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5 Health Informatics Legal/Ethical Podcasts

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5 Health Informatics Legal/Ethical Podcasts

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This webpage provides a list of 5 different podcasts all surrounding the topic of health informatics and touch on various aspects of the growing field including legal issues, security, and ethics. The list provides choices from different aspects of the healthcare industry and how informatics has changed and affected their areas of work. Beyond that the webpage provides some tips to identify differences between good and bad health informatics podcasts for those who choose to explore podcasts outside of this list.

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In all aspects of healthcare, even in schooling, the importance of abiding by the law and pillared principles of ethics is stressed. There are many reasons as to why operating within these tenants are so important within any organization but especially so within healthcare. This job requires providers at all levels of care to take to heart the Basic Ethical Principles and apply them ever day within their work with patients. By doing so they are able to ensure they are completing their positions to their fullest duties, avoiding potentially lethal mistakes, and help improve patient quality care and outcomes. Healthcare intrinsically involves application of these principles in day-to-day practice. With the rise in use of healthcare informatics and the rapid growth of technology it is important for ethical principles to be translated and appropriate applied to this highly nuanced world of informatics. It is within the informatics realm that communication, information exchange, privacy, access to information, and security all intersect and prompted the need for General Principles of  Informatics Ethics. Patient data so readily available and accessible must abide by high security standards and restrictions to ensure data is protected. Additionally this access to health data has extensive implications for medical research though must be protected and information provided while abiding by ethical informatic principles. Finding the balance to maximize the informatics data while adhering to ethical principles will help drive forward the age of healthcare technology adaptation and hopefully patient outcomes.

Link:
Attached is an article from the National Library of Medicine describing the evolution of informatics in healthcare and how ethics have adapted and been applied to the growing field.

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Summary

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

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

Introduction

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

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

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

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

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

1 Artificial Intelligence, Machine Learning, and Ethics

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

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

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

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

1.1 Quality and Standards Are Ethical Issues

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

1.2 Prevent and Eliminate Bias

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

1.3 Use Machine Learning Software for Good and not Evil

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

1.4 Insist on and Provide Robust Education and Evaluation

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

2 Big Data, Data Sharing, and Privacy

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

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

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

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

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

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

Are data reliable and who is responsible for ensuring reliability?

How exactly does the calculation work?

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

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

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

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

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

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

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

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

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

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

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

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

3 Duties to Use New Technology

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

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

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

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

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

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

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

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

4 Ethics, Standards, and Public Policy

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

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

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

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

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

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

Conclusion

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

Footnotes

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

References

2. Goodman K W.Health information technology and globalization Los Angeles: Sage; 2011117-25.. See also references therein. [ Google Scholar]

3. Goodman K W. Cambridge: Cambridge University Press; 2016. Ethics, Medicine and Information Technology: Intelligent Machines and the Transformation of Health Care. [ Google Scholar]

6. Anderson J G, Aydin C E. Cambridge: Cambridge University Press; 1998. Evaluating medical information systems: social contexts and ethical challenges; pp. 57-74. [ Google Scholar]

8. Louis P CA. Philadelphia: American College of Physicians; 2006. Essay on Clinical Instruction. [ Google Scholar]

9. Cozzens M, Miller S J. Providence, Rhode Island: American Mathematical Society; 2013. The Mathematics of Encryption: An Elementary Introduction. [ Google Scholar]

13. Institute of Medicine.The Learning Healthcare System: Workshop Summary (IOM Roundtable on Evidence-Based Medicine) Washington, DC: The National Academies Press; 2007 [ Google Scholar]

14. Friedman C P, Rubin J C, Sullivan K J. Toward an information infrastructure for global health development. Yearb Med Inform. 2017:16-23. [[ PMC free article] [ PubMed] Google Scholar]

18. Petersen C, Berner E S, Embi P J, Fultz Hollis K, Goodman K W, Koppel R, Lehmann C U et al. AMIA's code of professional and ethical conduct. J Am Med Inform Assoc. 2018;25(11):1579-82. [[ PMC free article] [ PubMed] Google Scholar]

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

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

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Getting Started with Healthcare Data Visualization 

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Getting Started with Healthcare Data Visualization 

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This website provides easy templates and guides to start using data visualization in the context of healthcare. The free templates are available through Google applications. Descriptions and education regarding how and what data visualization tools should be appropriately used are also given. This is a good tool to use as a launching point to get into the world of data visualization in healthcare.

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Healthcare facilities are among the sectors that produce incredibly immense data. This data is valuable only if it's leveraged to the fullest. For instance, it could provide insights that help cut costs and improve service delivery.

You need tested and proven charts to extract actionable insights into healthcare data. Luckily, we've put up together a list of recommended visualization designs you can leverage to extract crucial charts, namely:

Excel is one of the go-to popular data visualization tools among professionals and business owners worldwide.

But the visualization tool has very basic Comparison Bar, Treemap, and Sankey Charts. And this implies you've got to invest extra effort and time to edit the charts to align with your needs.

