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In this article you can find
the definition of health informatics, a glossary of terms relevant to health informatics,
potential careers and degrees in health informatics, and other fascinating information about this topic. Enjoy!

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Glossary of Terms Relevant to Health Informatics

Agency for Healthcare Research and Quality (AHRQ)

The branch of the United States Public Health Service that supports general health research and distributes research findings and treatment guidelines with the goal of improving the quality, appropriateness, and effectiveness of healthcare services.

American Recovery and Reinvestment Act of 2009 (ARRA)

The purposes of this act include:

Preserve and create jobs and promote economic recovery.

Assist those most impacted by the recession.

Provide investments needed to increase economic efficiency by spurring technological advances in science and health.

Invest in transportation, environmental protection, and other infrastructures that will provide long-term economic benefits.

Stabilize state and local government budgets in order to minimize and avoid reductions in essential services and counterproductive state and local tax increases.

Analysis

Review of the health record for proper documentation and adherence to regulatory and accreditation standards.

Beacon Community Cooperative Agreement Program

This program demonstrates how health IT investments and meaningful use of EHRs advance the vision of patient-centered care, while achieving the three-part aim of better health, better care, and lower cost. The Office of the National Coordinator for Health IT (ONC) is providing $250 million over three years to 17 selected communities throughout the United States that have already made inroads in the development of secure, private, and accurate systems of EHR adoption and health information exchange.

Biomedical Informatics

A field of study concerned with the broad range of issues in the management and use of biomedical information, including biomedical computing and the study of the nature of biomedical information itself. Formerly called medical informatics, the new name is intended to clarify that the domain encompasses biological and biomolecular informatics as well as clinical, imaging, and public health informatics. Biomedical informatics is the interdisciplinary field that studies and pursues the effective uses of biomedical data, information, and knowledge for scientific inquiry, problem solving, and decision making, motivated by efforts to improve human health. It's the use of information technology for assimilating, gathering, organizing, analyzing, and presenting healthcare-related data to produce information for decision support to improve quality of care, decrease costs, enhance patient safety, and increase interoperability. Health information technology is the tool and information is the outcome.

Biomedical Research

The process of systematically investigating subjects related to the functioning of the human body.

Centers for Disease Control and Prevention (CDC)

A federal agency dedicated to protecting health and promoting quality of life through the prevention and control of disease, injury, and disability. Committed to programs that reduce the health and economic consequences of the leading causes of death and disability, thereby ensuring a long, productive, and healthy life for all people.

Centers for Medicare and Medicaid Services (CMS)

The Department of Health and Human Services (HHS) agency responsible for Medicare and parts of Medicaid. Historically, CMS has maintained the UB-92 institutional electronic media claims (EMC) format specifications, the professional EMC NSF specifications, and specifications for various certifications and authorizations used by the Medicare and Medicaid programs. CMS is responsible for the oversight of HIPAA administrative simplification transaction and code sets, health identifiers, and security standards. CMS also maintains the HCPCS medical code set and the Medicare Remittance Advice Remark Codes administrative code set.

Certification Commission for Healthcare Information Technology (CCHIT)

An independent voluntary private-sector initiative organized as a limited liability corporation that has been awarded a contract by HHS to develop, create prototypes for, and evaluate the certification criteria and inspection process for electronic health record (EHR) products.

Certified Health Data Analyst (CHDA)

AHIMA credential awarded to individuals who have demonstrated skills and expertise in health data analysis.

Certified in Healthcare Privacy and Security (CHPS)

AHIMA credential that recognizes advanced competency in designing, implementing, and administering comprehensive privacy and security protection programs in all types of healthcare organizations. Requires successful completion of the CHPS exam sponsored by AHIMA.

Clinical Analytics

The process of gathering and examining data in order to help gain greater insight about patients.

Clinical Data Analytics

The process by which health information is captured, reviewed, and used to measure quality.

Clinical Decision Support

The process in which individual data elements are represented in the computer by a special code to be used in making comparisons, trending results, and supplying clinical reminders and alerts.

Clinical Document Architecture (CDA)

A Health Level Seven (HL7) XML-based document markup standard for the electronic exchange model for clinical documents (such as discharge summaries and progress notes). The implementation guide contains a library of CDA templates, incorporating and harmonizing previous efforts from HL7, Integrating the Healthcare Enterprise, and Health Information Technology Standards Panel (HITSP). It includes all required CDA templates for stage 1 of the "meaningful use" EHR Incentive Program and HITECH final rule. It is commonly referred to as Consolidate CDA or C-CDA.

Clinical Documentation Improvement (CDI)

The process an organization undertakes that will improve clinical specificity and documentation that will allow coders to assign more concise disease classification codes.

Clinical Documentation Improvement Plan

A program in which specialists concurrently review health records for incomplete documentation, prompting clinical staff to clarify ambiguity which allows coders to assign more concise disease classification codes.

