future developments around data sharing, such as medical crowdsourcing and data generalists.

Keywords: data sharing, data management, data science, big data, healthcare

1. Introduction

The past years have seen a steep rise in the amount of health data being generated. These data come not only from professional health systems (MRI scanners, pathology slides, DNA tests, etc.) but also from wearable devices. All these data combined form 'big data' that can be utilized to optimize treatments for each unique patient ('precision medicine') [ 1]. To achieve this precision medicine, it is necessary that hospitals, academia and industry work together to bridge the 'valley of death' of translational medicine [ 2]. However, hospitals and academia often have problems with sharing their data, even though the patient is actually the owner of his/her own health data, and data sharing is associated with increased citation rate [ 3, 4]. Academic hospitals usually want to be the first ones to publish papers on the data, because they spent a lot of time in setting up clinical trials and collecting the data. Society benefits the most if the patient's data are shared as soon as possible so that other researchers can work with it [ 5], but this idea has not settled in yet. Some datasets are publicly available (e.g., in prostate cancer [ 6]), but these are usually only shared after studies are finished and/or publications have been written based on the data, which means a severe delay of months or even years before others can use the data for analysis. One solution is to incentivize the hospitals [ 7, 8] to share their data with (other) academic institutes and the industry. Besides this academic reluctance, data is also being shared less because of stricter privacy laws such as the EU General Data Protection Regulation (GDPR) [ 9] and the California Consumer Privacy Act (CCPA) [ 10]. At the moment, only around 10% of the world's population has it personal information covered by the GDPR or similar laws, but Gartner Research predicts that this will be around 50% by 2022 [ 11]. There is an increasingly urgent need to balance the opportunity big data provides for improving healthcare, against the right of individuals to control their own data [ 1]. Scientists should maximize their efforts to improve healthcare, but they should also only use data with appropriate informed consent. This open science vs. privacy balance will remain an increasing challenge for the coming years.

The topic of data sharing has received more attention in recent years. In 1980, only 46 articles (0.0186% of the total) published in PubMed contained the keyword "data sharing", while in 2019 there were 5960 articles (0.4253% of the total) containing this keyword ( Figure 1 ). It is also interesting to see the sudden rise of interest in the subject since 2016, the year of the approval of the GDPR, and another peak in 2018, the year of its enforcement.

If we use PubMed to find terms related to "data sharing", there are some interesting observations ( Figure 2 ). Mostly used are obviously terms such as "patients", "health", "study" and "information", but closely behind these are "use" (or "used"/"using"), "treatment", "care" "analysis" and "rights". "Use" might point to the fact that data collection and sharing is closely connected to the usage of the data, i.e., in the consent form it should be mentioned in detail what the health data will be used for. "Treatment", "care" and "analysis" point to one of the main uses of the data: analysis in order to improve treatment and care, for example in clinical decision support (CDS) systems. "Rights" is probably related to the patients' privacy rights when it comes to data sharing, an issue that is discussed in detail in this manuscript.

There have been some studies on the conditions and challenges for sharing data. For example, for the BigData@Heart platform of the Innovative Medicines Initiative (IMI), a descriptive case study into the condition for data sharing was carried out [ 12]. Principle investigators of the participating databases were requested to send any kind of documentation that possibly specified the conditions for data sharing, which were then qualitatively reviewed for conditions related to data sharing and data access. This review revealed overlap on the conditions: (1) only to share health data for scientific research, (2) in anonymized/coded form, (3) after approval from a designated review committee, and while (4) observing all appropriate measures for data security and in compliance with the applicable laws and regulations. These challenges give thought to the design of an ethical governance framework for data sharing platforms. The conclusion of the case study was that current data sharing initiatives should concentrate on: (1) the scope of the research questions that may be addressed, (2) how to deal with varying levels of de-identification, (3) determining when and how review committees should come into play, (4) align what policies and regulations mean by "data sharing" and (5) how to deal with datasets that have no system in place for data sharing.

Sharing data should not just be a one-way street from the clinician to the researcher; ideally the clinician, the researcher and the patient (or patient organization) would work together on setting up the study, so that there is an agreement on data usage upfront, and expectations are managed. Sharing data will also increase confidence and trust in the conclusions drawn from clinical trials [ 14]. It will help to enable the independent confirmation of results (reproducibility), an essential part of the scientific process. It will foster the development and testing of new hypotheses. Sharing clinical trial data should also make progress more efficient by making the most of what may be learned from each trial and by avoiding unwarranted repetition. It will help to satisfy the moral obligation of researchers towards study participants, and it will benefit patients, investigators, sponsors, and society. In this review, we discuss several aspects of data sharing in the medical domain. The Section 2 is about publisher requirements, which shows what guidelines have been created by publishers and editors to promote the sharing of data. Since academics rely on publication of their data, these are important measures and a logical first topic to be discussed. The Section 3 shows that there is an ongoing discussion about data ownership, which influences the way that regulations are being implemented. The Section 4 shows the growing support for data sharing, making the link to open science and the reproducibility of results. The Section 5 shows data sharing initiatives that have been undertaken recently. The Section 6 and Section 7 discusses how the use of federated data might be a solution of the privacy and reproducibility issues mentioned in the Section 2, Section 3 and Section 4.

2. Publisher Requirements

Most publishers strongly recommend sharing research data. For this section, the publisher requirements of five major publishers are discussed, as well as the most widely used sets of guidelines from publishers and editors.

Nature states that data sharing makes new types of research possible [ 15], for example through the pooling of patient cohorts, and hints to future developments: sharing data is not only a way to improve the reproducibility and robustness of the science that is taking place today, but can drive new science for tomorrow. By browsing through existing datasets, new hypotheses can be formed, which can then be tested in new studies. Because nobody can predict how valuable a dataset will be in the future, data should be made available to future scientists whenever possible. The Science journals support the efforts of databases that aggregate published data for the use of the scientific community [ 16]. Therefore, before publication, large data sets must be deposited in an approved database and an accession number or a specific access address must be included in the published paper. The Science journals also encourage compliance with Minimum Information for Biological and Biomedical Investigations (MIBBI) guidelines [ 17]. British Medical Journal (BMJ) journals have three different data sharing policies ("tiers"), dependent of the journal [ 18]. They encourage researchers to make available as much of the underlying data from an article as possible (without compromising the privacy of the patients). The BMJ journals also consider reproducibility: all data that are needed to reproduce the results presented in the associated article should be made available. When submitting a manuscript to a publisher such as BioMed Central (BMC), the researcher even "agrees to make the raw data and materials described in your manuscript freely available to any scientist wishing to use them for non-commercial purposes, as long as this does not breach participant confidentiality" [ 19]. Public Library of Science (PLOS) journals require authors "to make all data necessary to replicate their study's findings publicly available without restriction at the time of publication. When specific legal or ethical restrictions prohibit public sharing of a data set, authors must indicate how others may obtain access to the data" [ 20]. Other publishers have similar guidelines in place, promoting data sharing on a global level.

In 2015, the Transparency and Openness Promotion (TOP) guidelines [ 21] were published. The guidelines were developed to translate scientific norms and values into concrete actions and change the current incentive structures to drive researchers' behavior toward more openness. The TOP guidelines have eight standards: (1) citation standards; (2) data transparency; (3) analytics methods (code) transparency; (4) research materials transparency; (5) design and analysis transparency; (6) preregistration of studies; (7) preregistration of analysis plans; and (8) replication. For each standard, there are three levels with increasing stringency. Currently, over 1000 scientific journals have implemented the TOP guidelines [ 22].

The International Committee of Medical Journal Editors (ICMJE) also recommends the sharing of data [ 14]. In 2016, they proposed to require authors to share with others the deidentified individual-patient data (IPD) underlying the results presented in the article no later than 6 months after publication. The data underlying the results are defined as "the IPD required to reproduce the article's findings, including necessary metadata". Since 2019, the ICMJE requires investigators to register a data-sharing plan when registering a trial as well. This plan must include where the researchers will house the data and, if not in a public repository, the mechanism by which they will provide others access to the data, whether data will be freely available to anyone upon request or only after application to and approval by a learned intermediary, whether a data use agreement will be required, etc. Declaring the plan for sharing data prior to their collection will further enhance transparency in the conduct and reporting of clinical trials by exposing when data availability following trial completion differs from prior commitments. However, ICMJE also stresses that the rights of investigators and trial sponsors must be protected. To achieve this, the following four rules apply: (1) editors will not consider the deposition of data in a registry to constitute prior publication; (2) authors of secondary analyses using these shared data must attest that their use was in accordance with the terms (if any) agreed to upon their receipt; (3) authors of secondary analyses must reference the source of the data using a unique identifier of a clinical trial's data set to provide appropriate credit to those who generated it and allow searching for the studies it has supported; (4) authors of secondary analyses must explain completely how theirs differ from previous analyses. In addition, those who generate and then share clinical trial data sets deserve substantial credit for their efforts. Those using data collected by others should seek collaboration with those who collected the data.