You can download and install a specific add-in in Excel to access ready-to-use creative data visualizations for healthcare (highlighted above).

In this blog post, you'll learn the following:

Table of Content:

1. Why is Data Visualization Important for Healthcare?

2. Benefits of Data Visualization in Healthcare

3. Data Visualization in Healthcare Examples

4. How can You Get Started with Data Visualization in Healthcare?

5. How to Create Sankey Chart in Excel to Visualize Healthcare Data?

6. How is Data Visualization Transforming the Healthcare Industry?

7. Video Tutorial: How to Visualize Healthcare Data Using Sankey Chart?

Before delving into the how-to guide, we'll address the following question: why is data visualization important in healthcare?

Why is Data Visualization Important for Healthcare?

Data visualization strategies help analysts in the healthcare sector communicate actionable insights.

Using tools like Microsoft Excel and Google Sheets, hospitals can create data streams and visualizations with the potential to optimize service delivery.

Tools for gathering data in most healthcare facilities include:

Through quantitative ("Please rate your satisfaction with your visit on a scale of 1 to 10") and qualitative (additional comments section) strategies, a survey can reveal opportunities for organizational growth.

Scorecards for tracking progression.

It's common for healthcare professionals to use scorecards for tracking recovery among patients.

Health care professionals can leverage data visualizations to tell compelling stories to various stakeholders.

In the coming section, we'll address the benefits of data visualization in healthcare.

Benefits of Data Visualization in Healthcare

Our brain processes visual information, such as charts, 60,000 times faster than poring over spreadsheets and data reports.

Visualization is a quick and easy way to convey insights to a broader audience. Make data visualization a habit in your organization to enjoy the benefits below:

Healthcare data visualization makes it amazingly easy to extract answers from your data to create compelling stories.

Imagine using the tables and spreadsheets to explain emerging patterns and other significant insights about a hospital to your audience.
How would they respond? Would you get buy-in after presenting the table to them?
Our brains grasp visual content, such as graphs and maps, 60,000 times faster than table reports.

And this means a compelling data story loaded with easy-to-interpret charts can empower quicker decision-making.

Bulky data provides unlimited opportunities for the healthcare sector to extract actionable insights. Yes, insights that could spell the difference between your brand and competition.

Visualizing data helps pinpoint relationships and patterns between metrics. Exploring these patterns enables you to save immense resources, such as time, by focusing only on areas that need urgent action.

Health data visualization charts can help you easily spot errors in the data. Working with error-free data validates the accuracy of the insights extracted.

The reason why we visualize data is to create data stories. Remember, poring over numbers in spreadsheets is monotonous, especially if you're in front of an audience.

So, you need to create a compelling story with insights extracted from the raw data. People love stories. Yes, and this is because they appeal to emotions.

To craft a compelling data story, you need an actual story.

It sounds contradictory. Yes, we know.

To create a narrative, start by asking a question or forming a hypothesis. And then, dig into your data to find answers.

Below are some of the questions you need to ask:
Remember, healthcare data visualization comes before you create a story.
In the coming section, we'll address data visualization in healthcare examples.

Data Visualization in Healthcare Examples

Some of the tested and proven healthcare data visualization charts you can use include:

Let's discuss these charts in detail.

A Comparison Bar Chart is one of the healthcare data visualization graphs you can use to visualize data.

It uses a bar to represent sections of the same category, and these bars are placed adjacent to each other. The chart is reliable, especially in comparing items between various groups. You can track the changes in key variables.

Sentiment Trend Chart displays patients' and other clients' sentiments and opinions towards a healthcare facility. The chart has bars on either side with different colors.

Treemap Graph (one of the healthcare data visualization charts) uses nested rectangles to visualize your data. Each data point is represented by a rectangle, whose area is proportional to its value.

You can use this insightful chart to represent several dimensions of your data, such as groups and subgroups.

Take a look at the table below. Can you provide a coherent story by looking at the data below?

Note the difference after using a healthcare data visualization chart.
Insights

The emergency department is the BIGGEST consumer of the hospital resources

Neurology is the LEAST consumer of hospital resources.

January and February are the busiest months.

It makes sense to allow most staff to take a vacation in April due to decreased visits to the hospital.

A Sankey Diagram visualizes "a flow" from one set of values to the next. The two items being connected are referred to as "nodes." The connections are labeled as "links." Besides, it's named after an Irishman, Capt. Matthew Sankey, first used them in a publication on the energy efficiency of a steam engine in 1898.

Sankey diagrams were initially used to visualize and analyze energy flows, but they're a great tool to depict the flow of money, time, and resources. Flows in the chart can display energy, materials, fluids, revenue, or costs.

You can use the visualization design to visually show complex processes, focusing on a single aspect or resource that you want to highlight. Besides, the chart offers the added benefit of supporting multiple viewing levels. Flows in the chart can display energy, materials, fluids, revenue, or costs.