Clinical Document Improvement Practitioner (CDIP)

AHIMA credential awarded to individuals who have achieved specialized skills in clinical documentation improvement.

Clinical Terminology

A set of standardized terms and their synonyms that record patient findings, circumstances, events, and interventions with sufficient detail to support clinical care, decision support, outcomes research, and quality improvement.

Commission on Accreditation of Health Informatics and Information Management Education (CAHIIM)

An independent accrediting organization whose mission is to serve the public interest by establishing and enforcing quality accreditation standards for health informatics and health information management educational programs.

Commission on Certification for Health Informatics and Information Management (CCHIIM)

An independent body within AHIMA that establishes and enforces standards for the certification and certification maintenance of health informatics and information management professionals.

Comparative Effectiveness Research (CER)

Research that generates and synthesizes evidence that compares the benefits and harms of alternative methods to prevent, diagnose, treat, and monitor a clinical condition or to improve the delivery of care.

Computer-Assisted Coding (CAC)

The process of extracting and translating dictated and then transcribed free-text data (or dictated and then computer-generated discrete data) into ICD-9-CM and CPT evaluation and management codes for billing and coding purposes.

Computerized Provider Order Entry (CPOE)

Electronic prescribing systems that allow physicians to write prescriptions and transmit them electronically. These systems usually contain error prevention software that provides the user with prompts that warn against the possibility of drug interaction, allergy, overdose, and other relevant information.

Covered Entity (CE)

As amended by HITECH, a covered entity may be defined as a health plan, a healthcare clearinghouse, or a healthcare provider who transmits any health information in electronic form in connection with a transaction covered by HITECH.

Data Analytics

The science of examining raw data with the purpose of drawing conclusions about that information. This includes data mining, machine language, development of models, and statistical measurements. Analytics can be descriptive, predictive, or prescriptive.

Data Dictionary

A descriptive list of the names, definitions, and attributes of data elements to be collected in an information system or database whose purpose is to standardize definitions and ensure consistent use.

Data Governance

The overall management of the availability, usability, integrity, and security of the data employed in an organization or enterprise.9

Data Mapping

Data mapping allows for connections between two systems. This connection allows for data initially captured for one purpose to be translated and used for another purpose. One system in a map is identified as the source while the other is the target. It is a process by which two distinct data models are created and a link between these models is defined. This process is used in data warehousing by which different data models are linked to each other using a defined set of methods to characterize the data in a specific definition. This definition can be any atomic unit, such as a unit of metadata or any other semantic. This data linking follows a set of standards, which depends on the domain value of the data model used. Data mapping serves as the initial step in data integration.

Data Mining

The process of extracting and analyzing large volumes of data from a database for the purpose of identifying hidden and sometimes subtle relationships or patterns and using those relationships to predict behaviors.

Data Stewardship

The responsibilities and accountabilities associated with managing, collecting, viewing, storing, sharing, disclosing, or otherwise making use of personal health information.

Decision Support System (DSS)

A computer-based system that gathers data from a variety of sources and assists in providing structure to the data by using various analytical models and visual tools in order to facilitate and improve the ultimate outcome in decision making tasks associated with non-routine and non-repetitive problems.

Descriptive Statistics

A set of statistical techniques used to describe data such as means, frequency distributions, and standard deviations; statistical information that describes the characteristics of a specified group or a population.

Enterprise Information Management (EIM)

Ensuring the value of information assets, requiring an organization-wide perspective of information management functions, calls for explicit structures, policies, processes, technology, and controls. EIM is the infrastructure and processes in place to ensure information is trustworthy and actionable.

Health Informatics

Scientific discipline that is concerned with the cognitive, information-processing, and communication tasks of healthcare practice, education, and research, including the information science and technology to support these tasks.

Health Informatics and Information Management (HIIM)

Refers to the individuals responsible for the management of healthcare data and information in paper or electronic form and control the collection, access, use, exchange, and protection of the information through the application of health information technology.

Health Information Technology

A term that encompasses the technical roles that process health data and records, such as classification, abstracting, and retrieval. Under HITECH, health IT is defined as hardware, software, integrated technologies or related licenses, intellectual property, upgrades, or packaged solutions sold as services that are designed for, or support the use by, healthcare entities or patients for the electronic creation, maintenance, access, or exchange of health information.

Healthcare Cost and Utilization Project (HCUP)

A family of databases and related software tools and products developed through a federal-state-industry partnership and sponsored by AHRQ. HCUP databases are derived from administrative data and contain encounter-level, clinical, and nonclinical information including all listed diagnoses and procedures, discharge status, patient demographics, and charges for all patients, regardless of payer, beginning in 1988.

