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Background

The rapidly increasing computerization of health care has produced benefits for clinicians and patients. Yet the integration of technology into medicine has been anything but smooth, and as newer and more sophisticated devices have been added to the clinical environment, clinicians' workflows have been affected in unanticipated ways. These fundamental shifts have resulted in new threats to patient safety-a cruel irony given that technological solutions have been promoted for many years as the most promising solution to medical errors.

Most health care technologies-be they computerized provider order entry systems (CPOE), smart intravenous infusion pumps, or cardiac monitoring devices-provide auditory or visual warnings to clinicians to prevent or act on unsafe situations. These warnings are well intended and in isolation may be helpful. However, in the current highly computerized clinical environment, an individual clinician interacts with many different alert-generating devices-meaning that every day, clinicians are on the receiving end of a staggering number of alerts. A 2014 study found that the physiologic monitors in an academic hospital's 66 adult intensive care unit beds generated more than 2 million alerts in one month, translating to 187 warnings per patient per day. In a study in the Veterans Affairs primary care, clinicians received more than 100 alerts per day.

The term alert fatigue describes how busy workers (in the case of health care, clinicians) become desensitized to safety alerts, and as a result ignore or fail to respond appropriately to such warnings. This phenomenon occurs because of the sheer number of alerts, and it is compounded by the fact that the vast majority of alerts generated by CPOE systems (and other health care technologies) are clinically inconsequential-meaning that in most cases, clinicians should ignore them. The problem is that clinicians then ignore both the bothersome, clinically meaningless alarms and the critical alerts that warn of impending serious patient harm. In essence, a proliferation of alerts that are intended to improve safety actually results in a paradoxical increase in the chance patients will be harmed. Although little discussed prior to the widespread use of electronic medical records, alert fatigue is now recognized as a major unintended consequence of the computerization of health care and a significant patient safety hazard.

Effect of alert fatigue on patient safety

Much of the literature on alert fatigue derives from studies of CPOE and clinical decision support systems, in which alerts are provided to warn of potentially harmful drug-drug interactions or incorrect medication doses. These studies consistently show three main findings:

Alerts are only modestly effective at best. A systematic review of computerized reminders found only minor improvements in targeted processes of care, and, while CPOE systems have been shown to markedly decrease prescribing errors, this can largely be ascribed to their ability to standardize drug doses, provide decision support, and eliminate errors from poor handwriting or incorrect transcriptions.

Alert fatigue is common. Clinicians generally override the vast majority of CPOE warnings, even "critical" alerts that warn of potentially severe harm. There is less literature on other types of warnings, but it is likely that rates of overriding or ignoring warnings in other settings are also high.

Alert fatigue increases with growing exposure to alerts and heavier use of CPOE systems. This finding is intuitive, but also raises the important implication that without system redesign, the safety consequences of alert fatigue will likely become more serious over time.

Although there are few studies that quantify adverse events related to alert fatigue, this phenomenon has been implicated as a significant cause in several high-profile errors. A 2011 Boston Globe investigation identified more than 200 deaths over a 5-year period attributable to failure to appropriately heed alarms from physiologic monitoring systems. A recent book by a prominent patient safety leader details how a hospitalized teenager received a 38-fold overdose of an antibiotic, in large part because the ordering physician had been advised by colleagues to "just ignore the alerts."

Current context

Although only recently recognized, alert fatigue (and the unintended consequences of the computerization of health care) has become a high profile patient safety issue. The Joint Commission released a sentinel event alert in April 2015 calling for health care organizations to pay close attention to information technology as a safety issue. In order to mitigate these consequences-including alert fatigue-The Joint Commission recommended improving the culture of safety by creating a shared sense of responsibility between users and developers, paying careful attention to safe IT implementation, and engaging leadership to provide oversight of health IT planning, implementation, and evaluation.

There is intense interest in developing specific methods to combat alert fatigue, but as yet, there is no consensus on the optimal approaches. Solving alert fatigue will require use of the principles of human factors engineering as well as those of informatics, as the problem fundamentally arises from both the technology itself and how busy human beings interact with the technology. A WebM&M provided several suggestions on how to minimize alert fatigue in CPOE systems:

Increase alert specificity by reducing or eliminating clinically inconsequential alerts.

Tailor alerts to patient characteristics and critical integrated clusters of physiologic indicators. For example, incorporate renal function test results into the alert system so that alerts for nephrotoxic medications are triggered only for patients at high risk.

Tier alerts according to severity. Warnings could be presented in different ways, in order to key clinicians to alerts that are more clinically consequential.

Make only high-level (severe) alerts interruptive.

Apply human factors principles when designing alerts (e.g., format, content, legibility, and color of alerts).

A quality improvement program in the Veterans Affairs system that incorporated the above principles and provided primary care physicians with education on managing alerts achieved a small but meaningful reduction in alerts.