How can You Get Started with Data Visualization in Healthcare?

Excel generates very basic healthcare data visualization charts, such as Sankey. Besides, it lacks a huge chunk of the charts we highlighted earlier, such as Sentiment Trend Charts and Sankey Charts.
So, what's the solution?
Download and install a particular third-party add-in into your Excel to access ready-made healthcare data visualization graphs.

ChartExpo is an Excel add-in with an ultra-friendly user interface to ensure that any professional can visualize data, irrespective of their coding or programming skills. The add-in comes loaded with all the healthcare data visualization charts highlighted above.

In the coming section, we'll show you how to visualize healthcare data using Sankey in ChartExpo.

How to Create Sankey Chart in Excel to Visualize Healthcare Data?

This section will use a Sankey Chart to visualize the data below.

To get started with healthcare visualizations, install the ChartExpo add-in for Excel from this link.

Select ChartExpo and click the Insert button to get started with ChartExpo.

Once ChartExpo is loaded, you will see a list of charts. Once the Chart pops up, click on the Sankey Chart icon to get started, as shown below.

Select your data and click the Create Chart From Selection, as shown below:

Check out the final healthcare data visualization chart.

Insights

CEPI got $157k from Germany, Canada, the United Kingdom, & Gates Foundation. Canada, the United Kingdom, the United States, & Gate Foundation donated $4.93M to NGOs & Foundations for fighting against the Ebola virus. UN agencies got the most donations.

The UK donated the biggest chunk of funds for Ebola prevention. In the coming section, we'll address the following question: how is data visualization transforming the healthcare industry?

Video Tutorial: How to Visualize Healthcare Data Using Sankey Chart?

In the following video, you will learn Video Tutorial: How to Create Sankey Chart in Excel to Visualize Healthcare Data?

Data Visualization in Healthcare | How is Data Visualization Transforming the Healthcare Industry?

Data analysis goes hand in hand with data preparation, modeling, and visualization.

Healthcare analytics is becoming more efficient today through artificial intelligence (AI) and machine learning technologies.

The adoption of visualization tools is a must-have for the healthcare sector to be fully data-driven.

Visualization brings what matters into focus, helping medical staff identify and react faster to threats.

By visualizing real-time data on patient health status, doctors can define and group patients according to the treatment and attention they need. Including all patient records in a chart prevents practitioners from missing critical insights.

Doctors can estimate changes in patient health status and make better diagnoses using predictive analytics tools.

Data visualization charts are vital, especially when crafting data stories for the top management in healthcare facilities. Top management can use data presentation charts, such as Sankey, as a backdrop for their decision.

Charts and graphs are powerful because they simplify data by making it understandable & readable at the same time.

Unlike manual reporting, real-time data visualization significantly speeds up the analysis and consumption of key insights, which aids in:

1. Minimizing process inefficiencies

2. Accelerate the decision-making process

3. Optimize costs

Errors and Fraud Detection

The annual cost of fraud and abuse in Medicare alone ranges from $58.5 to $83.9 billion.

Fraud schemes are committed by medical providers and patients and include double or phantom billing, service, forged prescriptions, insurance cheating, and more.

Healthcare data visualization graphs improve the transparency of connections between patients, practitioners, providers, payers, and claims.

The reason why we visualize data is to create data stories. Remember, poring over numbers in spreadsheets is monotonous, especially if you're in front of an audience.

So, you need to create a compelling story with insights extracted from the raw data. People love stories. Yes, and this is because they appeal to emotions.

FAQs:

How is data analytics used in healthcare?

Data visualization strategies help analysts in the healthcare sector communicate actionable insights.

Using tools like Microsoft Excel and Google Sheets, hospitals can create data streams and visualizations with the potential to optimize service delivery.

Health data visualization charts can help you spot errors in the data easily.

What are the different types of data analysis?

Analytics are all activities related to investigating data for hidden answers. The primary goal of the data analytics field is to make it easy for other stakeholders to access and understand insights.

There're four types of data analysis, namely:

Descriptive Analysis.

Diagnostic Analysis.

Predictive Analysis.

Prescriptive Analysis.

Wrap Up

Healthcare facilities are among the sectors that produce incredibly immense data.

This data is valuable only if it's leveraged to the fullest. For instance, it could provide insights that help cut costs and improve service delivery. You need tested and proven charts to extract actionable insights into healthcare data.

Luckily, we've put up together a list of recommended visualization designs you can leverage to extract crucial charts, namely:

Excel is one of the go-to popular data visualization tools among professionals and business owners worldwide.

But the visualization tool has pretty basic Comparison Bar, Treemap, and Sankey Charts. And this implies you've got to invest extra effort and time to edit the charts to align with your needs.

We recommend our readers to use the ChartExpo add-in to supercharge their Excel.

ChartExpo has many ready-made, insightful, and visually appealing charts. Besides, it has an intuitive interface, which means you don't need coding or programming skills to visualize your data.

Sign up for ChartExpo today to access ready-made healthcare data visualization charts.