Healthcare Effectiveness Data and Information Set (HEDIS)

A set of standard performance measures that can give an individual information about the quality of a health plan. One can find out about the quality of care, access, cost, and other measures to compared managed care plans. CMS collects HEDIS data for Medicare plans.

Informatics

A field of study that focuses on the use of technology to improve access to, and utilization of, information.

Information Governance (IG)

The accountability framework and decision rights to achieve enterprise information management (EIM). IG is the responsibility of executive leadership for developing and driving the IG strategy throughout the organization. IG encompasses both data governance and information technology governance.

Information Technology Governance (ITG)

Led by the chief information officer (CIO), the process to ensure the effective evaluation, selection, prioritization, and funding of competing IT investments. ITG oversees the implementation of these investments and extracts business benefits.

International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM)

The coding classification system that will replace ICD-9-CM, Volumes 1 and 2, on October 1, 2015. ICD-10-CM is the United States' clinical modification of the World Health Organization's ICD-10. ICD-10-CM has a total of 21 chapters and contains significantly more codes than ICD-9-CM, providing the ability to code with a greater level of specificity.

International Classification of Diseases, Tenth Revision, Procedure Coding System (ICD-10-PCS)

The coding classification system that will replace ICD-9-CM, Volume 3, on October 1, 2015. ICD-10-PCS has 16 sections and contains significantly more procedure codes than ICD-9-CM, providing the ability to code procedures with a greater level of specificity.

Interoperability

The capability of different information systems and software applications to communicate and exchange data.

Machine Learning

An area of computer science that studies algorithms and computer programs that improve employee performance on some task by exposure to training or learning experience.

Medical Informatics

A field of information science concerned with the management of data and information used to diagnose, treat, cure, and prevent disease through the application of computers and computer technologies.

Natural Language Processing (NLP)

A technology that converts human language (structured or unstructured) into data that can be translated and then manipulated by computer systems; a branch of artificial intelligence.

Office of the National Coordinator for Health Information Technology (ONC)

The principle federal entity charged with coordination of nationwide efforts to implement and use the most advanced health information technology and the electronic exchange of health information. The position of the National Coordinator was created in 2004, through an Executive Order, and legislatively mandated in the HITECH Act of 2009.

Predictive Modeling

A process used to identify patterns that can be used to predict the odds of a particular outcome based on the observed data.

Semantic Interoperability

Mutual understanding of the meaning of data exchanged between information systems.

Telehealth

A telecommunications system that links healthcare organizations and patients from diverse geographic locations and transmits text and images for medical consultation and treatment.

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Defining the Basics of Health Informatics for HIM Professionals - Retired

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Agency for Healthcare Research and Quality (AHRQ) The branch of the United States Public Health Service that supports general health research and distributes research findings and treatment guidelines with the goal of improving the quality, appropriateness, and effectiveness of healthcare services.

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Nursing Informatics: A historical perspectives of nursing informatics, nursing informatics impact on current bedside nursing practice, and the future of nursing informatics.

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Informatics is an established area of specialty practice in nursing. As with all specialty areas, each practicing nurse should have an essential awareness and understanding of its components. The purpose of this activity is to provide the learner with an opportunity to review historical perspectives of nursing informatics, discuss how nursing informatics impacts current bedside nursing practice, and consider the future of nursing informatics.

Nurses may think of informatics as an area that is not "for them", in the same way some would say, "I could never be a (insert least favorite specialty here) nurse!" This module challenges the reader, the every-day-nurse at the bedside, to push past the thought that nursing informatics is just about computers and not "for them". A challenge to think about how informatics is a tool in the toolbox for every nurse in the developed world, every day, with every patient encounter.
Definitions
If bedside nurses were asked to define nursing informatics, a variety of responses would likely follow. Nurses who have been educated within the last 15 years have a general idea of the concept. Nurses who trained 20 to 30 years ago and are still practicing may remember the days of paper charting when within normal limits (WNL) was a taught as a best practice for nursing documentation in the chart by exception era. During this era, one physician reports learning the definition of the abbreviation WNL as, we never looked instead of within normal limits. Many nurses, regardless of the period of their education, may define informatics as primarily related to documentation and computers (Cummin, Gundlapalli, Marray, Park, & Lehmann, 2016).

The Quality and Safety Education in Nursing (QSEN) Institute defines nursing informatics as the "use of information and technology to communicate, manage knowledge, mitigate error, and support decision making" (QSEN, 2019, para 1). The QSEN definition explains why nursing informatics is more than documentation and provides the best rationale for why nursing informatics is more than just another nursing specialty. Communication, managing knowledge, and clinical decision support are worlds apart from the quick nursing note of WNL. Communicating, managing knowledge, and making clinical decisions are bedrocks of each nurse's scope of practice. These are not areas to be dismissed as a specialty that the nurse may or may not be interested in pursuing (Cummin et al., 2016).
Organizational Influences
Nursing informatics evolved against the backdrop of the Fourth Industrial Revolution's impact on healthcare (McMaster, 2018). Before the advent of computer charting, documentation amongst nurses and other healthcare providers was inconsistent between organizations, and often among organizations as well. The exponential growth of technology in the early 2000s provided opportunities for innovative thinkers to find solutions to complex problems in healthcare communication. Health information is the broad field birthed through this period. Nursing, the professionals who work closest to the patient every day, were early adopters of technology when the benefit to the patient was evident (Cummin et al., 2016).