By providing the guidelines and rules set out above, the publishers and editors contribute to the acceptance of data sharing by researchers. Not only does it help solve their problem of a lack of reproducibility of the scientific results published in their journals, increasing confidence and trust in these results; it will also help the scientists in the generation of new hypotheses, and avoiding unnecessary repetition. In the end, publishers, as well as scientists, patients and societies will benefit from complying with these rules.

3. Data Ownership

When discussing the sharing of data, it is important to realize that there is not much consensus on who is actually the owner of that data. This section briefly discusses this issue of data ownership in the light of recent privacy laws. These laws have a very large impact on the topic of data sharing.

Institutions tend to believe that they own the patient data, since they collected it. However, these institutions are in fact just "data custodians"; the data is the property of the patient and the access and use of that data outside of the clinical institute usually requires patient consent [ 1]. This limits the exploitation of the "big data" that are available in the clinical records, because the data should be destroyed (or sufficiently anonymized) after the end of the study. Big data techniques such as machine learning and deep learning use thousands to millions of data points, which may have required considerable processing. It would be a waste to lose such valuable data at the end of the project. Therefore, it is advised to ask the patient for consent to store and use their data for future scientific research. Although it is not possible to use the data from a large number of retrospective datasets in this manner, this will make sure that at least the prospectively collected data can be used in future studies. The dilemma of the use of patient data versus privacy rights has gotten much attention because of the implementation of the GDPR in 2018 (as well as the CCPA in 2020), initiating an international debate on the sharing of big data in the healthcare domain [ 23]. Earlier laws such as the Health Insurance Portability and Accountability Act (HIPAA) Privacy Rule [ 24] of the USA and the Personal Information Protection and Electronic Documents Act (PIPEDA) [ 25] of Canada already gave more rights to patients regarding their data, but the GDPR and CCPA have taken it to another level. However, GDPR and similar laws do not say much about data ownership. The GDPR's main entities are the data controller and the data processor [ 9]. "Data controller" means the natural or legal person, public authority, agency or other body which, alone or jointly with others, determines the purposes and means of the processing of personal data. "Data processor" means a natural or legal person, public authority, agency or other body which processes personal data on behalf of the controller. In countries outside of the European Union, where GDPR does not apply, there is also not much agreement on data ownership, making it even more justifiable to always ask for the consent of the patient.

4. Growing Support for Data Sharing

The idea that data should be shared as much as possible to enable scientific progress is gaining momentum, mostly because of the power of big data analyses, machine learning, deep learning, etc. In this section, some developments are discussed which show this growing support for data sharing. Some of them were already known to the author, whereas others were a result from the literature analysis mentioned in the introduction.

Science in Transition [ 26] claims that "science has become a self-referential system where quality is measured mostly in bibliometric parameters and where societal relevance is undervalued", emphasizing that researchers tend to care mostly about publications instead of using the data to solve real-life problems. It also gives attention to the reproducibility problem in science: more than 70% of researchers have tried and failed to reproduce another scientist's experiments, and more than half have even failed to reproduce their own experiments [ 27]. This problem is not only caused by a lack of data sharing, but also because researchers do not share methodologies used to combine and analyse datasets. In many projects, data from several sources (possibly collected using different protocols and standards) need to be combined before the data analysis can take place. If these methodologies, as well as the analysis scripts, are not shared, results cannot be reproduced even if the data is available. This reproducibility issue could be resolved by 'Open Science', which is defined as the practising of science in a sustainable manner which gives others the opportunity to work with, contribute to and make use of the scientific process. This allows users from outside science to influence the research world with questions and ideas and help gather research data [ 28]. The Open Science movement stimulates not only open access to data, but also open access publishing, open source scientific software and open educational resources [ 29].

The Mayo Clinic Platform [ 30] is a new cloud-based clinical data analytics platform, storing de-identified patient data, which providers, payers and pharmaceutical companies outside of Mayo can link up to via application programming interfaces (APIs), as well as establishing standard templates for compliance and legal agreements. The first partner of the Mayo Clinic Platform is Nference, a software startup that Mayo is an investor in. Nference develops analytics, machine learning and natural language processing tools that "augment" the work of data scientists, in order to help research organizations and pharmaceutical companies conduct "research at scale". Mayo Clinic hopes to work with pharma to commercialize new therapies. Mayo itself wouldn't commercialize those therapies, though the system could receive royalties from insights generated on the platform. These royalties would be re-invested into Mayo's clinical practice, research and education work.

Healthcare Business and Technology wrote about how data sharing could change the entire healthcare industry [ 31]. It discusses the partnership announced by Apple in 2018 with 13 major healthcare systems, including Johns Hopkins and the University of Pennsylvania, that will allow Apple to download patients' electronic health data onto its devices (with consent of the patients). This type of data sharing could transform the U.S. healthcare industry by empowering patients in new ways and improving care. It could even reduce organizational costs by streamlining care processes, because hospital staff would need to spend less time on making data available to patients. And artificial intelligence (AI) could use the patient data to answer patients' questions and direct them to the healthcare services they need.

The 'Ten Commandments of Translational Research Informatics' [ 32] are some guidelines related to data management and data integration in translational research projects. Some of the commandments relate to the sharing of data: clear arrangements about data access need to be made (commandment 4), agree about de-identification and anonymization (commandment 5), the FAIR guiding principles [ 33] should be adhered to (commandment 8), and researchers should think about what will happen to the data after the project (commandment 10): e.g., research can be shared in a public repository.

5. Data Sharing Initiatives

There are many initiatives around the world supporting the sharing of medical data, leading the way to open science while still respecting the privacy rights of the patients. This paragraph gives some recent examples of these initiatives, resulting from the literature analysis from the introduction.

GIFT-Cloud [ 34] is a platform for data sharing and collaboration in medical imaging research. The goal of GIFT-Cloud is to provide a flexible, clinician- and researcher-friendly system for anonymising and sharing data across multiple institutions. It was built to support the Guided Instrumentation for Fetal Therapy and Surgery (GIFT-Surg) project, an international research collaboration that is developing novel imaging methods for fetal surgery, but it also has general applicability to other areas of imaging research. It simplifies the transfer of imaging data from clinical to research institutions, facilitating the development and validation of medical research software and the sharing of results back to the clinical partners. GIFT-Cloud supports collaboration between multiple healthcare and research institutions while satisfying the demands of patient confidentiality, data security and data ownership. It achieves this by building upon existing, well-established cross-platform technologies. GIFT-Cloud stores data from each institution in a separate data group and access to these groups can be individually configured for each account, corresponding with what is arranged in the data sharing agreements.

Another development in data sharing is the Personalized Consent Flow [ 35]: a new consent model that allows people to control their personally collected health data and determine to what extent they want to share these for research purposes. Three main features characterize the consent flow: (1) Users are asked general questions about sharing data. When they wish to share data for scientific research, they may opt for "narrow" consent (treating each study separately) or "broad" consent (for multiple studies). Furthermore, users can decide which data will be shared for specific studies and with whom. (2) Users can choose to share existing data that they have collected passively, to share prospectively, collect data, or both. For prospective studies, researchers can invite specific users to collect selected data during a specific time period. Users can also be notified about future studies by signing up for the research program. (3) Expiration dates are connected to each consent choice, which ensures that a user reconsiders his decision. A default expiration date of one year will be assigned, but users may also select personal expiration dates, such as an expiration date connected to the duration of the study. Users can choose to quit sharing data at any time, as required by GDPR regulations. During all steps, users are informed about implications of consent options.