How much did you enjoy this article?

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How to Get Started with Healthcare Data Visualization?

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Healthcare facilities are among the sectors that produce incredibly immense data. This data is valuable only if it's leveraged to the fullest. For instance, it could provide insights that help cut costs and improve service delivery. You need tested and proven charts to extract actionable insights into healthcare data.

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Transitioning seamlessly from last week's topic of data use to build stories from data results to help bring the data to life, this week touches on similar ideas of taking the data and presenting it in a visual medium to better understand, apply, and draw conclusions from. Reformatting presentation of data can help to make it more digestible, understand the impact and applications of use for data, and can influence decisions people may make based on the data and its presentation. Applying data visualization concepts to statistics within the healthcare spectrum companies, researches, as well as providers can improve health outcomes, expedite research, and narrow focus for quality improvement initiatives. Data visualization can provide interactive applications presenting data that could allow visual representation of largest quality improvement issues, initiatives and success rates based on previous data/research, and can also incorporate patient perspective on largest quality improvement issues. The comprehensiveness of data visualizations like this, would streamline understanding of the data, its importance in quality improvement application, identify greatest areas of need or impact, and can provide patient perspective on the issues simply with visually digestible representations of data information. The broad scope of data input and that can be represented provides a more complete view of actual importance and efficacy of quality improvement issues, and allows researchers to take into account the patient's perspectives, allowing their voices to be heard.

Link: The attached link is to the website for the Agency for Healthcare Research and Quality. The site offers programs to create data visualization tools as well as infographics to be used for medical data research.

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AHRQ's interactive data visualization tools allow researchers, policymakers, healthcare leaders, and others to view visual depictions of healthcare trends. Content drawn from AHRQ's data resources address various topics, such as COVID-19 hospitalizations, health insurance coverage, and emergency department visits. Users may customize visualizations according to geographic location, age, income, and other categories. AHRQ also offers data infographics to showcase its research findings.

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AHRQ's interactive data visualization tools allow researchers, policymakers, healthcare leaders, and others to view visual depictions of healthcare trends. Content drawn from AHRQ's data resources address various topics, such as COVID-19 hospitalizations, health insurance coverage, and emergency department visits. Users may customize visualizations according to geographic location, age, income, and other categories.

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Purdue Online Data Storytelling Certification Course

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Purdue Online Data Storytelling Certification Course

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For those further interested in learning more about data storytelling or become a certified data storyteller this online course offers a certification program via Purdue College Online. Provided are details of the courses offered (4 in total), general content covered, who may benefit from taking the course and more. As an additional note those who sign up for the paid courses will receive the initial "Data Storytelling 101" for free.

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Data is the foundation upon which critical insights are made - whether you are working in a doctor's office, a political news room, a startup, a science laboratory, or even a University classroom. Considering the importance of data to our everyday lives, being able to interpret and communicate data in a way that informs, compels or reassures is a core personal and professional skill.

For data to have an impact, it has to be presented as more than a set of numbers. Good, useful data needs to be organized and delivered within a coherent story. But what makes a good data story? Purdue's Data Storytelling courses prepare professionals to build data stories that are effective, exciting, and capable of offering innovative solutions to a wide range of issues.

The Data Storytelling Certificate

The Data Storytelling Certificate program is a series of four courses available 100% online. The courses combine elements of rhetoric, data science, visualization and storytelling to form a cohesive and unique plan of study. Each of the 4 courses are subdivided into 6 modules. The first course, Data Storytelling 101, is free for students who also register for a paid course.

Students can register for and complete the data storytelling courses individually or purchase and complete all four to earn a Data Storytelling Certificate from Purdue. Data Storytelling 101 is a free course that must be taken before you start any of the other paid courses.

Data Storytelling Courses Available

Data Storytelling 101 offers an introduction to the concept of Data Storytelling, why it matters, and how it can transform the results of your research into impactful narratives through which your audience learns new things, remembers important findings and then acts on them. This course must be taken before you start any of the other paid courses.

Provide examples and justify all core techniques in storytelling, including challenging prior assumptions, providing alternative explanations, balancing timing versus immediacy, and transforming each story into a teachable moment.

Data Storytelling 101 is an introduction course that is required prior to taking the remaining three courses. Students do not individually register for the Data Storytelling course but must register for one or all of the remaining three courses. Once registered for any other course, the student will also receive notification for access to Data Storytelling 101.

Building Causal Data Stories

This course teaches students how to think about research - from data collection to data analysis to reporting results - as a narrative process. Rooted in basic statistics, the course provides students who have some numeric literacy a clear and direct path for advancing from reportable results to stories with impact. Students will especially learn how to construct numeric stories that demonstrate causality.

Enumerate and defend the core relationships between research design and story-building, as well as define a valid causal inference and possible fallacies.