The Centers for Disease Control and Prevention (CDC, 2019) provides an excellent overview of the timeline leading up to the current state of health information in the US. A concerted effort to update government infrastructure led to the American Reinvestment and Recovery Act (ARRA) in 2009, which included language addressing health information management in the Health Information Technology for Economic and Clinical Health (HITECH) Act. The Centers for Medicaid and Medicare, and The Office of the National Coordinator for Health IT (ONC) worked to implement changes to support meaningful use of electronic data in healthcare (CDC, 2019).

Snowdon (2017) authored the Healthcare Information and Management Systems Society's (HIMSS) white paper calling for transparency in clinically integrated systems on an international level. Snowdon's work punctuated the mortality statistics related to medical error and cited the Institutes of Medicine (IOM), Canadian, and European Union's responses to these challenges. Peltonen, Sensmeier, Saranto, Newbold, and Ramírez (2018) provide a historical overview and lessons learned from nursing informatics globally, highlighting similarities and differences, and called for more international collaboration and consistency.

In 2010, the Healthcare Information and Management Systems Society published the Technology Informatics Guiding Education Reform (TIGER) initiative, informatics competencies for the nurse generalist. Ten years later, the three TIGER initiative competencies of primary/basic computer skills, information literacy, and information management are included in pre-licensure education. The third TIGER competency, information management, is the area with the most exponential growth (HIMSS, 2010).

Decades of hard work, legislation, non-profit organizations, professional organizations, and individuals in practice are responsible for the positive effects that are seen at the bedside today. The revolution has not been painless, and it has not been without casualties. Ivory (2019), describes the process as a tug-of-war with technology pulling against patient care.
Current Practice General Nursing Education
Today, formal training in informatics is part of the required undergraduate curriculum in the United States. The content is typically integrated throughout the curriculum, instead of an individual course at the undergraduate level. The National Council of State Boards of Nursing licensure examination (NCLEX) includes items related to nursing informatics. Peltonen et al. (2018) describe competency requirements at the undergraduate, masters, and doctoral levels for nurses. At the graduate level, individual courses in nursing informatics are standard. Continuing education in practice is not standardized at this time. Borycki, Cummings, Kushniruk, and Saranto (2017) describe the need for health information competencies to improve patient safety and reduce the risk of technology-related errors. Nurses in practice have continuing education opportunities to stay current in nursing informatics trends (Kleib, Simpson, & Rhodes, 2016). Despite the systematic approach to integrating nursing informatics into nursing education, gaps in practice and knowledge are still evident.
Nursing Specialty Development
Nurses may obtain further formal and professional education to obtain specialty status. A nurse may complete a Master of Science in Nursing program with an emphasis in nursing informatics at an accredited university. Additionally, nurses may also complete a Doctorate in Nursing Practice in Informatics. Graduate school is not the only path to a specialized career in nursing informatics. Practicing registered nurses with the experience to meet the eligibility criteria may take a nationally recognized credentialing examination through a professional organization that offers such certifications, such as the American Nurses Credentialing Center (Cummin et al., 2016).

Nurses who specialize in informatics are in high demand in healthcare institutions around the world. Nurse informatics are forward thinkers. Brennan and Bakken (2015) explain the puzzle of big data in nursing. The colloquial saying, "more is better", may not always be true when it comes to healthcare data. While knowing more about a patient's history and the condition assists in developing the patient's plan of care, having reams and reams of data to sift through may slow down the process and delay the genuine care the patient needs. The word panopticon has been used to describe the electronic health data, all-seeing structure recording volumes, and volumes of data (Dillard, 2019).

Society needs individuals who are trained, first and foremost, as a nurse, to embrace the challenges of big data and find ways to manage the knowledge that is available to reduce errors and improve communication as the needs relate to nursing care of the patient. Hussey and Kennedy (2016) connect the specialty of nursing informatics with holistic patient care. An ongoing argument against technology in healthcare is the decrease in person-to-person interaction and the seemingly endless increase in person-to-technology communication. A visitor to a patient in a hospital room may notice a nurse walk into the room, look at the monitors, check the infusion pumps, and silence an alarm all before the nurse speaks to the patient or the visitor. A specialty degree in nursing informatics is not required to deliver holistic, person-centered care. Still, a nurse with a specialty in informatics may be able to collaborate with the design team and the bedside staff to create a way for the nurse the see the patient before the nurse considers the technology (Cummin et al., 2016).
Implications Impact on Patient Safety
Keeping patients safe is an explicit goal of all patient care encounters. With the changes over the last two decades from a paper to an electronic world of healthcare, the debate on patient safety continues. Sewell (2019) emphasizes how big data can be used to prevent deaths. While there are several positive aspects of informatics in healthcare, with every benefit, there is an inherent risk. Borycki et al. (2017) provide a literature review to develop an understanding of the concept of technology-induced errors.