One limitation to addressing alert fatigue pertains to the legal consequences of removing alerts. A recent commentary pointed out that system developers have thus far been reluctant to remove alerts for fear of being held liable if patients were harmed in the absence of a warning. There has been progress toward developing guidelines for high-severity alerts (which warn of significant risk of harm and should be retained) and low-severity alerts (less clinically consequential warnings, which could be made non-interruptive or removed entirely).

In solving the problem of alert fatigue, health care will need to look to examples from other industries. The aviation industry offers a sharp contrast to health care, because cockpit technology is rigorously designed to provide only highly consequential alerts to pilots, minimizing minor alerts in order to allow pilots to maintain situational awareness. This use of human factors engineering and deep attention to the experience of the end-user has thus far been lacking in health care technology design.

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Alert fatigue (alarm fatigue) is when healthcare workers become desensitized to electronic safety alerts. Alert fatigue can lead to medical errors and adverse events.

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(Technology Informatics Guiding Education Reform)
-designed to address set of skills needed by nurses
-Focused on helping the nursing profession adopt informatics tools
-makes healthcare safer and more efficient

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-Improve quality of healthcare
-prevent medical erros
-reduces healthcare costs
-increases administrative efficiences
-expands access to affordable healthcare

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Health information technology (health IT) involves the processing, storage, and exchange of health information in an electronic environment. Widespread use of health IT within the health care industry will improve the quality of health care, prevent medical errors, reduce health care costs, increase administrative efficiencies, decrease paperwork, and expand access to affordable health care. It is imperative that the privacy and security of electronic health information be ensured as this information is maintained and transmitted electronically.

Cloud Computing

With the proliferation and widespread adoption of cloud computing solutions, HIPAA covered entities and business associates are questioning whether and how they can take advantage of cloud computing while complying with regulations protecting the privacy and security of electronic protected health information (ePHI). HHS has developed guidance to assist such entities, including cloud services providers (CSPs), in understanding their HIPAA obligations.

HIPAA Privacy Components of the Privacy and Security Toolkit

The materials below are the HIPAA privacy components of the Privacy and Security Toolkit developed in conjunction with the Office of the National Coordinator. The Privacy and Security Toolkit implements the principles in The Nationwide Privacy and Security Framework for Electronic Exchange of Individually Identifiable Health Information (Privacy and Security Framework). These guidance documents discuss how the Privacy Rule can facilitate the electronic exchange of health information.
Learn more about the Privacy and Security Framework and view other documents in the Privacy and Security Toolkit, as well as other health information technology resources.

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Health Information Exchange | HealthIT.gov

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

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What is Nursing Informatics?

Nursing informatics is the “specialty that integrates nursing science with multiple information and analytical sciences to identify, define, manage and communicate data, information, knowledge, and wisdom in nursing practice” (HIMSS).
Examples;
-Patient portals, EMR, Telehealth, EPIC, etc.

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Jonathan Mack PhD, RN-BC, NP

Director of Health Care and Nursing Informatics and Health Systems Leadership

What is Health Informatics?

Health care informatics is, at a very basic level, the use of data and information technology to meet the quadruple aim goals of health care: improved clinical outcomes, lower cost of care, improved experiences for both patients and providers, and to provide better care in a more cost-effective manner. It also has important applications for health care professionals, public health specialists, insurance industry professionals, medical researchers, health care professionals administrators, biotech companies and more.

While there are many competing definitions of health informatics, two things are certain:

Health informatics is revolutionizing the practice of health care, from caring for individual patients to improving the health of entire populations.

Health informatics is one of the nation's fastest-growing job sectors, with well-paying career opportunities for those with the right experience and training.

According to the American Medical Informatics Association (AMIA) , "health informatics applies principles of computer and information science to the advancement of life sciences research, health professions education, public health, and patient care. This multidisciplinary and integrative field focuses on health information technologies (HIT), and involves the computer, cognitive, and social sciences."

In the continually evolving field of health informatics, the goal is to use this data and the insights it produces to:

Improve patient care at the individual and clinical level

Improve the health of entire populations (for example, by using data to predict and even prevent outbreaks of disease in a particular geographical area)

Enable health organizations to deliver care more cost effectively

The widespread use of health care technologies and data management has allowed many health care specialties to utilize health informatics in ways that work best for them, meaning there is no "one size fits all" understanding of the field. Here is a closer look at some of the primary subdisciplines that fall under the umbrella term "health informatics."

Applied Clinical Informatics

AMIA classifies clinical informatics as "the application of informatics and information technology to deliver healthcare services." This classification of health informatics refers directly to the tools, technologies and tactics used directly by clinicians in their delivery of care. Clinical informatics can refer to using information science in connection with anything from electronic medical records to diagnostic tools like radiology and pathology. Medscape.com calls clinical informatics "a growing medical subspecialty devoted to using data to improve patient care."

According to the AMIA, clinical informatics now affects a broad range of health professionals (physicians, nurses, dentists, pharmacists, etc.) and covers a diverse group of topics, including:

Clinical decision support and documentation

Population health and High-Reliability health care

Health data analytics and data visualization

Visual imaging (radiological, pathological, dermatological, ophthalmological, etc.)