Generate hypotheses based on data, explain how the hypotheses could be tested, and show how to translate different experimental designs and data pools into stories

Register Now
Narrative Visualization

This course provides key strategies for visualizing results and enhancing the visual communication of data stories, followed by a deeper dive into how to go from analysis of results to a compelling story enhanced with visuals.

Create specific data visualizations that carry forward casual narratives, such as stacked bar charts, time series, and multidimensional causal chains, using effective and recognized visual and charting building blocks.

Become familiar with how to avoid common pitfalls in designing visual content with narrative goals.

Identify story elements that map onto the five narrative techniques learned and demonstrate through written compositions the ability to use them effectively.

Register Now
Data Storytelling as an Ethical Choice

In this course, students learn how to create data stories that use ethical research and protect basic human rights: autonomy, fairness, trust, beneficence, and non-malevolence.

Define and use appropriate checks and reasoning processes for ensuring data transparency, validity, and ethical use.

Identify the effect of a story by the amount of belief change it induces in an audience, determine the threshold of significance for various audiences, and use the amount and type of learning for each story to predict the degree to which it could be an actionable story or not.

Register Now
View All Courses Register for the Full Certificate Program FAQ

Who Should Enroll

This course is designed for professionals who use data in their daily lives and for professionals looking to become more effective data communicators. Being able to effectively communicate, or tell stories, about data is a key skill for:

Communication professionals

PR professionals

Journalists

Academic researchers

Scientific researchers

STEM professionals and students

Business analysts

Professionals whose tasks include sharing data through presentations, emails, flyers, and other written/visual materials

Students are not expected to know a programming language. An understanding of basic statistics is helpful but not required.

About the Instructor

Dr. Sorin Adam Matei is Associate Dean of Research and Graduate Education in the College of Liberal Arts and Professor of Communication in the Brian Lamb School of Communication. His research studies the relationship between information technology, group behavior, and social structures. He is known for spearheading innovative research projects on Wikipedia, social media cognition and emotion, and ethics in Big Data. He has been awarded numerous grants and has published nine books as well as dozens of journal articles in publications like The Journal of Communication and Communication Research.

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Data is the foundation upon which critical insights are made - whether you are working in a doctor's office, a political news room, a startup, a science laboratory, or even a University classroom. Considering the importance of data to our everyday lives, being able to interpret and communicate data in a way that informs, compels or reassures is a core personal and professional skill.

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This week's topic was particularly interesting as statistics are universally a very dry topic though provides a lot of important data that medical professionals, researches, and business alike use every day. Initially the ideas of statistics and storytelling seemingly have no over lap, raw data and information compared to creative and processed mediums. Though, after the asynchronous material the power of statistical storytelling truly shined and displayed the power and influence "statistical storytelling" holds. Using the conclusions drawn from the data to build a story that people can relate to or highlighting a crucial need with the data story helps the information become digestible and impactful and can motivate people to act on the information. The first video does a great job of this in relation to creating a compelling story and asking thought provoking humanitarian and business questions with the conclusions. Both videos take aspects of data to wisdom evolution theory and give the data knowledge and contextual purpose that can move stakeholders to make favorable decisions. Creating stories and personalities for the data or even conclusions that can be drawn from the data sparks a human connection to the hard numbers and facts, where people can almost put themselves in the world of the data story. Personal connection and application are driving factors for many interested groups. These groups tend to be reactive and "selfish" for lack of a better term, finding if proposals, changes or investments do not affect themselves, their company/business, finances, or otherwise people will not be drawn to even hearing out groundbreaking research, proposals, or ideas.

Link: The provided link is to the Harvard Business Blog. This particular blog posts gives background and further information regarding what data storytelling is, how it is done, and what it can be applied to. More over the post goes on to describe the advantages and positive business outcomes that can be achieved via data storytelling.

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When you hear the term "data analysis," what do you think of? Your mind may jump to scouring spreadsheets, implementing algorithms, and making mathematical calculations-all "hard skills" of data analysis. Yet, hard skills are useless without their soft skill counterparts. It's not enough to just analyze data; you need to know how to communicate the story it tells in a clear, compelling manner-a skill called data storytelling.

According to the Bureau of Labor Statistics, the demand for research analysts is expected to grow 25 percent between 2020 and 2030, much faster than the average across all industries. Many companies have begun including data storytelling as a required skill in analyst job descriptions, while others have opted to hire for data storyteller positions to supplement their existing analytics teams' abilities. Possessing the skills to both analyze data and communicate its insights can help you stand out as a well-rounded candidate.

Here's a primer on the key components of data storytelling, why storytelling is an impactful communication tool, and how to craft a compelling narrative of your own.

Free E-Book: A Beginner's Guide to Data & Analytics
Access your free e-book today.
DOWNLOAD NOW

What Is Data Storytelling?

Data storytelling is the ability to effectively communicate insights from a dataset using narratives and visualizations. It can be used to put data insights into context for and inspire action from your audience.

There are three key components to data storytelling:

1. Data: Thorough analysis of accurate, complete data serves as the foundation of your data story. Analyzing data using descriptive, diagnostic, predictive, and prescriptive analysis can enable you to understand its full picture.