Sewell (2019) provides an overview of several quality measures related to informatics tools available for bedside nursing practice as well as some of the potential risks. Clinical decision support provides nurses and providers with direct support at the point of care. Administering medications with the assistance of an electronic medication administration record (eMAR) decreases the risk of order entry error. The use of technology improves positive patient identification to protect the patient. The risks are often related to the reality of workarounds (Brenan & Bakken, 2015).

Workarounds are unfortunate examples of what happens when technology makes the actual nursing care difficult or nearly impossible to complete within the time demands of concern for the patient. Thus, clinically competent and experienced nurses with advanced training in informatics are needed to design systems and processes that are genuinely workable for nurses at the bedside. Technology is only as safe as the person who uses it. A systems approach to problem-solving is required to eliminate the practice of workarounds. Bedside nurses should know when and how to communicate technical issues and to understand that problems should be addressed with patient safety in mind. This gap between technology advances and expected uses and the actual functionality issues at the bedside create the space for workarounds. The need for workarounds and the feeling that technology is impeding on safe patient care are additional battlegrounds in the tug-of-war (Ivory, 2019).
Research and Innovations
Borycki et al. (2017) describe the nursing informatics researchers and innovators as nurses who utilize informatics and health information theories and work to create new research. These nurses work to interpret and find new, meaningful ways to use big data. Kieft et al. (2017) discuss the need for improvements in nursing terminology to improve the interoperability of data. Language for medical diagnosis and order sets have developed over the last two decades as the diagnoses and orders are now tied directly to billing. Nursing diagnoses and clinical documentation are now directly related to reimbursement. As more nurses become experts in informatics, nursing documentation will continue to move toward a clear standard for terminology.

Ronquillo, Topaz, Pruinelli, Peltonen, and Nibber (2017) list collaboration and integration as a significant theme to consider for nursing informatics. As nurses continue to refine the language of nursing, the ability to integrate the unique perspectives of nurses into the management of information in healthcare will improve. Kim (2019) suggests that some of the best solutions may lie in a shared educational experience. Combining nursing students with engineering students to change perspectives and induce creative solutions. Murphy, Honey, Newbold, Weber, and Wu (2018) propose shared informatics competencies across healthcare disciplines. Nursing informatics innovators see the trends in technology and health and collaborate with other disciplines, even outside healthcare, to find creative solutions to challenges. Nurses have to leverage technology for the best patient care possible.
Summary
In summary, nursing informatics is one of the fastest-growing specialty areas in the profession. Unlike many of the specialty areas of nursing, informatics is used habitually by every nurse in practice with every patient encounter in the developed world where technology is present. Nurses, regardless of the level of education, use this technology to improve communication and patient safety. Nurses can obtain advanced degrees in informatics as well as earn specialty credentials. The history of nursing informatics is grounded in what some have called the Fourth Industrial Revolution. Sweeping changes following the American Reinvestment and Recovery Act in 2009 in the United States propelled the development of informatics in healthcare as well as competencies required.

Future research and trends focus on the meaningful use of the volumes of big data available for patient health as well as finding ways to mitigate the risk of technology-induced errors (Westra et al., 2017; QSEN, 2019; Brennan & Bakken, 2015). The primary focus of all nursing care is the patient. In the future, nursing informatics innovators will continue to refine processes, connect-the-dots, improve communication, and empower patients by harnessing the momentum of technology in practical and innovative ways.
References
Brennan, P. F., & Bakken, S. (2015). Nursing needs big data, and big data needs nursing. Journal of Nursing Scholarship, 47(5), 477-484. doi: 10.1111/jnu.12159

Borycki, E., Cummings, E., Kushniruk, A., & Saranto, K. (2017). Integrating health information technology safety into nursing informatics competencies. Studies in Health Technology and Informatics, 232, 222-228.

Centers for Disease Control and Prevention (2019). Public health and promoting interoperability programs (formerly known as Electronic Health Records Meaningful Use). Retrieved from https://www.cdc.gov/ehrmeaningfuluse/introduction.html

Cummin, M., Gundlapalli, A., Marray, P., Park, H., & Lehmann (2016). Nursing informatics certification worldwide: History, pathway, roles, and motivations. Yearbook of Medical Informatics, 10( 1), 264-271. doi: 10.15265/IY-2016-039.