Provider order entry systems

Complex socio-technical systems design, implementation and adoption

Clinical informatics is now an officially recognized medical subspecialty that requires practitioners to pass a certification exam administered by the American Board of Preventive Medicine. A new AMIA Health Informatics Certification is also being developed for applied health professionals who are not eligible for the Clinical Informatics Subspecialty, reflecting the growing importance of contributions being made by informaticists, and a national recognition of the informatics delineation of practice.

Nursing Informatics

According to HIMSS (the Healthcare Information and Management Systems Society) , nursing informatics is "the specialty that integrates nursing science with multiple information management and analytical sciences to identify, define, manage, and communicate data, information, knowledge, and wisdom in nursing practice." Much like clinical informatics, nursing informatics is aimed at supporting clinical caregivers, in this case nurses, in their practice and in their ability to support and care for patients.

Today, the field of informatics holds great potential for nurses, especially those who have worked with health technology like electronic health records, remote monitoring/telehealth and clinical decision support tools. Knowledge and experience in informatics can position nurses both for clinical leadership roles and also for pathways that involve transitioning away from the patient bedside.

Public Health Informatics

In its application to public health, informatics takes on a slightly different, but similarly data-focused definition. The Public Health Informatics Institute defines this specialization as a discipline that "assures that the right technologies are used to improve timely delivery of quality data and assists data-driven decision making. It builds bridges across siloed public health work areas by 'translating' between these communities, creating opportunities for interoperable information pathways. Ultimately, public health informatics empowers disease interventions and prevention - leading to better health of individuals and the community in which they live."

Translational Bioinformatics

Outside of direct patient care, there exists a level of informatics known as translational bioinformatics , which AMIA defines as "the development of storage, analytic, and interpretive methods to optimize the transformation of increasingly voluminous biomedical data, and genomic data, into proactive, predictive, preventive, and participatory health." Primarily the domain of doctoral-level researchers, this area of health informatics is more focused on the research aimed at developing new techniques that apply to the entire health care field.

Health Informatics vs. Health Information Management - What's the Difference?

For the layman, it can be easy to confuse these two distinct but closely related fields. At the risk of oversimplification, health information management refers to the practice of acquiring, analyzing and protecting digital and traditional medical information vital to providing quality patient care.

Health informatics professionals are responsible for ensuring that "the right information is available when and where it's needed, while at the same time making sure it's the highest quality data, it's confidential and it's secure," according to Steve Wretling, chief technology and innovation officer for HIMSS.
[RELATED READING] How Health Informatics is Shaping the Future of Health Information Management >>
Future Implications of Health Informatics

Health informatics has transformed health care in ways that have significant implications across clinical care, health management, disease prevention and health policy. While some of those results cannot be foreseen, there are indicators of how health informatics is revolutionizing health care across the following disciplines.

Clinical Implications

At the clinical level, informatics is being used to improve both individual patient care and the health of entire populations. Health care informatics is revolutionizing how clinicians diagnose and manage patient care, thereby ensuring that care is delivered safely and is tailored to the individual. On a larger scale, informatics is improving the operational efficiency of health care delivery systems through identification of trends, outcome prediction and identifying care gaps and disparities between and among groups of patients.

Health care informatics is contributing to medical advancement and health care innovation by providing not only data, but the actionable information to improve processes, clinical outcomes and the overall experience for patients and providers. Because of these advancements, major tech firms have now focused on health care informatics as an area for investment and innovation.

Information Governance Implications

Policy implications of health care informatics will deal with privacy standards and data sharing regulations. As the United States and the world still debate how to best handle data privacy and security, the health care industry itself is figuring out how it can advance public health initiatives without violating patient privacy standards. Going forward, much will depend on what policymakers decide to prioritize in the name of worldwide health initiatives.

As technology has become an integral tool in providing high-quality health care, there is increasing emphasis on effectively and securely managing the troves of data that now give providers enhanced insights into every aspect of care. With this amount of connected technology comes questions about levels of access, and how much is too much. Data privacy standards will have to be adjusted and monitored as the capabilities and usage of health care informatics continue to expand in the years to come.

Working in the Field of Health Informatics

As more and more organizations across the health care spectrum look to reap the benefits of informatics, there is a growing need for employees who possess both clinical experience as well as proficiency in information technology or informatics. This has created high demand for qualified health informatics professionals.

A search for "health informatics" job openings on platforms such as "LinkedIn" will result in several thousand listings at a wide variety of companies across the nation. Listings for positions may include the following:

Clinical Informaticist

Health (or Clinical) Informatics Analyst (I,II, III, IV, Lead, Specialist, Manager)

Health (or Clinical) Informatics Technician, Specialist, Product Manager,

Health (or Clinical) Informatics Lecturer, Professor

Electronic Medical Record Trainer

Director, Clinical Informatics

Chief Medical Information Officer (CMIO), Chief Nursing Information Officer (CNIO)

Healthcare Chief Information Officer (CIO)

Healthcare Informatics Security and Data Manager

Health Informatics Consultant

[RELATED READING] Health Informatics Jobs and Salaries >>
With strong demand and great pay, it is easy to see why a career in health informatics is so appealing. Opportunities are as diverse as the world of health care itself - from public health, veterinary, dental and clinical care to nursing, biotech, software development, telemedicine and the insurance industry. The number of practical applications for health informatics continues to grow, which means that diverse career opportunities will continue to expand as well.