In the United States, the Sync for Science (S4S) [ 36] collaboration between Electronic Health Record (EHR) vendors, the National Institutes of Health (NIH), the Office of the National Coordinator for Health IT (ONC), and Harvard Medical School's Department of Biomedical Informatics was started in 2016. S4S allows individuals to access their health data and share these data with researchers to support studies that generate insights into human health and disease [ 37]. Different EHRs collect and store health data differently, so S4S has focused on promoting both authorization and healthcare data standards to make it possible for EHR systems to release, upon patient approval, high quality data that researchers can readily consume. The All of Us Research Program [ 38] was the first study to adopt S4S technology in a pilot program. The program began national enrollment in 2018 and is expected to last at least 10 years. An initiative like Sync for Science gives the power to the patient: the patient can decide what information to share with researchers, and under what conditions. Much like many countries now have organ donation registration systems in place, this 'data donation registration' might be something that will be implemented around the world in the near future.

In the EU, the 1+ Million Genomes Initiative [ 39] is a good example of how many datasets could be combined into one large database, enabling the study of, e.g., rare diseases. The declaration aims to bring together fragmented infrastructure and expertise supporting a shared and tangible goal: one million genomes accessible in the EU by 2022. The 22 participating European countries envision that the digital transformation of genomic medicine (and healthcare in general) will help health systems to meet the challenges they face and become more sustainable, thereby improving the provision of high-quality health services and the effectiveness of treatments. They believe that this requires a concerted effort to overcome data silos, lack of interoperability and fragmentation of initiatives across the EU. Another recent example of such a large-scale collaborative data sharing effort is the Pan-Cancer Analysis of Whole Genomes (PCAWG) [ 40], which was facilitated by international data sharing using compute clouds. PCAWG contains 2658 whole-cancer genomes and their matching normal tissues across 38 tumour types. More than 1300 scientists and clinicians from 37 countries were involved in the project.

Data sharing goes beyond the academic world. Many public-private partnerships have been set up, in order to make sure that discoveries are not only published, but also applied in a product such as a medical device, a medicine or a computer program. Funding programmes, such as the Horizon 2020 Research and Innovation Programme of the European Union, very much stimulate data sharing with companies. In May 2016, it was announced that Deepmind, a company owned by Google and most famous for its innovative use of AI, was given access to the healthcare data of up to 1.6 million patients from three hospitals run by a major London NHS trust [ 41]. And in January 2018, Apple announced that they created a pact with 13 prominent health systems, including prestigious centers like Johns Hopkins and the University of Pennsylvania, allowing Apple to download the electronic health data of patients onto its devices, with consent [ 42]. Of course, when sharing data with commercial parties, privacy needs to be taken into account. For example, if GDPR applies, the patient needs to 'opt-in' for sharing their data for commercial use. Besides industry using data generated by academia, the opposite is also possible; these collaborations are called "data collaboratives" [ 43]. Data collaboratives are a new form of partnership in which privately held data are made accessible for analysis and use by external parties working in the public interest. By having researchers from both industry and academia work on the data, new insights and innovations can be created, and the potential of privately-owned data can be unlocked.

6. Federated Data

Data federation is a recent development in medical science, which is a possible solution for the data sharing vs. patient privacy dilemma. In this section, three examples of federated data systems for sharing medical data are discussed, resulting from the literature analysis from the introduction.

When applying machine learning methods on healthcare data, large samples sizes are required. Often these sizes can only be achieved by combining data from several studies. But this kind of pooling of information is difficult because of patient privacy and data protection needs. Privacy preserving distributed learning technology has the potential to overcome these limitations. The general idea behind distributed learning is that sites share a statistical model and its parameters, instead of sharing sensitive data. Each site runs computations on a local data store that generate these aggregated statistics. In this setting, organizations can collaborate by exchanging aggregated data/statistics while keeping the underlying data safely on site and undisclosed. VANTAGE6 [ 44] provides a way to use distributed learning technology, using open source software. It is one of the federated data systems that has recently become available in order to share data while preserving the patients' privacy rights.

The Personal Health Train (PHT) [ 45, 46] is another example of a federated data system; it aims to connect distributed health data and create value by increasing the use of existing health data for citizens, healthcare, and scientific research. The key concept in the Personal Health Train is to bring algorithms ('trains') to the data where they happen to be ('stations'), rather than bringing all data together in a central database. The Personal Health Train is designed to give controlled access to heterogeneous data sources, while ensuring privacy protection and maximum engagement of individual subjects. As a prerequisite, health data are made FAIR (Findable, Accessible, Interoperable and Reusable) [ 33]. Stations containing FAIR data may be controlled by individuals, (general) physicians, biobanks, hospitals and public or private data repositories. The Personal Health Train was applied recently to a project with 20,000+ lung cancer patients [ 47] and will also be used in the Coronary ARtery disease: Risk estimations and Interventions for prevention and EaRly detection (CARRIER) project [ 48]. Likely more projects will follow.

Another example of a federated data system is DataSHIELD [ 49]. It provides a novel technological solution that can circumvent some of the most basic challenges in facilitating the access of researchers and other healthcare professionals to individual-level data. It facilitates research in settings where sharing the data itself is not possible (due to government restrictions, intellectual property issues, or data size). Commands are sent from a central analysis computer (AC) to several data computers (DCs) storing the data to be co-analysed. The data sets are analysed simultaneously but in parallel. The separate parallelized analyses are linked by non-disclosive summary statistics and commands transmitted back and forth between the DCs and the AC. DataSHIELD has been used by the Healthy Obese Project and the Environmental Core Project of the Biobank Standardisation and Harmonisation for Research Excellence in the European Union (BioSHaRE-EU [ 50]) for the federated analysis of 10 datasets across eight European countries.

7. Conclusions

This review discusses the current state of data sharing in healthcare. It shows that data sharing is widely supported by governments, funding programs and publishers, but that there are also issues. Clinicians or researchers might be reluctant to share data, because of publication pressure and fear for competition. This might be solved by the "open science" initiatives mentioned in this paper, which need to be supported by governments as well as the scientific communities itself in order to make it a success. Next to this, there are also patient-related issues such as stricter privacy laws. A possible (technical) solution here is the use of federated data systems such as the Personal Health Train, which enable algorithms to reach out the data without having the need to bring all data together. The challenges around privacy might also be solved by non-technical means such as using standardized consent forms to enable future use of data for research and/or commercial purposes. For the (near) future, there are some more developments that might be influential on the acceptance of open science and the sharing of data. One of these developments is medical crowdsourcing [ 51], which offers hope to patients who suffer from complex health conditions or rare diseases that are difficult to diagnose. Medical crowdsourcing platforms empower patients to use the "wisdom of the crowd" by providing access to a large pool of diverse medical information. One example medical crowdsourcing platform is CrowdMed [ 52]. This platform was appreciated by some patients with undiagnosed illnesses, because they received helpful guidance from crowdsourcing their diagnoses during their difficult diagnostic journeys [ 53]. Greater participation in crowdsourcing increases the likelihood of encountering a correct solution, and this might help to encourage patients to share their data. However, more participation can also lead to more noise, making the identification of the most likely solution from a broader pool of recommendations difficult. The challenge for medical crowdsourcing platforms is to increase participation of both patients and solution providers, while simultaneously increasing the efficacy and accuracy of solutions. Moreover, caution should be taken when giving people without a medical background the power to diagnose others. Another future development is the increase in "data generalists"; experts that focus entirely on data sharing and communication [ 54]. A data generalist takes on all responsibility for the sharing of data and needs critical thinking skills to integrate, evaluate and communicate the benefits and drawbacks of providing open data. They also have a role in data analysis. The emergence of this role should encourage better sharing of data. In a time where much attention is going to the data scientist, it could be the data generalist that really has the job of the future.

Funding

This research received no external funding.

Conflicts of Interest

Tim Hulsen is employed by Philips Research.

References

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Articles from International Journal of Environmental Research and Public Health are provided here courtesy of Multidisciplinary Digital Publishing Institute (MDPI)

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Sharing Is Caring-Data Sharing Initiatives in Healthcare

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In recent years, more and more health data are being generated. These data come not only from professional health systems, but also from wearable devices. All these 'big data' put together can be utilized to optimize treatments for each ...

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What happens when information cannot be easily exchanged? 

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What happens when information cannot be easily exchanged? 