Dillard, W. J. (2019). Electronic health record as a panopticon: A disciplinary apparatus in nursing practice. Nursing Philosophy, 20(2). doi: 10.1111/nup.12239

Healthcare Information and Management Systems Society. (2010). Informatics competencies for every practicing nurse: Recommendations from the TIGER collaborative. Retrieved from https://www.himss.org/informatics-competencies

Hussey, P. A., & Kennedy, M. A. (2016). Instantiating informatics in nursing practice for integrated patient-centered holistic models of care: A discussion paper. Journal of Advanced Nursing, 72(5), 1030-1041. doi: 10.1111/jan.12927

Ivory, C. (2019). Nursing informatics: The tug of war between practice and technology. Nursing Management, 50(1), 7-9. doi: 10.1097/01.NUMA.0000544459.81451.e0

Kieft, M., Vreeke, M., de Groot, E., Volkert, P., Francke, A., & Delnoij, D. (2017). The development of a nursing subset of patient problems to support interoperability. BMC Medical Informatics and Decision Making, (1), 1 doi: 10.1186/s12911-017-0567-5

Kim, H. (2019). A conceptual framework for interdisciplinary education in engineering and nursing health informatics. Nurse Education Today, 74, 91-93. doi: 10.1016/j.nedt.2018.12.010

Kleib, M., Simpson, N., & Rhodes, B. (2016). Information and communication technology: Design, delivery, and outcomes from a nursing informatics boot camp. Online Journal of Issues in Nursing, 21(2). doi: 10.3912/OJIN.Vol21No02Man05

McMaster, R. (2018). Is the fourth industrial revolution relevant to you? Nursing & Health Sciences, 20(2), 139-141. doi:10.1111/nhs.12542

Murphy, J., Honey, M., Newbold, S., Weber, P., & Wu, Y. (2018). Forecasting informatics competencies for nurses in the future of connected health. Studies in Health Technology and Informatics, 250, 58-59.

Peltonen, L., Sensmeier, J., Saranto, K., Newbold, S. K., & Ramírez, C. (2018). Supporting nursing informatics in practice - Lessons learned by nursing informatics pioneers. Studies in Health Technology and Informatics, 250, 62-64.

Quality and Safety Education in Nursing Institute. (2019). QSEN competencies: Informatics. QSEN Institute. Retrieved from http://qsen.org/competencies/pre-licensure-ksas/#informatics

Ronquillo, C., Topaz, M., Pruinelli, L., Peltonen, L.-M., & Nibber, R. (2017). Competency recommendations for advancing nursing informatics in the next decade: International survey results. Studies in Health Technology and Informatics, 232, 119-129.

Sewell, J. (2019). Informatics and nursing: Opportunities and challenges (6th ed.). Philadelphia, PA: Wolters Kluwer Health | Lippincott Williams & Wilkins.

Snowdon, A. (2017). A clinically integrated outcomes strategy for health systems globally. Healthcare Information and Management Systems Society Analytics. Retrieved from https://www.himssanalytics.org/news/white-paper-clinically-integrated-outcomes strategy-health-systems-globally

Westra, B. L., Sylvia, M., Weinfurter, E. F., Pruinelli, L., Park, J. I., Dodd, D., ... Delaney, C. W. (2017). Big data science: A literature review of nursing research exemplars. Nursing Outlook, 65(5), 549-561. doi: 10.1016/j.outlook.2016.11.021

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Informatics is an established area of specialty practice in nursing. As with all specialty areas, each practicing nurse should have an essential awareness and understanding of its components. The purpose of this activity is to provide the learner with an opportunity to review historical perspectives of nursing informatics, discuss how nursing informatics impacts current bedside nursing practice, and consider the future of nursing informatics.

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Informatics in Healthcare and Research: Future Focus

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Informatics in Healthcare and Research: Future Focus

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NursingCE (2020, para. 2) stated "future research and trends focus on the meaningful use of the volumes of big data available for patient health as well as finding ways to mitigate the risk of technology-induced errors"

NursingCE. (2020). Nursing informatics nursing CE course. Retrieved from https://www.nursingce.com/ceu-courses/nursing-informatics

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There are always unlimited amounts of data available to individuals. The use of the internet, smartphones, and my favorite, Dr. Google, has made searching for healthcare information easy and fast. The problem is sorting through all this information for reliable, evidence-based data and research. In healthcare, it is imperative to know how to analyze data and research to guide our clinical decision-making skills. Data analysis and research go together. When researching, one must analyze the validity and evidence behind the information present in research. Learning these tools will not only make you successful in the educational environment but are invaluable when making clinical decisions during patient care.