For that reason, many current and aspiring health informatics professionals are taking advantage of specialized master's degree programs that build on their work experience and knowledge of the health care field while refining their programmatic, technical, analytical and leadership skills. Programs like the M.S. in Health Care Informatics offered at University of San Diego offer students a pathway to earn their degree while continuing to work full time, through flexible online courses, as well as evening classes offered on campus.
[RELATED] Opportunities in Health Informatics are Vast for Those with a Master's Degree >>
Health Informatics FAQs
Q: What do health care informaticists do?
A: Health informaticists use their training and knowledge of health care, information systems, databases and information technology to gather, store, analyze and manage patient data and medical records. Informaticists can use this data for a broad range of applications, including to analyze trends, improve clinical workflow, improve overall quality of care and individual patient outcomes and even the health of entire populations.
Q: What are examples of practical applications of health care informatics?
A: Health informatics is being used across the health care field, from research to front-line care. Working in this field can require juggling many different responsibilities, but common day-to-day tasks include

Developing information systems/processes that improve the quality and efficiency of care

Building, optimizing, and maintaining electronic health record (EHR) systems

Designing, developing and evaluating emerging technologies

Providing data management, analytical, and leadership skills

Designing and maintaining medical databases, computer networks and applications

Evaluating the impact of IT on clinical workflow and outcomes

Developing data analysis and utilization protocols

Developing privacy and security policies and systems

More specifically, two unique applications of this specialization include projects for battling the opioid epidemic and combating sepsis and pediatric asthma. For more examples of how health informatics is being used to solve real-world health challenges, read " 5 Exciting Health Care Informatics Projects ."
Q: Is health care informatics a good profession to enter?
A: Yes. In examining the career outlook for medical and health services managers, a job category that is closely connected to health informatics, the U.S. Bureau of Labor Statistics projects a 32% job growth by 2029; a rate much faster than the average for other occupations. The U.S. Bureau of Labor Statistics also projected annual salaries of $100,980 for people working in this field. The COVID-19 pandemic has brought additional attention to the field, which is likely to further increase employer demand. Moreover, job surveys consistently indicate that professionals working in the field have a high level of job satisfaction.
Q: How can I build a strong health care informatics resume?
A: Those who aspire to work in health informatics should have high-quality interpersonal skills, communication skills, problem solving skills, project management skills, knowledge of health data systems, EHR expertise, data analytics knowledge, programming knowledge, knowledge about the health care industry, and an understanding of clinical quality and value-based care . From there, many consider additional training, in the form of advanced coursework or a master's degree, to gain additional health care and IT knowledge as well as to develop critical health administration, project management and leadership skills.
Q: How does health informatics improve the quality and delivery of health care?
A: Broadly speaking, informatics is being used to improve both individual patient care and the health of entire populations of people. At the micro level, health care informatics is revolutionizing how clinicians diagnose and manage patient care, thereby ensuring care is delivered safely and is tailored to the individual. At the macro level, informatics is improving the operational efficiency of health care delivery systems through identification of trends, outcome prediction and identifying care gaps and disparities between and among groups of patients.

Health care informatics is contributing to medical advancement and health care innovation by providing not only data, but the actionable information to improve processes, clinical outcomes and the overall experience for patients and providers. Because of these advancements, major tech firms have now focused on health care informatics as an area for investment and innovation.
Q: How does informatics prevent health care fraud?
A: While its primary intention is not to prevent fraud, some informatics strategies, like data analytics, can support fraud detection. According to Health Payer Intelligence , analytics platforms and visualization dashboards can be used to detect patterns in provider fraud, visualize fraud activity to determine the prevalence of fraud and help audit claims information.
Q: How does health care informatics improve patient safety?
A: Health care informatics analyzes and leverages real patient data from a myriad of sources and practices - such as electronic medical records, information sharing and medical record transparency - to improve diagnostic accuracy. Utilizing authentic patient and health care data and patterns to inform care choices improves the quality of care and ultimately patient safety.
Q: How is health informatics essential to the handling and processing of information in health care?
A: Health informatics makes it possible for the health care industry to handle and maximize the massive volume of data that exists today. Valuable medical data is continually generated in today's hyper-connected world (think medical devices, patient records, insurance claims, billing departments, and even wearables). According to IBM Watson, the amount of medical data being generated and stored is now projected to double every 73 days, with each person generating enough health data in their lifetime to fill 300 million books. With health informatics, health care providers, researchers and clinicians can extract value from that data and improve care.
Q: What is nursing informatics and how does it affect health care?
A: According to HIMSS, nursing informatics is "the specialty that integrates nursing science with multiple information management and analytical sciences to identify, define, manage, and communicate data, information, knowledge, and wisdom in nursing practice." This field of study is aimed at supporting clinical caregivers, in this case nurses, in their practice and in their ability to support and care for patients. Knowledge and experience in informatics can position nurses both for clinical leadership roles and also for pathways that involve transitioning away from the patient bedside.