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Why, in the midst of a U.S. health crisis, there are major challenges for doctors to access patient records

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Medical record interoperability in the U.S. is far from a solved problem, doctors say. It remains a challenge for hospitals and clinics to exchange patient information across the country.

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Office of the National Coordinator for Health IT

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Health IT for You: Giving You Access to Your Medical Records When and Where They're Needed (60 sec)

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Physicians often struggle to help patients change their health behaviors. Patients may know that they need to quit smoking, lose weight, or exercise more, but summoning the will to change is hard. But combining behavioral economics and "gamification" - putting game elements such as points and achievement levels into non-game contexts - holds promise for driving behavior change. But to make this happen, gamified health programs must leverage behavioral science. For example, personalized goal setting, commitment pledges, "fresh start" moments when people fall off the wagon, and social incentives can boost participation and outcomes over the long-term. And while incorporating principles from behavioral economics is not hard or expensive, it does require attention to detail. Small changes can have a big impact.

Physicians often struggle to help patients change their health behaviors. Patients may know that they need to quit smoking, lose weight, or exercise more, but summoning the will to change is hard. It's particularly difficult for the highest-risk patients who may have life circumstances - challenges such as unemployment or homelessness - that make it harder for them to focus on the long-term. But combining behavioral economics and "gamification" - putting game elements such as points and achievement levels into non-game contexts - holds promise for driving behavior change when a doctor's advice, and patient's good intentions, are not enough.

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Physicians often struggle to help patients change their health behaviors. Patients may know that they need to quit smoking, lose weight, or exercise more, but summoning the will to change is hard. But combining behavioral economics and "gamification" - putting game elements such as points and achievement levels into non-game contexts - holds promise for driving behavior change.

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Gamification: The Future of ADHD Treatment | Vincent Hennemand, Akili Interactive

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Jack Miner: The COVID-19 Telehealth Accelerator

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Jack Miner addresses the acceleration of telehealth due to the COVID-19 pandemic and how it can help the future of healthcare.

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Defining Telehealth, Telemedicine, and Telecare

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Defining Telehealth, Telemedicine, and Telecare

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When thinking about healthcare, most of us conjure up images of office visits or trips to the ER. Whether it's for a routine check-up, lab tests, an outpatient procedure or major surgery, the norm is for patients and caregivers to leave their homes (often sitting in traffic or rushing from work) to meet their doctor at a healthcare facility of some kind. But things are changing.

Based on advances in information and communications technologies, medical professionals as well as other "health and care" providers can now offer increasingly robust, remote (from their location to another), interactive (two-way) services to consumers, patients and caregivers.

The terms used to describe these broadband-enabled interactions include telehealth, telemedicine and telecare. "Telehealth" evolved from the word "telemedicine." "Telecare" is a similar term that you generally hear in Europe. All three of these words are often - but not always - used interchangeably. They can also have different meanings depending on who you ask. And that's precisely why you should ask your doctor, your insurance provider, your nurse, anyone who's part of your health and care universe ...

what do you mean when you say ...

Telemedicine? - Telemedicine can be defined as using telecommunications technologies to support the delivery of all kinds of medical, diagnostic and treatment-related services usually by doctors. For example, this includes conducting diagnostic tests, closely monitoring a patient's progress after treatment or therapy and facilitating access to specialists that are not located in the same place as the patient.

Telehealth? - Telehealth is similar to telemedicine but includes a wider variety of remote healthcare services beyond the doctor-patient relationship. It often involves services provided by nurses, pharmacists or social workers, for example, who help with patient health education, social support and medication adherence, and troubleshooting health issues for patients and their caregivers.

Telecare? - Telecare generally refers to technology that allows consumers to stay safe and independent in their own homes. For example, telecare may include consumer-oriented health and fitness apps, sensors and tools that connect consumers with family members or other caregivers, exercise tracking tools, digital medication reminder systems or early warning and detection technologies.

For More Information. The Connect2HealthFCC Task Force is working to raise consumer awareness about the value of broadband in the health and care sectors. Learn about the FCC's Connect2Health Task Force and its work on consumer health issues at www.fcc.gov/health. For information about other communications issues, visit the FCC's Consumer website at www.fcc.gov/consumers.

Download a printable version of this document. (English) (Spanish)

For this or any other consumer publication in an accessible format (electronic ASCII text, Braille, large print or audio), write or call us at the address or phone number at the bottom of this page, or send an email to FCC504@fcc.gov

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Telehealth, Telemedicine and Telecare: What's What?

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When thinking about healthcare, most of us conjure up images of office visits or trips to the ER. Whether it's for a routine check-up, lab tests, an outpatient procedure or major surgery, the norm is for patients and caregivers to leave their homes (often sitting in traffic or rushing from work) to meet their doctor at a healthcare facility of some kind.

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HIPAA Training 101: What is HIPAA Compliance?

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https://www.youtube.com/watch?v=nIleuj43Jqk

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HOW TO IMPROVE YOUR SKILLS IN STORYTELLING WITH DATA WITH COLE NUSSBAUMER KNAFLIC

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https://www.youtube.com/watch?v=Iwpi1Lm6dFo

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How to avoid death By PowerPoint | David JP Phillips | TEDxStockholmSalon

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Harvard Business Review - Smart Data Visualizations

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Harvard Business Review - Smart Data Visualizations

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https://hbr.org/2016/06/visualizations-that-really-work

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Idea in Brief

Context

Knowledge workers need greater visual literacy than they used to, because so much data-and so many ideas-are now presented graphically. But few of us have been taught data-visualization skills.

Tools Are Fine...

Inexpensive tools allow anyone to perform simple tasks such as importing spreadsheet data into a bar chart. But that means it's easy to create terrible charts. Visualization can be so much more: It's an agile, powerful way to explore ideas and communicate information.

...But Strategy Is Key

Don't jump straight to execution. Instead, first think about what you're representing-ideas or data? Then consider your purpose: Do you want to inform, persuade, or explore? The answers will suggest what tools and resources you need.

Not long ago, the ability to create smart data visualizations, or dataviz, was a nice-to-have skill. For the most part, it benefited design- and data-minded managers who made a deliberate decision to invest in acquiring it. That's changed. Now visual communication is a must-have skill for all managers, because more and more often, it's the only way to make sense of the work they do.

Data is the primary force behind this shift. Decision making increasingly relies on data, which comes at us with such overwhelming velocity, and in such volume, that we can't comprehend it without some layer of abstraction, such as a visual one. A typical example: At Boeing the managers of the Osprey program need to improve the efficiency of the aircraft's takeoffs and landings. But each time the Osprey gets off the ground or touches back down, its sensors create a terabyte of data. Ten takeoffs and landings produce as much data as is held in the Library of Congress. Without visualization, detecting the inefficiencies hidden in the patterns and anomalies of that data would be an impossible slog.

But even information that's not statistical demands visual expression. Complex systems-business process workflows, for example, or the way customers move through a store-are hard to understand, much less fix, if you can't first see them.

Thanks to the internet and a growing number of affordable tools, translating information into visuals is now easy (and cheap) for everyone, regardless of data skills or design skills. This is largely a positive development. One drawback, though, is that it reinforces the impulse to "click and viz" without first thinking about your purpose and goals. Convenient is a tempting replacement for good, but it will lead to charts that are merely adequate or, worse, ineffective. Automatically converting spreadsheet cells into a chart only visualizes pieces of a spreadsheet; it doesn't capture an idea. As the presentation expert Nancy Duarte puts it, "Don't project the idea that you're showing a chart. Project the idea that you're showing a reflection of human activity, of things people did to make a line go up and down. It's not 'Here are our Q3 financial results,' it's 'Here's where we missed our targets.'"

Managers who want to get better at making charts often start by learning rules. When should I use a bar chart? How many colors are too many? Where should the key go? Do I have to start my y-axis at zero? Visual grammar is important and useful-but knowing it doesn't guarantee that you'll make good charts. To start with chart-making rules is to forgo strategy for execution; it's to pack for a trip without knowing where you're going.

Your visual communication will prove far more successful if you begin by acknowledging that it is not a lone action but, rather, several activities, each of which requires distinct types of planning, resources, and skills. The typology I offer here was created as a reaction to my making the very mistake I just described: The book from which this article is adapted started out as something like a rule book. But after exploring the history of visualization, the exciting state of visualization research, and smart ideas from experts and pioneers, I reconsidered the project. We didn't need another rule book; we needed a way to think about the increasingly crucial discipline of visual communication as a whole.