2. Narrative: A verbal or written narrative, also called a storyline, is used to communicate insights gleaned from data, the context surrounding it, and actions you recommend and aim to inspire in your audience.

3. Visualizations: Visual representations of your data and narrative can be useful for communicating its story clearly and memorably. These can be charts, graphs, diagrams, pictures, or videos.

Data storytelling can be used internally (for instance, to communicate the need for product improvements based on user data) or externally (for instance, to create a compelling case for buying your product to potential customers).

The Advantages of Data-Driven Decision-Making

The Psychological Power of Storytelling

Humans have told stories since the Cro-Magnon era to communicate with others for survival and record accounts of daily life. While storytelling methods have come a long way since the days of cave paintings, its psychological power holds true tens of thousands of years later.

The brain's preference for stories over pure data stems from the fact that it takes in so much information every day and needs to determine what's important to process and remember and what can be discarded.

When someone hears a story, multiple parts of the brain are engaged, including:

Wernicke's area, which controls language comprehension

The amygdala, which processes emotional response

Mirror neurons, which play a role in empathizing with others

When multiple areas of the brain are engaged, the hippocampus-which stores short-term memories-is more likely to convert the experience of hearing a story into a long-term memory.

Rather than presenting your team with a spreadsheet of data and rattling off numbers, consider how you can engage multiple parts of their brains. Using data storytelling, you can evoke an emotional response on a neural level that can help your points be remembered and acted upon.

How to Craft a Compelling Data Narrative

Data storytelling uses the same narrative elements as any story you've read or heard before: characters, setting, conflict, and resolution.

To help illustrate this, imagine you're a data analyst and just discovered your company's recent decline in sales has been driven by customers of all genders between the ages of 14 and 23. You find that the drop was caused by a viral social media post highlighting your company's negative impact on the environment, and craft a narrative using the four key story elements:

1. Characters: The players and stakeholders include customers between the ages of 14 and 23, environmentally conscious consumers, and your internal team. This doesn't need to be part of your presentation, but you should define the key players for yourself beforehand.

2. Setting: Set the scene by explaining there's been a recent drop in sales driven by customers of all genders ages 14 to 23. Use a data visualization to show the decline across audience types and highlight the largest drop in young users.

3. Conflict: Describe the root issue: A viral social media post highlighted your company's negative impact on the environment and caused tens of thousands of young customers to stop using your product. Incorporate research (such as this article in the Harvard Business Review) about how consumers are more environmentally conscious than ever and how sustainably-marketed products can potentially drive more revenue than their unsustainable counterparts. Remind the team of your company's current unsustainable manufacturing practices to clarify why customers stopped purchasing your product. Use visualizations here, too.

4. Resolution: Propose your solution. Based on this data, you present a long-term goal to pivot to sustainable manufacturing practices. You also center marketing and public relations efforts on making this pivot visible across all audience segments. Use visualizations that show the investment required for sustainable manufacturing practices can pay off in the form of earning customers from the growing environmentally conscious market segment.

If there isn't a conflict in your data story-for instance, if the data showed your current marketing campaign was driving traffic and exceeding your goal-you can skip that element and go straight to recommending that the current course of action be maintained.

Whatever story the data tells, you can communicate it effectively by formatting your narrative with these elements and walking your audience through each piece with the help of visualizations.

Communicating the Need for Action

Data storytelling can help turn data insights into action. Without effective communication, insights can go unnoticed or unremembered by your audience; both hard and soft skills are crucial for leveraging data to its fullest potential.

Harvard Business School Professor Jan Hammond speaks to this in the online course Business Analytics, one of three courses that make up the Credential of Readiness (CORe) program.

"Always remember that applying analytical techniques to managerial problems requires both art and science," Hammond says. "Over my career, I've learned that it's the soft skills that are the hardest to master, but they're critically important."
Do you want to become a data-driven professional? Explore our eight-week Business Analytics course and our three-course Credential of Readiness (CORe) program to deepen your analytical skills and apply them to real-world business problems.

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Data Storytelling: How to Tell a Story with Data

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Data storytelling is the ability to communicate insights from data using narratives and visualizations. Here's how to effectively tell a story with data.

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Clinical Practice Informs Secure Messaging Benefits and Best Practices

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Clinical Practice Informs Secure Messaging Benefits and Best Practices

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This study looked to explore the benefits of introducing direct securing messaging between patients and providers. The study was conducted at the VA and looked into how secure messaging can improve patient-centered care and efficiency in health care.

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Jolie N. Haun, 1, ² Wendy Hathaway, ¹ Margeaux Chavez, ¹ Nicole Antinori, ¹ Brian Vetter, ³ Brian K. Miller, ⁴ Tracey L. Martin, ⁵ Lisa Kendziora, ⁶ Kim M. Nazi, ⁷ and Christine Melillo ¹

Abstract

Background  Clinical care team members in Department of Veterans' Affairs (VA) facilities nationwide are working to integrate the use of Secure Messaging (SM) into care delivery and identify innovative uses. Identifying best practices for proactive use of SM is a key factor in its successful implementation and sustained use by VA clinical care team members and veterans.