Jonathan Mack PhD, RN-BC, NP

Director of Health Care and Nursing Informatics and Health Systems Leadership

Dr. Mack earned his R.N. Diploma in 1983 from Los Angeles County Medical Center, School of Nursing and an A.A. degree from Los Angeles City College. Dr. Mack completed his Master of Science in Nursing degree in 1994, with a certificate as a Nurse Practitioner from Simmons College in Boston, MA.

Dr. Mack continued his graduate education and completed his Ph.D. in Nursing at the University of San Diego Hahn School of Nursing and Health Science. Dr. Mack is board certified in Nursing Informatics from the American Nurses Credentialing Center and holds an NP designation from the California Board of Registered Nursing.

Advance Your Career in the Rapidly Growing Field of Health Informatics

Earn your Master's degree in Health Care Informatics

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Jonathan Mack PhD, RN-BC, NP Director of Health Care and Nursing Informatics and Health Systems Leadership Health care informatics is, at a very basic level, the use of data and information technology to meet the quadruple aim goals of health care: improved clinical outcomes, lower cost of care, improved experiences for both patients and providers, and to provide better care in a more cost-effective manner.

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I found this video very informative. Explained the concept of Big Data very well and also helped put into perspective how we use it on a daily basis.

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How Big Data in Healthcare Influences Patient Outcomes

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How Big Data in Healthcare Influences Patient Outcomes

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Big Data in Health Care and Patient Outcomes

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What is big data in health care? How does big data improve patient outcomes? Learn more about the benefits of big data in health care for patients and providers.

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The DIKW model or DIKW pyramid is an often used method, with roots in knowledge management, to explain the ways we move from data (the 'D') to information (I), knowledge (K) and wisdom (W) with a component of actions and decisions.
Essentially that's what we do in digital transformation. And with Industry 4.0, IoT and AI/ML the decisions/actions can also be (semi-)autonomous, although everything depends on the nature and purpose of the data.

Simply put, DIKW is a model to look at various ways of extracting insights and value from all sorts of data: big data, small data, smart data, fast data, slow data, unstructured data, it doesn't matter; we want outcomes, the 'actionable intelligence'.
The DIKW model is often depicted as a hierarchical model in the shape of a pyramid and also known as the data-information-knowledge-wisdom hierarchy, among others.

The DIKW model: usage and limitations
As is the case with all models, DIKW has its limits. You'll notice the DIKW model is quite linear and expresses a logical consequence of steps and stages with information being a contextualized 'progression' of data as it get more meaning.
Reality is different. Knowledge is much more than just a next stage of information. And solving data management and information management challenges these days looks far more complex with the proliferation of data sources and types. Nevertheless, the DIKW model is still used in many forms and shapes to look at the extraction of value and meaning of data and information. It's a simple and thus strong metaphor.

One of the main criticisms of the model is indeed that it's a hierarchical one and misses several crucial aspects of knowledge and the new data and information reality in this age of big data, APIs and ever more data and ways to capture them and turn them into action, sometimes bypassing the steps in DIKW ( think about self-learning systems). Some professionals also say it underestimates the value of data (at the bottom of the pyramid) and raw data. The new oil, remember. Moreover, we gather data for a reason. So, there is already wisdom and decisions going on before the capture starts.

But still, the essence stays the same. Just look at what we do with data lakes and turning data through big data analytics into decisions and actions.

If you want to learn all about the DIKW model, there is an excellent paper in the Journal of Information Science, entitled ' The wisdom hierarchy: representations of the DIKW hierarchy' (PDF) and written by Jennifer Rowley of the Bangor Business School.

It's an interesting paper as Jennifer revisits the DIKW hierarchy, a.k.a. 'data-information-knowledge-wisdom hierarchy', 'Knowledge Hierarchy', 'Information Hierarchy' and, almost done, 'Knowledge Pyramid'. Given the many names it received you can imagine the DKIW Pyramid has always been very popular in the broader space of information management - and beyond.
Where is the wisdom we have lost in knowledge?
As you can imagine, the DIKW Pyramid - as all models or ways of looking at things in a more or less structured way - has been discussed and looked upon from various angles with ample changes to the precise terms that have been used, sometimes in a specific context. Where is the knowledge we have lost in information?
Beyond wisdom: enlightenment

Some suggested to omit wisdom, others debated the exact definitions and the relationships between them and a few wanted to add a dimension of truth and moral sense to it, with the addition of something even higher than wisdom: "enlightenment".
In her paper, Jennifer Rowley mapped the DKIW model to different types of information management systems.
While it's very interesting to discuss about things such as truth, right and wrong, enlightenment and so on, that's not our purpose here. The long history of DIKW and views on it have made it easier to illustrate this article, that is for sure.