The typology described in this article is simple. By answering just two questions, you can set yourself up to succeed.

The Two Questions

To start thinking visually, consider the nature and purpose of your visualization:

Is the information conceptual or data-driven?

Am I declaring something or exploring something?

If you know the answers to these questions, you can plan what resources and tools you'll need and begin to discern what type of visualization will help you achieve your goals most effectively.

The first question is the simpler of the two, and the answer is usually obvious. Either you're visualizing qualitative information or you're plotting quantitative information: ideas or statistics. But notice that the question is about the information itself, not the forms you might ultimately use to show it. For example, the classic Gartner Hype Cycle uses a traditionally data-driven form-a line chart-but no actual data. It's a concept.

If the first question identifies what you have, the second elicits what you're doing: either communicating information (declarative) or trying to figure something out (exploratory).

Managers most often work with declarative visualizations, which make a statement, usually to an audience in a formal setting. If you have a spreadsheet workbook full of sales data and you're using it to show quarterly sales in a presentation, your purpose is declarative.

But let's say your boss wants to understand why the sales team's performance has lagged lately. You suspect that seasonal cycles have caused the dip, but you're not sure. Now your purpose is exploratory, and you'll use the same data to create visuals that will confirm or refute your hypothesis. The audience is usually yourself or a small team. If your hypothesis is confirmed, you may well show your boss a declarative visualization, saying, "Here's what's happening to sales."

Exploratory visualizations are actually of two kinds. In the example above, you were testing a hypothesis. But suppose you don't have an idea about why performance is lagging-you don't know what you're looking for. You want to mine your workbook to see what patterns, trends, and anomalies emerge. What will you see, for example, when you measure sales performance in relation to the size of the region a salesperson manages? What happens if you compare seasonal trends in various geographies? How does weather affect sales? Such data brainstorming can deliver fresh insights. Big strategic questions-Why are revenues falling? Where can we find efficiencies? How do customers interact with us?-can benefit from a discovery-focused exploratory visualization.

The Four Types

The nature and purpose questions combine in a classic 2×2 to define four types of visual communication: idea illustration, idea generation, visual discovery, and everyday dataviz.

Idea Illustration. We might call this quadrant the "consultants' corner." Consultants can't resist process diagrams, cycle diagrams, and the like. At their best, idea illustrations clarify complex ideas by drawing on our ability to understand metaphors (trees, bridges) and simple design conventions (circles, hierarchies). Org charts and decision trees are classic examples of idea illustration. So is the 2×2 that frames this article.

Idea illustration demands clear and simple design, but its reliance on metaphor invites unnecessary adornment. Because the discipline and boundaries of data sets aren't built in to idea illustration, they must be imposed. The focus should be on clear communication, structure, and the logic of the ideas. The most useful skills here are similar to what a text editor brings to a manuscript-the ability to pare things down to their essence. Some design skills will be useful too, whether they're your own or hired.

Suppose a company engages consultants to help its R&D group find inspiration in other industries. The consultants use a technique called the pyramid search -a way to get information from experts in other fields close to your own, who point you to the top experts in their fields, who point you to experts in still other fields, who then help you find the experts in those fields, and so on.

It's actually tricky to explain, so the consultants may use visualization to help. How does a pyramid search work? It looks something like this:

The axes use conventions that we can grasp immediately: industries plotted near to far and expertise mapped low to high. The pyramid shape itself shows the relative rarity of top experts compared with lower-level ones. Words in the title-"climbing" and "pyramids"-help us grasp the idea quickly. Finally, the designer didn't succumb to a temptation to decorate: The pyramids aren't literal, three-dimensional, sandstone-colored objects.

Too often, idea illustration doesn't go that well, and you end up with something like this:

Here the color gradient, the drop shadows, and the 3-D pyramids distract us from the idea. The arrows don't actually demonstrate how a pyramid search works. And experts and top experts are placed on the same plane instead of at different heights to convey relative status.

Idea Generation. Managers may not think of visualization as a tool to support idea generation, but they use it to brainstorm all the time-on whiteboards, on butcher paper, or, classically, on the back of a napkin. Like idea illustration, idea generation relies on conceptual metaphors, but it takes place in more-informal settings, such as off-sites, strategy sessions, and early-phase innovation projects. It's used to find new ways of seeing how the business works and to answer complex managerial challenges: restructuring an organization, coming up with a new business process, codifying a system for making decisions.

Although idea generation can be done alone, it benefits from collaboration and borrows from design thinking-gathering as many diverse points of view and visual approaches as possible before homing in on one and refining it. Jon Kolko, the founder and director of the Austin Center for Design and the author of Well-Designed: How to Use Empathy to Create Products People Love, fills the whiteboard walls of his office with conceptual, exploratory visualizations. "It's our go-to method for thinking through complexity," he says. "Sketching is this effort to work through ambiguity and muddiness and come to crispness." Managers who are good at leading teams, facilitating brainstorming sessions, and encouraging and then capturing creative thinking will do well in this quadrant. Design skills and editing are less important here, and sometimes counterproductive. When you're seeking breakthroughs, editing is the opposite of what you need, and you should think in rapid sketches; refined designs will just slow you down.

Suppose a marketing team is holding an off-site. The team members need to come up with a way to show executives their proposed strategy for going upmarket. An hourlong whiteboard session yields several approaches and ideas (none of which are erased) for presenting the strategy. Ultimately, one approach gains purchase with the team, which thinks it best captures the key point: Get fewer customers to spend much more. The whiteboard looks something like this:

Of course, visuals that emerge from idea generation often lead to more formally designed and presented idea illustrations.

Visual Discovery. This is the most complicated quadrant, because in truth it holds two categories. Recall that we originally separated exploratory purposes into two kinds: testing a hypothesis and mining for patterns, trends, and anomalies. The former is focused, whereas the latter is more flexible. The bigger and more complex the data, and the less you know going in, the more open-ended the work.

Visual confirmation. You're answering one of two questions with this kind of project: Is what I suspect actually true? or What are some other ways of depicting this idea?

The scope of the data tends to be manageable, and the chart types you're likely to use are common-although when trying to depict things in new ways, you may venture into some less-common types. Confirmation usually doesn't happen in a formal setting; it's the work you do to find the charts you want to create for presentations. That means your time will shift away from design and toward prototyping that allows you to rapidly iterate on the dataviz. Some skill at manipulating spreadsheets and knowledge of programs or sites that enable swift prototyping are useful here.

Suppose a marketing manager believes that at certain times of the day more customers shop his site on mobile devices than on desktops, but his marketing programs aren't designed to take advantage of that. He loads some data into an online tool (called Datawrapper) to see if he's right (1 above).

He can't yet confirm or refute his hypothesis. He can't tell much of anything, but he's prototyping and using a tool that makes it easy to try different views into the data. He works fast; design is not a concern. He tries a line chart instead of a bar chart (2).

Now he's seeing something, but working with three variables still doesn't quite get at the mobile-versus-desktop view he wants, so he tries again with two variables (3). Each time he iterates, he evaluates whether he can confirm his original hypothesis: At certain times of day more customers are shopping on mobile devices than on desktops.

On the fourth try he zooms in and confirms his hypothesis (4).

New software tools mean this type of visualization is easier than ever before: They're making data analysts of us all.

Visual exploration. Open-ended data-driven visualizations tend to be the province of data scientists and business intelligence analysts, although new tools have begun to engage general managers in visual exploration. It's exciting to try, because it often produces insights that can't be gleaned any other way.

Because we don't know what we're looking for, these visuals tend to plot data more inclusively. In extreme cases, this kind of project may combine multiple data sets or load dynamic, real-time data into a system that updates automatically. Statistical modeling benefits from visual exploration.

Exploration also lends itself to interactivity: Managers can adjust parameters, inject new data sources, and continually revisualize. Complex data sometimes also suits specialized and unusual visualization, such as force-directed diagrams that show how networks cluster, or topographical plots.

Function trumps form here: Analytical, programming, data management, and business intelligence skills are more crucial than the ability to create presentable charts. Not surprisingly, this half of the quadrant is where managers are most likely to call in experts to help set up systems to wrangle data and create visualizations that fit their analytic goals.