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Healthcare industries have finally jumped on the bandwagon for collecting BIG DATA . It may have been costly upfront, but the impact was enormous. This data can then be analyze to improve the healthcare industry as a whole and (most importantly) improve patient outcomes. New technology has allowed the collection of BIG DATA (large amounts of data) to become easier and less time consuming. The EHR is great resource for collecting big data in a healthcare facility.

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Key Steps in the Analysis of Healthcare Data: You Can Make a Difference!

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Key Steps in the Analysis of Healthcare Data: You Can Make a Difference!

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Great video about nurses using data analysis to help identify and prevent a patient safety issue. Help make patient care safer by learning to apply these steps.

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Key steps in the analysis of health data - Interpret health data - turn information into new insights | Coursera

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Video created by The University of Sydney for the course "Using clinical health data for better healthcare". In this module, we look at the use of health data and basic data analysis to inform and improve decision making and practice. We explore ...

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1) What is Big Data in healthcare and how is this data used?

2) Do you think research and data analysis are important in nursing practice? Explain your answer. What are some examples of the ways you analyze data at work to make clinical decisions about patient care?

3) How has the evolving development in healthcare informatics and technology positively impacted patient outcomes? Name at least two.

4) Name a professional careers in nursing healthcare informatics, the education and training required for this profession, and the professional responsibilities of this role. Do you think you would want a career in nursing informatics? Why or why not?

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Data Analysis, Research, and Informatics in Healthcare Paper

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Research and Data Analysis: Make Sense of a Data Report

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Research and Data Analysis: Make Sense of a Data Report

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Need an idea for your next research paper? Data analysis is a crucial skill for effective research. Data reports from the EHR are excellent resources for a research project!

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This Course Video Transcript

Digital health is rapidly being realised as the future of healthcare. While this is placing emphasis on the input of quality health data in digital records and systems, the delivery of safe and quality healthcare relies not only on the input of data, but also the ability to access and derive meaning from data to generate evidence, inform decision making and drive better health outcomes. This course provides insight into the use of healthcare data, including an overview of best practices and the practical realities of obtaining useful information from digital health systems via the understanding of the fundamental concepts of health data analytics. Learners will understand why data quality is essential in modern healthcare, as they are guided through various stages of the data life cycle, starting with the generation of quality health data, through to discovering patterns and extracting knowledge from health data using common methodologies and tools in the basic analysis, visualisation and communication of health data. In doing so, learners explore current healthcare delivery contexts, and future and emerging digital health data systems and applications that are rapidly becoming tomorrow's reality. On completion of this course, you will be able to: 1. Identify digital health technologies, health data sources, and the evolving roles of health workforce in digital health environments 2. Understand key health data concepts and terminology, including the significance of data integrity and stakeholder roles in the data life cycle 3. Use health data and basic data analysis to inform and improve decision making and practice. 4. Apply effective methods of communication of health data to facilitate safe and quality care. During this course, you will interact with learning content contributed by: * Digital Health Cooperative Research Centre * Australian Digital Health Agency * eHealth NSW * Sydney Local Health District * The NSW Ministry of Health * Health Education and Training Institute * Clinical Excellence Commission * Chris O'Brien Lifehouse * Monash Partners / Australian Health Research Alliance * Australian Research Data Commons * Justice Health & Forensic Mental Health Network * South Eastern Sydney Local Health District * Western Sydney Local Health District * Westmead Breast Cancer Institute * Agency for Clinical Innovation * Western NSW Local Health District * Sydney Children's Hospital Network This course is a collaborative venture between NSW Health, the University of Sydney and the Digital Health Cooperative Research Centre, including dedicated resources from eHealth NSW, Health Education and Training Institute, and the Research in Implementation Science & eHealth group. While many learning resources and case examples are drawn from the NSW Health service context, this course has relevance for all existing and future health workforce, regardless of role or work context. Note: Materials used are for learning purposes and content may not reflect your organisation's policies. When working with data, make sure you act within the guidelines and policies of your organisation.

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Interpret health data - turn information into new insights

In this module, we look at the use of health data and basic data analysis to inform and improve decision making and practice. We explore some common methods and tools used in the analysis of health data. There is an opportunity to develop a data query and to practice working with data. Note: Materials used are for learning purposes and content may not reflect your organisation's policies. When working with data, make sure you act within the guidelines and policies of your organisation.