We use DIKW as one of several ways to define, illustrate and explain the various forms of data, information etc. in a business, transformation and customer/stakeholder perspective. We have nothing against enlightenment as a step beyond wisdom, usually defined as 'evaluated understanding' or 'knowing why', which we would then call truly understanding the purpose of information in a context of what people need and want, beyond the more factual knowledge. The enlightened business? Who knows. Another step that's been added now and then - as you can see below - is 'decisions'. And why not?
What we're most interested in, is the action part. We've talked about 'actionable data' and 'actionable information' before and Jennifer Rowley refers in her paper to knowledge as being actionable information, based on the work of E.M Awad and H.M. Ghaziri, more specifically their 2004 book Knowledge Management.

Data is related with transaction processing systems.

Information with, indeed, information management systems.

Knowledge with decision support systems.

Wisdom with expert systems.

What matters: actions and decisions in DKIW

Action. Decisions. That's what we need. Because without action there is little sense in gathering, capturing, understanding, leveraging, storing and even talking about data, information and knowledge.

We mean action as in business and customer outcomes, creating value in an informed way. But of course in the bigger picture, action can also simply be learning or anything else.
From the poem by T.S. Eliot, The Rock:
Where is the wisdom we have lost in knowledge?
Where is the knowledge we have lost in information?
You can see more variations of DIKW in our article ' From data to value '; there's even a big data DIKW model.

Fun fact to know: although a 1989 paper from Russel L. Ackoff, entitled 'From data to wisdom' is often cited in the context of the DIKW hierarchy, there seems to be a more poetic aspect about it too.

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The DIKW model for knowledge management and data value extraction

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The DIKW model or DIKW pyramid is an often used method to depict and explain how we extract information and value from data. A guide.

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A handful of analytic frameworks for quality assessment have guided measure development initiatives in the public and private sectors. One of the most influential is the framework put forth by the Institute of Medicine (IOM), which includes the following six aims for the healthcare system.

Safe: Avoiding harm to patients from the care that is intended to help them.

Effective: Providing services based on scientific knowledge to all who could benefit and refraining from providing services to those not likely to benefit (avoiding underuse and misuse, respectively).

Patient-centered: Providing care that is respectful of and responsive to individual patient preferences, needs, and values and ensuring that patient values guide all clinical decisions.

Timely: Reducing waits and sometimes harmful delays for both those who receive and those who give care.

Efficient: Avoiding waste, including waste of equipment, supplies, ideas, and energy.

Equitable: Providing care that does not vary in quality because of personal characteristics such as gender, ethnicity, geographic location, and socioeconomic status.

Existing measures address some domains more extensively than others. The vast majority of measures assess effectiveness and safety. Some capture timeliness and patient-centeredness. Fewer measures focus on efficiency and equity of care, but attention to those domains has been growing.

Frameworks like the IOM domains also make it easier for consumers to grasp the meaning and relevance of quality measures. Studies have shown that providing consumers with a framework for understanding quality helps them value a broader range of quality indicators. For example, when consumers are given a brief, understandable explanation of safe, effective, and patient-centered care, they view all three categories as important. Further, when measures are grouped into user-friendly versions of those three IOM domains, consumers can see the meaning of the measures more clearly and understand how they relate to their own concerns about their care.

Learn more about Organizing Measures To Reduce Information Overload.

Learn more about selecting and reporting measures within categories that consumers understand: Organizing Measures by Quality Domain.

Institute of Medicine (IOM). Crossing the Quality Chasm: A New Health System for the 21st Century. Washington, D.C: National Academy Press; 2001.
Institute of Medicine (IOM). Performance Measurement: Accelerating Improvement. Washington, D.C: National Academy Press; 2005.
Hibbard JH, Pawlson LG. Why Not Give Consumers a Framework for Understanding Quality? Joint Commission Journal on Quality Improvement 2004 June. 30(6); 347-351.

Also in "Select Measures to Report"

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A handful of analytic frameworks for quality assessment have guided measure development initiatives in the public and private sectors. One of the most influential is the framework put forth by the Institute of Medicine (IOM), which includes the following six aims for the healthcare system.

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The DIKW acronym has worked into the rotation from knowledge management. It demonstrates how the deep understanding of the subject emerges, passing through 4 qualitative stages: "D" - data, "I" - information, "K" - knowledge and "W" - wisdom. The latter is, hence, the ultimate stage of any cognition process.

"Data → Information → Knowledge → Wisdom" hierarchy, image source
This approach is philosophical, in a way, because it emphasizes the difference between categories. Facts alone are not the information, nor the knowledge is wisdom. Look at these interpretations:

Data is numbers and facts.
A rainy day is an objective reality. This is something everybody can see and even feel - a fact. Without context and interpretation, the fact that it's raining is useless, as a whole. Now, let's suppose the airport dispatcher has to decide whether to correct today's schedule or not. He or she will probably collect data about the rain itself, as well as general flights' characteristics: All these form the context for coming up with a specific decision. That is, raw facts are being transformed into information.
The data is what it all starts with, it is undeniable and deprived of the context. The data doesn't make much sense on its own. Look at the following DIKW examples with the rainy weather:

Information is the data enhanced with context, the purpose of analysis and analytical methods.