Anmol Garg, a data scientist at Tesla Motors, has used visual exploration to tap into the vast amount of sensor data the company's cars produce. Garg created an interactive chart that shows the pressure in a car's tires over time. In true exploratory form, he and his team first created the visualizations and then found a variety of uses for them: to see whether tires are properly inflated when a car leaves the factory, how often customers reinflate them, and how long customers take to respond to a low-pressure alert; to find leak rates; and to do some predictive modeling on when tires are likely to go flat. The pressure of all four tires is visualized on a scatter plot, which, however inscrutable to a general audience, is clear to its intended audience.

Garg was exploring data to find insights that could be gleaned only through visuals. "We're dealing with terabytes of data all the time," he says. "You can't find anything looking at spreadsheets and querying databases. It has to be visual." For presentations to the executive team, Garg translates these exploration sessions into the kinds of simpler charts discussed below. "Management loves seeing visualizations," he says.

Everyday Dataviz. Whereas data scientists do most of the work on visual exploration, managers do most of the work on everyday visualizations. This quadrant comprises the basic charts and graphs you normally paste from a spreadsheet into a presentation. They are usually simple-line charts, bar charts, pies, and scatter plots.

"Simple" is the key. Ideally, the visualization will communicate a single message, charting only a few variables. And the goal is straightforward: affirming and setting context. Simplicity is primarily a design challenge, so design skills are important. Clarity and consistency make these charts most effective in the setting where they're typically used: a formal presentation. In a presentation, time is constrained. A poorly designed chart will waste that time by provoking questions that require the presenter to interpret information that's meant to be obvious. If an everyday dataviz can't speak for itself, it has failed-just like a joke whose punch line has to be explained.

That's not to say that declarative charts shouldn't generate discussion. But the discussion should be about the idea in the chart, not the chart itself.

Suppose an HR VP will be presenting to the rest of the executive committee about the company's health care costs. She wants to convey that the growth of these costs has slowed significantly, creating an opportunity to invest in additional health care services.

The VP has read an online report about this trend that includes a link to some government data. She downloads the data and clicks on the line chart option in Excel. She has her viz in a few seconds. But because this is for a presentation, she asks a designer colleague to add detail from the data set to give a more comprehensive view.

This is a well-designed, accurate chart, but it's probably not the right one. The executive committee doesn't need two decades' worth of historical context to discuss the company's strategy for employee benefits investments. The point the VP wants to make is that cost increases have slowed over the past few years. Is that clearly communicated here?

In general, when it takes more than a few seconds to digest the data in a chart, the chart will work better on paper or on a personal-device screen, for someone who's not expected to listen to a presentation while trying to take in so much information. For example, health care policy makers might benefit from seeing this chart in advance of a hearing at which they'll discuss these long-term trends.

Our VP needs something cleaner for her context. She could make her point as simply as this:

Simplicity like this takes some discipline-and courage-to achieve. The impulse is to include everything you know. Busy charts communicate the idea that you've been just that-busy. "Look at all the data I have and the work I've done," they seem to say. But that's not the VP's goal. She wants to persuade her colleagues to invest in new programs. With this chart, she won't have to utter a word for the executive team to understand the trend. She has clearly established a foundation for her recommendations.

In some ways, "data visualization" is a terrible term. It seems to reduce the construction of good charts to a mechanical procedure. It evokes the tools and methodology required to create rather than the creation itself. It's like calling Moby-Dick a "word sequentialization" or The Starry Night a "pigment distribution."

It also reflects an ongoing obsession in the dataviz world with process over outcomes. Visualization is merely a process. What we actually do when we make a good chart is get at some truth and move people to feel it-to see what couldn't be seen before. To change minds. To cause action.

Some basic common grammar will improve our ability to communicate visually. But good outcomes require a broader understanding and a strategic approach-which the typology described here is meant to help you develop.

A version of this article appeared in the June 2016 issue (pp.92-100) of Harvard Business Review.

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Not long ago, the ability to create smart data visualizations (or dataviz) was a nice-to-have skill for design- and data-minded managers. But now it's a must-have skill for all managers, because it's often the only way to make sense of the work they do.

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https://www.sisense.com/blog/10-useful-ways-visualize-data-examples/

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13 Powerful Methods for Data Visualization Success l Sisense

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Eliminate bad graphs and charts! Learn how to find the best way to visualize data and get your message across for your business scenario.

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https://ojin.nursingworld.org/MainMenuCategories/ANAMarketplace/ANAPeriodicals/OJIN/TableofContents/Vol-22-2017/No2-May-2017/Ethical-Issues-with-Medical-Errors.html

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Jeanne Merkle Sorrell, PhD, RN, FAAN
Citation: Sorrell, J.M., (March 7, 2017) "Ethics: Ethical Issues with Medical Errors: Shaping a Culture of Safety in Healthcare" OJIN: The Online Journal of Issues in Nursing Vol. 22, No. 2.

DOI: 10.3912/OJIN.Vol22No02EthCol01
https://doi.org/10.3912/OJIN.Vol22No02EthCol01

Patient safety experts at Johns Hopkins have calculated that more than 250,000 deaths per year in the United States are caused by medical errors ( McMains, 2016). In 2013, the Centers for Disease Control and Prevention (CDC) ranked the top three causes of death as heart disease, cancer, and respiratory disease. In 2016, researchers at Johns Hopkins found that medical errors replaced respiratory disease as the third leading cause of death ( Makary & Daniels, 2016; McMains, 2016). In international comparisons of deaths that are considered preventable by timely and effective healthcare, data showed that the United States had the highest number of preventable deaths in comparison to nine other countries, with France and Australia being the lowest ( Nolte & McKee, 2011). How can nurses help to address this problem by shaping a culture of safety in healthcare? Keep reading for some helpful, safety-promoting suggestions.

Students in the Advanced Clinical Nursing class that I taught were assigned to write a paradigm case--a story that reflected an ethical dilemma that they had experienced in their clinical practice. It was made clear that they needed to write something they could share with others, as we would set aside class time to read their stories aloud. With 22 students gathered in a circle in the classroom, Sarah* prefaced the reading of her story by confiding that she had read it to her husband before class. He asked her whether she really thought she should share the story openly. She replied, "Yes."

Sarah's story:

"Do no harm." This ethical principle has guided my nursing practice for almost 30 years. As a young nurse I was taught the 5 rights of medication administration. As an oncology nurse, I am painfully aware that many of the chemotherapy agents that I administer have the potential to cause death if not administered properly. Some time has passed now since I made a mistake that could have easily cost my patient her life. Today I share my story to help us think about what we can do to reduce the possibility of errors in our practice. The day that Mrs. May* received more than twice the dose of chemotherapy that had been ordered for her was not just any day. It was very close to Christmas. I was feeling especially excited because after being a registered nurse for over 25 years, I had finally completed my Bachelors of Science in Nursing. I was proud of my accomplishment and had received many compliments and best wishes for continued professional success. Mrs. May had been a patient in our practice for more than a year following a diagnosis of cancer, but I had not met her. I introduced myself and administered the chemotherapy. Afterward, Mrs. May went directly to see her physician and her chart went with her, so I did not have a chance to record the medications that I had given. At the close of the day, I realized that I had never charted on Mrs. May's record, so I retrieved her chart. As I looked at the dose of medication the oncologist had written, I felt a lump forming in my throat. I knew for a fact that I had mixed and administered two and a half times that amount. The medication was new to me. I remembered reconstituting 4 vials. I knew that I had given 250 mg instead of the 100 mg. that had been ordered. My heart began to race. I went back through the chart and realized that the dilution of the drug had been written where I had become accustomed to seeing the dose of the drug. Since the drug was under the same classification as another that I administered regularly, I didn't question that this might be too high a dose. Immediately, I went to the nurse manager to tell her what had happened. I knew that if Mrs. May died, there was a good chance that I would not ever be allowed to practice nursing again. Instead of giving me a reprimand, my manager said, "Let's make sure that you truly gave that much of the drug. You are always so careful." Together we went through the trash and found the 4 boxes. My heart sank. I kept thinking, "This lady is going to die and it is right at Christmas time."Today, I still remember the kindness that was bestowed on me that day. When I told the oncologist about my terrible error, he stated that he had ordered a lower dose of the medication for Mrs. May than normal for a patient with her condition, as she had a life expectancy of only 6 months to a year and didn't want to live with serious side effects from the chemotherapy. The oncologist said that she would be very, very sick for about a month but would not die from the overdose. He would treat her aggressively with agents to increase her cell counts so that she would have enough reserve to keep her counts from going down to zero. The next morning Mrs. May came to the clinic. I arrived early to tell her how very sorry I was. She said that her oncologist had told her if I had not come forth and told him of the mistake, she might not have lived. She trusted the doctor that she would not die from the error. I am happy to say that Mrs. May is still alive today. She certainly was a very sick lady for the next 2 weeks. One day she said to me, "I will not lie to you. I have never felt this bad in my life, but I will make it." Yes, Mrs. May made it. Her tumor decreased to one half its original size in about a month. Almost 2 years after this incident, Mr. and Mrs. May were able to enjoy an international vacation together. I talk with Mrs. May whenever she comes to the clinic and she always gives me a hug and reassurance that she is okay. Was I unbelievably fortunate? Yes. Can a medication error happen to even the most careful and conscientious nurse? Yes. Am I even more conscious of my ethical obligations to provide safe care? Yes. This incident has changed my life. I share my story with other nurses with the hope that it will cause them to think, "If it could happen to her, it could happen to me."