Objectives  A collaborative project solicited input from VA clinical care teams about their local practices using SM to provide access to proactive patient-centered care for veterans and enhance workflow.

Methods  This project implemented a single-item cross-sectional qualitative electronic survey via internal e-mail to local coordinators in all 23 Veterans Integrated Service Networks (VISNs). Content analysis was used to manage descriptive data responses. Descriptive statistics described sample characteristics.

Results  VA clinical care team members across 15 of 23 VISNs responded to the questionnaire. Content analysis of 171 responses produced two global domains: (1) benefits of SM and (2) SM best practices. Benefits of SM use emphasize enhanced and efficient communication and increased access to care. Care team members incorporate SM into their daily clinical practices, using it to provide services before, during, and after clinical encounters as a best practice. SM users suggest improvements in veteran care, clinical team workflow, and efficient use of health resources. Clinical team members invested in the successful implementation of SM integrate SM into their daily practices to provide meaningful and useful veteran-centered care and improve workflow.

Conclusion  VA clinical care team members can use SM proactively to create an integrated SM culture . With adequate knowledge and motivation to proactively use this technology, all clinical team members within the VA system can replicate best practices shared by other clinical care teams to generate meaningful and useful interactions with SM to enrich veterans' health care experience.

Keywords: patient provider communication, messaging, ambulatory care/primary care, process improvement, diffusion of innovation

Background and Significance

Secure Messaging (SM) is an asynchronous communication tool within the Department of Veterans' Affairs (VA) My Health e Vet (MHV) patient portal. Similar to e-mail, SM provides a convenient and secure way for veterans to communicate electronically with their VA health care teams and other VA staff. Implementation of SM began on a voluntary basis in 2008; however, by 2012 it became mandatory for all VA primary care teams to ensure nationwide availability for veterans to communicate electronically with their primary care teams. Teams can establish a triage process for incoming messages that aligns with their existing processes (e.g., a nurse may triage and respond and complete or assign an incoming message from a veteran to a specific staff member). Based on guidelines for appropriate use for delivery of care and communication with veteran patients, SMs from veterans must be responded to and completed within 3 business days or the message is escalated and alerts are generated to notify the team. SMs may be reassigned not only to other team members but also to other SM teams both locally and nationally. SMs may be saved to the patient's electronic record as necessary to keep all care providers informed and, as appropriate, documented as an online evaluation and management encounter.

SM is an effective tool for supporting patient access to care and enhancing patient-provider communication, patient engagement, and self-management. This is accomplished through more efficient utilization of health services and resources, resulting in improved patient outcomes. 1 2 3 4 5 6 7 8 9 10 11 12 To this end, VA has established MHV coordinators throughout the VA health care system to enroll patients and support their use of this patient-facing portal. A national My Healthe Vet Coordinator Workgroup prioritizes goals related to enrollment, use, and functioning of SM across the VA health care system. The workgroup includes subject matter experts, operational partners, and local MHV coordinators. A systematic literature review conducted by VA researchers suggests that use of a patient portal including SM was associated with improved patient satisfaction and improved outcomes for patients with chronic diseases including diabetes and hypertension. 13 A VA study conducted by Shimada et al demonstrated that sustained use of SM improved glycemic control and LDL (low-density lipoprotein) cholesterol for patients with type 2 diabetes mellitus. 14 These studies suggest that use of SM as part of an integrated patient portal results in improved patient satisfaction, compliance, and communication, yielding improved patient outcomes. However, as with most innovations in health care, there is variation in perceived value and use. To date, most SMs are initiated by veterans, with the health care team responding to questions or requests. Yet the potential for SM to be used proactively, with the health care team initiating a message to the patient, may provide additional benefits. Further expansion to all surgical and specialty care teams has been significant; however, not all potential users (i.e., clinical care team members) are fully invested, motivated, or aware of how to fully integrate SM into care delivery. Identifying and disseminating local best practices for innovative and proactive use of SM is a key factor in its successful implementation and sustained use by VA clinical care team members and veterans to generate meaningful and useful interactions with SM.

Objectives

The goal of this project was to improve the integration of SM into the fabric of VA health care by identifying and disseminating SM best practices reported by front-line VA clinical care team members to provide proactive, patient-centered care to veterans and enhance provider workflow.

Methods

The VHA system is divided into 23 network areas called Veterans Integrated Service Networks (VISNs). This field-initiated collaborative project used a cross-sectional study design to distribute a single-item electronic survey via internal e-mail to local MHV coordinators in all 23 VISNs. MHV coordinators used e-mail and interpersonal contact to solicit input from their local clinical communities. Individual methods used by coordinators were not collected as part of this data collection process.