Think of stock exchanges' quotes tables. The numbers alone show as much as nothing, right? However, if you know units and reporting time frames, you'll probably be able to make some calculations and draw conclusions about biddings:

who lead the stock market

who was the market outsider

who had the highest growth ratio

and who stagnated.

Investors use this information to make their day-to-day decisions about whether to escalate the portfolio or sell stocks. And real professionals use it to check their assumptions and make far-reaching predictions. In other words, they acquire the knowledge.

Knowledge comes up from the information that has been accumulated over time. These are axioms, theories, concepts, or definitions.

Iron laws of Economics, such as the supply-and-demand model, have derived from the identification of patterns in both consumers' and seller's long-term behavior.

Wisdom is what the subject eventually comes to after having worked with information for a long time and having received a sufficient amount of knowledge.

Being aware of, comprehensive, mindful - these are all synonyms of being wise. Wisdom gives an ability to predict some events' scenarios, based on past experience. Referring to the example with the stock exchange: outstanding investors, like Warren Buffett or Peter Lynch, turned out to be more successful thanks to both the colossal experience and bidding wisdom, in a sense.

Now that you know what is data, information, knowledge and wisdom, let's move on to how it can be used for daily and long-term decision-making in practice.

DIKW pyramid in decision-making

The DIKW hierarchy to decision-making seems to be simple - but only on the surface. In theory, managers collect data, the information drives their decisions, and quality assurance provides knowledge and evolves to wisdom. The reality is more pragmatic, however:

people can be too overwhelmed with the data and stuck the bottom of the pyramid

there may be too little or too much time spent to move onto the next level

some steps, willfully or not, might be skipped.

How raw data is transformed into business decision, image source
But the main threat appears when analytical and decision-making processes go in parallel and do not intersect. The data-driven approach not only answers to "what?" and "why?", but suggests "what's next" and "how to do it right". Hence, the DIKW pyramid shall be enhanced with the 5th step - the decision itself.

Here's the decision making cycle, based on the data-driven approach:

1. Bulk data collecting through measurements, logging, tracking or recording

2. Data organization, processing and storage with the use of BI software

3. Evaluating past experience

4. Coming up with the actions plan

5. Taking actions

6. Analyzing first results and correcting the initial plan

7. Analyzing long-term consequences of the decision and capturing the experience.

The 4th and 5th steps are where the pyramid ends, while the 6th and 7th are what closes the loop of the advanced DIKW pyramid.

How to scale up and measure the DIKW model with Weje?

Without scaling and evaluating the DIKW model remains a concept - not something you can apply. To adapt this to a daily routine, use all-in-one software solutions, such as Weje. This is a true on-hand personal assistant when it comes to dealing with data - for both individuals and organizations.

Imagine your company develops 'turnkey' landing pages for B2B clients. To deliver projects, aside from the specification alone, you'll probably need to consolidate a number of other inputs:

technologies - what programming languages are to be used to ensure webpages' functions

resources - what professionals are required to do tasks

visuals - what the design should be

budgeting - how costs will be distributed

timing - what milestones shall you plan.

Weje has a straightforward instrument for organizing relevant data, which is a canvas. Here, you can upload documents from the computer or drag & drop relevant figures, content extracts, or images - right from the Web.

Provide co-workers with access to the dashboard, so that everyone can work separately with his or her data scope. No more fear of dismissing something important - the project's dossier will be in one place.

Now that all figures are in place, it's time for classification and distributing them into blocks. Use Weje tools - write lists or checklists, create libraries or mind maps, sort and range the data. You can easily do this by moving and merging separate data cards. The process is native, thanks to the well-designed interface.

In the case of landing pages' development - the information can consist of the following separated blocks:

design insights

content ideas

web hosting inputs

a "what-makes-these-all-work" chapter - HTML/CSS/JS.

After you've prepared and classified the information, tie it all together and come up with the knowledge over the project.

At this point, make sure that the work within the team is synchronized. Take advantage of Weje collaborative tools, such as Kanban boards, meetings' scheduling and to-do lists. Use Weje to create corporate databases, share performance analytics and project cases within teams. Remember, your experience is your best teacher to end up with business wisdom.

The final step the DIKW model leads us to - is the decision itself. With Weje, it's not only you transform raw data - you get the visual evidence of further to-do-steps.

The canvas you worked on is a true prototype of the project. You don't need to transfer findings into documents or separate presentations - simply share the link with the project's stakeholders to launch discussions or come to the realization.

In a nutshell

The 2018 McKinsey survey has shown data-targeting companies tend to be more effective than their competitors, thanks to clear data strategies and strong data-governance practices. Hence, the "Data, Information, Knowledge, Wisdom" model is crucial for developing a "data-first" culture within the company's decision-making process.