Sarah read this story** to my class a number of years ago but I still remember vividly how I felt after she finished. The classroom was totally silent. Many of us were fighting back tears. A classmate reached over to put her arm around Sarah's shoulder, a silent acknowledgement of support and admiration for the courage that Sarah had shown in sharing such a personal story. I think probably everyone in that room still remembers the moment when we each realized, "It could happen to me."

Medical errors are not typically caused by a negligent or incompetent healthcare professional. Instead they are often the result of a breakdown in processes that guide delivery of patient care ( Bonney, 2014). Sarah was a competent, careful, and caring nurse, but variances in the usual process of care set up a situation for error. The medication was new to Sarah and was ordered in a format that led to confusion of dose versus dilution. Medication orders should be written in clear and consistent formats so that the person administering the drug can readily understand the appropriate dose. Also, the chart was removed from the clinic setting before Sarah had a chance to record the medication administration. Access to the order on the medical record while administering the medication provides an important safety check to ensure the correct dose is both administered and recorded.

Many potential and actual medical errors fall within the sphere of nursing practice ( Lachman, 2007). Thus nurses have an ethical obligation to help prevent and manage medical errors. The remainder of this column will discuss ethical principles related to medical errors for nurses to consider, along with recommendations that can help to shape a culture of safety for the prevention of medical errors.

Ethical issues related to medical errors can be categorized around four ethical principles: autonomy and right to self-determination; beneficence and nonmaleficence; disclosure and right to knowledge; and veracity ( Bonney, 2014). Each of these principles will be discussed below.
Autonomy and Right to Self Determination
Concepts of autonomy and right to self determination acknowledge patients' rights to make their own choices and to take actions based on their personal views and perceived benefits. Healthcare providers have an ethical obligation to inform patients about their ongoing plan of care, including if a medical error has occurred. If Sarah had not informed others of her error, Mrs. May would not have been able to make appropriate decisions about the treatment that she needed as a result of the error. Healthcare providers are also obligated to assist patients in making decisions, as the physician did in his care of Mrs. May. The physician's honest discussion with Mrs. May about the medication error and potential adverse effects helped her to maintain trust in those caring for her and follow their instructions for treatment so that she could minimize harm from the error.
Beneficence and Nonmaleficence
The principles of beneficence and nonmaleficence direct healthcare providers to do what is best for patients and avoid harm. This may create moral conflicts for healthcare providers in terms of balancing projected benefits with possible risk for the patient. Although there is a range of severity in errors, they all cause harm - to the patient, to the person who made the error, and/or to the system ( Kalra, Kalra, & Baniak, 2013).Healthcare providers should take necessary steps to minimize the harm caused by an error. Sarah may have thought that informing Mrs. May of the error would cause unnecessary worry and suffering but had she not informed others of her error, Mrs. May would not have received important treatments to offset potential harm.
Disclosure and Right to Knowledge
Healthcare providers have an ethical obligation to disclose information that patients need for informed decision making. The patient's bill of rights calls for full disclosure of a medical error ( Ghazal, Saleem, & Ariani, 2014). Fortunately, in Sarah's situation healthcare providers disclosed the information needed to help Mrs. May make decisions about her care, thus respecting her autonomy and decreasing the potential for harm. Every institution needs clear and detailed policies for disclosure of information about medical errors.
Veracity
The principle of veracity requires healthcare personnel to provide comprehensive, accurate, and objective information in a manner that helps patients understand the information. Telling the truth about medical errors helps to establish trust. Healthcare providers' careful communication with Mrs. May helped to establish a sense of trust that can be seen in the mutual respect that Sarah and Mrs. May shared years after the unfortunate incident.

Communication researchers suggest that the ways healthcare providers 'story' their mistake experiences can help to understand medical errors ( Noland & Carmack, 2015).

Storytelling shifts thinking from 'rational and scientific' patterns to reflective thought that calls forth a detailed context surrounding the experience. Sarah's story illustrates the importance of context as she remembers why she did not record the medication she had administered to Mrs. May. She remembers the lump forming in her throat when she realized her error, her fear of losing her nursing license, her gratitude for the kindness of her nurse manager and the physician, and the trust that Mrs. May had in her healthcare providers, even after the error. Reflective thinking helps to uncover beliefs, values, and knowledge embedded in the experience ( Noland & Carmack, 2015). Storying an experience of a medical error helps the narrator and 'listeners' to come to know, understand, and make sense of the experience.

The prevention of medical errors within an organization requires systematic management strategies. Healthcare providers need education to understand the importance of reporting medical errors. Researchers in a study of 289 Canadian nurses working in long-term care noted that participants had different definitions of what constitutes harm with a medical error; their perceptions of harm influenced whether they reported the error ( Wagner, Damianakis, Pho, & Tourangeau, 2013). Because of busy working conditions, these nurses prioritized which errors to report. One participant stated, "If it's caused no harm, it's no big deal!" (p.3). Participants indicated overwhelmingly that they would like to receive continuing education to help them learn how to handle the 'after effects' of error occurrence. Nurses in all settings need education and training to develop a shared definition of harm and understand the process for reporting errors.

Although fear of litigation is often cited as a barrier to disclosure by healthcare providers, there is no established link between willingness to disclose medical errors and the risk of litigation ( Bonney, 2014). Sarah's story revealed multiple factors that contributed to her error, such as a new medication and a system event that delayed recording the medication. The culture of the organization, however, supported 'reporting' of her error. A 'blame culture' is a major source of medical errors ( Kalra et al., 2013). Organizations must create an environment where healthcare providers feel supported in reporting, disclosing, and discussing errors.

Considerable research in recent years has focused on disclosure of medical errors but has lacked interdisciplinary dialogue ( Hannawa, Beckman, Mazor, Paul, & Ramsey, 2013). Research is needed that incorporates disciplinary perspectives of professionals in healthcare, law, communication, and ethics to help healthcare providers understand and implement ethical practices for prevention and management of medical errors.

The American Nurses Association (ANA) defines a Culture of Safety as a work culture in which healthcare providers at all levels of the organization are committed to core values and behaviors that emphasize safety over competing goals ( ANA, 2016). Literature related to medical errors suggests that most medical errors are preventable ( Bonney, 2014). The categories listed in the Box below reflect important ways that nurses can contribute to prevention and management of medical errors ( Noland & Carmack, 2015; Zikhani, 2016). As the largest group of healthcare professionals, over 3 million strong, registered nurses are in a unique position to lead initiatives that promote a culture of safety ( ANA, 2016).
Box. Strategies for Prevention and Management of Medical Errors

Rules and Policies. Involve nurses in the development of clear and detailed policies for creating a safer environment in their organization.

Communication. Convey messages promptly and clearly. Handoff tools, such as SBAR or task debriefing, are effective ways to reduce communication failures.

Checklists, Reminders, and Double Checks. Design checklists and similar tools to reduce medical errors, especially in situations where errors tend to occur.

Simplification, Standardization, and Organization. Break down and standardize procedures and organize care-implementation processes into simple steps.

Computerization and Automation. Identify best practices for using technologies, such as healthcare informatics, to promote efficiency and accuracy and avoid errors.