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Using tools within the eMR such as data reports, can help us gain information and new insights about our health services and patients. Personally, I regularly use data reports from the eMR to plan and evaluate quality improvement activities, produce periodic reports, reflect on my personal professional performance or that of the team, and sometimes just because I'm curious about something in our practice that data trends may help me to answer. In this session, an eMR data report example is used to explore how to interpret and establish the validity of a data report and how do we evaluate data to identify trends, gaps, opportunities, and potential care trajectories for an individual or cohort of individuals. I'm often approached by staff who have clinical questions they want answered with data from the eMR. These questions could be as simple as, how many cesarean sections were done in the district last year? Sometimes they're more complicated. How many cesarean sections were done in the district last year to women with gestational diabetes? In many instances, the answers they seek are available to them via their eMR. Before we continue, it's helpful for us to briefly remind ourselves of the multiple potential issues we may need to consider with regards to data quality. For example, is all the information being collected electronically in the eMR or is some information still being captured on paper records? We refer to this as a hybrid process. If the information is captured on both paper and electronic sources, we'd have to consider the potential impacts, say, on the averages etc. We need to think about the various staff contributing to the record. What abbreviations do they use? Do we all use the same terminology? Is it a Caesar or other? What about the impact of data entry error such as incorrect numbers being entered or the incorrect spelling of a name or condition? Consider the needs of filtering in Excel and apply the principles of standardization to the workforce entering the information, to have the best chance of clean data at the point of report extraction. If the query is one that is likely to have been previously asked, a report may already have been built to answer the question. For example, if I wanted to know the fall risk assessment compliance rate in my hospital, I would probably find a report that has already been built to answer that question, given our organization has been tracking that data for some time. Let's take a look. In our eMR, these reports are located in the "Explorer Menu", and this is a component available to all users. If you're using a different eMR, it is worthwhile taking the time to identify equivalent reporting features available to you. The number and types of reports vary from system to system and include information about patient movement through the hospital or district, commonly reported diseases and their management, and audits for service provision and safety. Reports can be utilized at a ward, facility, district, state, or national level. By entering a few simple search parameters, such as location and date range, health staff can look at existing data reports that satisfy their information needs. Here we've highlighted the report that relates to full risk assessment, it helps to familiarize yourself with the script of the report. In this instance, the script is located at the bottom of the report. This tells us what objective data has been questioned out of the database. In this case, the medical reference number, patient name, date of birth etc. This is where you can check if the report reflects the information requirements to satisfy your query. For the purpose of this demonstration, we're going to look at the weight change report. This report shows patient weight on admission, and then subsequent weights across their inpatient stay. This can be used to monitor changes to patient weight, including significant weight loss as a sign of malnutrition. The report can facilitate decision-making such as the initiation or referral of dietitian assessment and intervention. Let's build and test a report. First we can decide on the information to capture in the report, how broad are our data requirements to satisfy our information needs? There are both pros and cons of casting the net wide. In this instance, there is no date range option because the report only shows patients who have not been discharged. We can confirm this by the report description. We will select all wards at Royal Prince Alfred Hospital and have it exportable to Excel for filtering. Once we've obtained the report, we need to test it to determine if it represents the correct data in the right way in order to satisfy our information requirements. Like all decision-making in practice, we need to apply our professional judgment and critical appraisal skills when considering any information that can influence our decision-making. We can't take it at face value that the available information is correct and will meet our needs. We need to test the report to determine the validity and appropriateness of the information that has been captured by the system. Let's test this weight variance report. These are the headers at the top of each column that help you to identify the type of data contained within. Because we are conducting this example in the live environment, we have hidden the actual data to ensure patient confidentiality. We will have the opportunity to review de-identified data soon. Here's where we start to test the report by cross checking the data, asking questions such as, does it capture the desired date range? Does it represent the location in question such as specific wards, or collections of wards within our facilities? Does the data look abnormal in any way that would cause you to question the integrity of the data? For example, does the report indicate your patient's weight as 150 kilos when you know your patient is more likely 50? We could stop there and have a report summarizing our specified range of raw data, however, if we really want to make sense of the data, further analysis is required. So to analyze the data, we're going to copy and paste it into a secure spreadsheet. We then want to filter out data in a table. Now we can filter by the length of stay for example, I only want to see patients with a length of stay more than 90 days. This shows us some interesting data. We can see we have five patients and some patients did not have an admission weight recorded. This column shows you the difference between their latest weight and their admission weight as a percentage. For me this raises a couple of key questions. Why have admission weights not been recorded? Do we need to initiate quality improvement activities to ensure that staff are compliant with policy regarding mandatory admission weights? Is the data for this patient accurate? Has this patient really had an 18 percent weight gain since admission? Do we need to conduct data quality checks? From this spreadsheet, we could ascertain the average length of stay, weight gained and weight lost. Additionally, this data could be used to influence our clinical decision-making such as a referral for dietetics review, which could ultimately impact patient outcomes. By this case example, we have explored, the process of reading a data report, how to establish validity by cross-checking raw data against data reports, and the importance and process of reviewing a report for potential anomalies, gaps, trends, and patterns in reported data.

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Make sense of a data report - Interpret health data - turn information into new insights | Coursera

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Video created by The University of Sydney for the course "Using clinical health data for better healthcare". In this module, we look at the use of health data and basic data analysis to inform and improve decision making and practice. We explore ...