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What is the Data, Information, Knowledge, Wisdom (DIKW) Model? - Weje.io blog

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The DIKW acronym has worked into the rotation from knowledge management. It demonstrates how the deep understanding of the subject emerges, passing through 4 qualitative stages: "D" - data, "I" - information, "K" - knowledge and "W" - wisdom. The latter is, hence, the ultimate stage of any cognition process.

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It’s time for nurses to demand improvements in the design of the electronic health record—and to lead the effort.

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Cloud Based EHR - Electronic Health Records | Practice Fusion

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Visit our updated Value-Based Reimbursement & Quality Initiatives page to get the latest information on these programs. Learn more

CMS Meaningful Use definition

Under the Health Information Technology for Economic and Clinical Health (HITECH Act), which was enacted under the American Recovery and Reinvestment Act of 2009 (Recovery Act), incentive payments are available to eligible professionals (EPs) who successfully demonstrate meaningful use of certified EHR technology. The Recovery Act specifies three main components of meaningful use: The use of a certified EHR in a meaningful manner, the use of certified EHR technology for electronic exchange of health information to improve quality of health care and the use of certified EHR technology to submit clinical quality and other measures.

Meaningful Use stages

Meaningful Use is implemented in a phased approach over a series of 3 stages.

Stage 1

Promotes basic EHR adoption and data gathering

Stage 2

Emphasizes care coordination and exchange of patient information

Stage 3

Improves healthcare outcomes

CMS recently released changes to all stages of the Meaningful Use program. Visit our blog for up-to-date information on implementing Meaningful Use "

Practice Fusion is certified

CMS requires attesting providers to use, at minimum, EHR technology certified to the 2014 Edition. If it is available, attesting providers may use EHR technology certified to the 2015 Edition, or a combination of the two. Practice Fusion has received Health IT Module 2015 Edition ONC Certification for select components of our EHR Version 3.7 and our 2014 Edition Complete EHR Certification now is applicable to EHR Version 3.7, making it a combination 2014 Edition / 2015 Edition certified Health IT product. Learn more "

What are the Meaningful Use requirements?

The most up-to-date requirements can be found on the CMS website or by contacting your state Medicaid agency.

What is the reporting timeline?

During your first year of meeting Meaningful Use, you have to fulfill the criteria over a 90-day reporting period. After that, you have to demonstrate the criteria for a full calendar year.

If you're participating in the Medicaid program, you can Adopt, Implement, or Upgrade (AIU) a certified EHR or demonstrate meaningful use of one in your first year to earn incentives. You would follow the reporting timeline starting next year.

What are the incentives and penalties?

Medicare EHR Incentive Program

Please note: The Medicare EHR Incentive Program was sunsetted by CMS on December 31, 2016. The program was replaced by the Quality Payment Program. For more information about participation in the Quality Payment Program, please visit Practice Fusion's Quality Payment Program Center.

Medicare incentive payments are equal to 75% of your annual Medicare Part B allowed charges up to a maximum yearly amount (see chart below).

Beginning in 2015, providers who are eligible for the Medicare Meaningful Use program who don't successfully demonstrate Meaningful Use will be penalized-starting at 1% of Medicare Part B reimbursements and increasing each year to a maximum of 5%. There will be hardship exceptions for eligible providers.

Meaningful Use payment adjustments are based on achieving Meaningful Use two years before the adjustment year-so providers starting in 2013 avoid a penalty in 2015.

Medicare Incentives

Medicaid EHR Incentive Program

If you started your participation in the Medicaid MU program in 2015, you can earn incentives through 2020. Please note that 2016 was the last year that Eligible Professionals (EPs) could enroll for participation in the Medicaid EHR Incentive Program. As such, enrollment is no longer open for new participants.

If you see Medicaid patients, you can earn up to $63,750 in incentive payments over the six years that you choose to participate in the program (see chart below). If you start in 2015, you can earn incentives through 2020. Please consult your state's agency for information about a specific payment schedule.

Providers who are eligible for Meaningful Use under the Medicaid program are not subject to payment penalties, unless the provider is also eligible under the Medicare program.

Medicaid Incentives

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What is Meaningful Use? Stage 1 and Stage 2 | Practice Fusion

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Visit our updated Value-Based Reimbursement & Quality Initiatives page to get the latest information on these programs. Learn more Under the Health Information Technology for Economic and Clinical Health (HITECH Act), which was enacted under the American Recovery and Reinvestment Act of 2009 (Recovery Act), incentive payments are available to eligible professionals (EPs) who successfully demonstrate meaningful use of certified EHR technology.

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Connecticut Official Health Informatics Exchange (Connie)

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Connecticut Official Health Informatics Exchange (Connie)

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I thought this was interesting becuase I didn't know each state had thier own HIE. I think this is very beneficial for patients and also very conventient for providers as it makes access patient medical health records easier!

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Connie, CT’s health information exchange, is a safe, confidential way of sharing health information digitally among healthcare organizations.

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Risks with sharing DNA with genetic testing companies

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Risks with sharing DNA with genetic testing companies