Forcing Function. Devise procedures that make it almost impossible for errors to occur. For example, prevent an individual from entering a wrong medication into the computer or starting a process without submitting a completed checklist.

Sharing Stories of Errors. Encourage students and practitioners to tell their stories of medical errors, rather than hiding errors out of fear, and to listen to stories from others. Knowing how others have handled mistakes, or wish they had handled them, can help other staff prevent and/or manage healthcare errors.

Notes:

*Sarah and Mrs. May are pseudonyms used to protect the privacy of individuals involved in this story.

**This story is adapted from the chapter, Do no harm, as reported in the book Defining moments: The courage to be [Chapbooks]. The book was part of a class project and self-published by Jeanne Sorrell. The copyright date for this book is May 2001 and the copyright is held by Jeanne Sorrell.

Jeanne Merkle Sorrell, PhD, RN, FAAN
Email: jsorrell@gmu.edu

Jeanne Sorrell is former Senior Nurse Scientist, Nursing Research and Innovation, at the Cleveland Clinic in Cleveland, OH, and Professor Emerita, George Mason University in Fairfax, VA. She earned a BSN from the University of Michigan in Ann Arbor, MI, a MSN from the University of Wisconsin in Madison, WI, and a PhD from George Mason University. Her scholarly interests focus on philosophical inquiry, writing across the curriculum, qualitative research, and ethical considerations in healthcare.

American Nurses Association (ANA). (2016). American Nurses Association calls for a culture of safety in all health care settings. Retrieved from http://www.nursingworld.org/Call-for-Culture-of-Safety.html

Bonney, W. (2014). Medical errors: Moral and ethical considerations. Journal of Hospital Administration, 3(2), 80-88. doi:10.5430/jha.v3n2p80

Ghazal, L., Saleem, Z., & Ariani, G. (2014). A medical error: To disclose or not to disclose. Journal of Clinical Research & Bioethics, 5(2). doi:10.4172/2155-9627.1000174

Hannawa, A. F., Beckman, H., Mazor, K. M., Paul, N., & Ramsey, J. V. (2013). Building bridges: Future directions for medical error disclosure research. Patient Education and Counseling, 92, 319-327. doi: 10.1016/j.pec.2013.05.017.

Kalra, J., Kalra, N., &Baniak, N. (2013). Medical error, disclosure and patient safety: A global view of quality care. Clinical Biochemistry, 46, 1161-1169. doi: 10.1016/j.clinbiochem.2013.03.025.

Lachman, V. D. (2007). Patient safety: The ethical imperative. MEDSURG Nursing, 16(6), 401-403.

Makary, M., & Daniels, M. (2016). Medical error - The third leading cause of death in the U.S., British Medical Journal, 353, 1-5. doi:10.1136/bmj.i213

McMains, V. (2016). Johns Hopkins study suggests medical errors are third-leading cause of death in U.S. Johns Hopkins Magazine. Retrieved from http://hub.jhu.edu/2016/05/03/medical-errors-third-leading-cause-of-death/

Noland, C. M., &Carmack, J. (2015). Narrativizing nursing students' experiences with medical errors during clinicals. Qualitative Health Research, 25(10), 1423-1434. doi: 10.1177/1049732314562892.

Nolte, E., & McKee, M. (2011). Variations in amenable mortality - Trends in 16 high-income nations. Health Policy, 103(1), 47-52. doi: 10.1016/j.healthpol.2011.08.002 Retrieved from http://www.healthpolicyjrnl.com/article/S0168-8510(11)00159-X/pdf

Wagner, L. M., Damianakis, T., Pho, L., & Tourangeau, A. (2013). Barriers and facilitators to communicating nursing errors in long-term care settings. Journal of Patient Safety, 9(1), 1-7. doi:10.1097/PTS.0b013e3182699919.

Zikhani, R. (2016). Seven-step pathway for preventing errors in healthcare. Journal of Healthcare Management, 61( 4), 271-281.

© 2017 OJIN: The Online Journal of Issues in Nursing

Article published March 7, 2017

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Ethics: Ethical Issues with Medical Errors: Shaping a Culture of Safety in Healthcare

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Jeanne Merkle Sorrell, PhD, RN, FAAN Citation: Sorrell, J.M., (March 7, 2017) "Ethics: Ethical Issues with Medical Errors: Shaping a Culture of Safety in Healthcare" OJIN: The Online Journal of Issues in Nursing Vol. 22, No. 2.

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https://keydifferences.com/difference-between-law-and-ethics.html

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In simple terms, the law may be understood as the systematic set of universally accepted rules and regulation created by an appropriate authority such as government, which may be regional, national, international, etc. It is used to govern the action and behavior of the members and can be enforced, by imposing penalties.

Many times the term law is juxtaposed with the term ethics, but there is a difference, as ethics are the principles that guide a person or society, created to decide what is good or bad, right or wrong, in a given situation. It regulates a person’s behavior or conduct and helps an individual in living a good life, by applying the moral rules and guidelines.

For a layman, these two terms are same, but the fact is that there is a difference between law and ethics. Read the article carefully, to overcome your ambiguities.

Content: Law Vs Ethics

1. Comparison Chart

2. Definition

3. Key Differences

4. Conclusion

Comparison Chart

Basis for Comparison | Law | Ethics
Meaning | The law refers to a systematic body of rules that governs the whole society and the actions of its individual members. | Ethics is a branch of moral philosophy that guides people about the basic human conduct.
What is it? | Set of rules and regulations | Set of guidelines
Governed By | Government | Individual, Legal and Professional norms
Expression | Expressed and published in writing. | They are abstract.
Violation | Violation of law is not permissible which may result in punishment like imprisonment or fine or both. | There is no punishment for violation of ethics.
Objective | Law is created with an intent to maintain social order and peace in the society and provide protection to all the citizens. | Ethics are made to help people to decide what is right or wrong and how to act.
Binding | Law has a legal binding. | Ethics do not have a binding nature.

Definition of Law

The law is described as the set of rules and regulation, created by the government to govern the whole society. The law is universally accepted, recognized and enforced. It is created with the purpose of maintaining social order, peace, justice in the society and to provide protection to the general public and safeguard their interest. It is made after considering ethical principles and moral values.

The law is made by the judicial system of the country. Every person in the country is bound to follow the law. It clearly defines what a person must or must not do. So, in the case of the breach of law may result in the punishment or penalty or sometimes both.

Definition of Ethics

By ethics, we mean that branch of moral philosophy that guides people about what is good or bad. It is a collection of fundamental concepts and principles of an ideal human character. The principles help us in making decisions regarding, what is right or wrong. It informs us about how to act in a particular situation and make a judgment to make better choices for ourselves.

Ethics are the code of conduct agreed and adopted by the people. It sets a standard of how a person should live and interact with other people.

Types of Ethics

The major differences between law and ethics are mentioned below:

1. The law is defined as the systematic body of rules that governs the whole society and the actions of its individual members. Ethics means the science of a standard human conduct.

2. The law consists of a set of rules and regulations, whereas Ethics comprises of guidelines and principles that inform people about how to live or how to behave in a particular situation.

3. The law is created by the Government, which may be local, regional, national or international. On the other hand, ethics are governed by an individual, legal or professional norms, i.e. workplace ethics, environmental ethics and so on.

4. The law is expressed in the constitution in a written form. As opposed to ethics, it cannot be found in writing form.

5. The breach of law may result in punishment or penalty, or both which is not in the case of breach of ethics.

6. The objective of the law is to maintain social order and peace within the nation and protection to all the citizens. Unlike, ethics that are the code of conduct that helps a person to decide what is right or wrong and how to act.

7. The law creates a legal binding, but ethics has no such binding on the people.

Conclusion

Law and ethics are different in a manner that what a person must do and what a person should do. The former is universally accepted while the latter is ideal human conduct, agreed upon by most of the people. Although, both the law and ethics are made in alignment so that they do not contradict each other. Both go side by side, as they provide how to act in a particular manner. Every person is equal in the eyes of law and ethics, i.e. nobody is superior or inferior. Further, these two allow a person to think freely and choose.

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Difference Between Law and Ethics (with Comparison Chart) - Key Differences

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There exists a fine line of difference between law and ethics. The former, is the systematic set of universally accepted rules and regulation created by appropriate authority, i.e. government while the latter are the principles that guide a person or society, created on the basis of what is good or bad.