As a part of the operational mission of the VA to increase the proactive use of SM by clinical care team members, survey findings were disseminated using multiple methods. VA leverages multiple strategies for informing, educating, and motivating adoption of best practices by VA employees and as such the research team, operational representatives, and the national My Health e Vet Coordinator Workgroup collaborated to create a series of products for dissemination including a multimedia video and a "playbook' of best practices. These products were disseminated on a series of leadership calls, distributed to national workgroups, and posted on internal shared sites to be accessible to all VA employees. These dissemination efforts are further detailed in the discussion section.

Data Collection Instrument

A single-item questionnaire was developed to elicit information about how front-line providers use SM to: (1) improve communication; (2) improve efficiency; (3) increase patient access; and (4) improve workflow in clinical practice. These four areas are key priorities to the MHV Workgroup. Providers were specifically asked: "Please explain how you are using Secure Messaging to improve communication, efficiency, patient access, or workflow in your practice." Open-ended responses were collected to encourage clinical care teams to think broadly and describe their practices in their own words.

Data Management and Security

Responses were organized by row using an Excel spreadsheet. Responses were de-identified, assigned unique response IDs and entered in individual cells. Corresponding cells in each response row contained respondent demographic information that included the Respondent's Role, VISNs, Medical Center, and Service Unit. The spreadsheet was housed on a server protected by the VA firewall and raw data were shared using encrypted e-mail only.

Data Analysis

A rapid, content analysis of responses was conducted by two trained qualitative researchers. Researchers created a data reduction matrix 15 using a Microsoft Excel workbook to organize response data by domains and subdomains that represented clinical care team member's experiences using SM. First-level coding was guided by the four domains stated in the single-item questionnaire. Data were organized into descriptive titles representing the four primary questionnaire constructs: (1) communication, (2) efficiency, (3) patient access, and (4) workflow improvements. Constructs were not mutually exclusive and could be single-, double-, triple-, or quadruple-coded to account for multiple topics within a single entry. For example, a response could be coded "efficiency" and "communication" if it indicated that using SM to follow up with patients instead of calling them on the phone saves time and creates a record of contact that the patients can review as many times as they need. Responses were then sorted into four separate spreadsheets representing each of the four constructs. The sorted spreadsheet responses were coded using a second-level, thematic coding technique, which drilled down on subdomains within each domain. Researchers used memos to describe and operationalize codes and document their decision-making process. Memos helped researchers refine subdomains iteratively during the coding process. Following thematic coding, all data were sorted into two global domains: (1) benefits of SM and (2) SM best practices.

Finally, a panel of subject matter experts from the MHV Coordinators Workgroup and VA national offices reviewed and validated data findings. A total of nine review sessions were conducted over 2 months to validate data and reach agreement. The dataset collected for the current study are available from the corresponding author on reasonable request; however, the majority of data generated during this study are included in this published article as Supplementary Material (available in the online version).

Results

One hundred and seventy-one responses to the survey item were collected from clinical care team members representing 15 of the 23 VISNs. Clinical care team type of VA service, respondent role, and geographic VISNs are shown in Table 1 . Responses represented two global domains: (1) benefits of SM and (2) SM best practices. A comprehensive list of responses is organized in Supplementary Material (available in the online version).

Table 1

N (%)

VA service

Primary care

85 (49.7)

Specialty care

61 (35.7)

Behavioral health

16 (9.4)

Health promotion and disease prevention

6 (3.4)

Women's health

3 (1.8)

VA respondent role

Nursing

55 (32.2)

Provider

37 (21.6)

LCSW

3 (1.8)

MHV coordinator

3 (1.8)

PM&R

1 (0.6)

Psychologist

4 (2.3)

Other

34 (19.9)

Missing

34 (19.9)

Clinical care team member reports addressed access, trust, quality and continuity of communication, and care. Team members reported SM enhances continuity in communication among care team members and veterans. SM increases access to communicate with veterans, while simultaneously providing a multitude of workflow benefits; limiting duplication of efforts, and increasing efficient use of time and resources (e.g., paper, labels, postage). Team members also reported a perceived reduction in miscommunication and related errors. Efficiency in response times and reductions in miscommunications and errors all contribute to a key benefit of improving veteran's access to care and their experience of care and reducing their frustration to ensure high-quality service delivery. Domains and exemplar quotes relevant to clinical team members-identified benefits of SM are presented in Table 2 .

Table 2

Benefits

Exemplar quotes

#

I can send out all five messages to participants in ∼15-30 minutes. So, with minimal time spent, veterans have access to a great deal of useful information that enhances our class content.

Using SM has saved me a lot of time that I would have spent making labels and mailing out the information.

I communicate with them directly, eliminating the risk of miscommunication.

Our veterans know they can use this communication tool instead of the telephone, walking into the clinic and/or emergency room while knowing their needs will be met expeditiously.

I use SM to provide the veteran with a direct connection to the PACT-this allows the user to have more control of his care, it allows the staff to provide the veteran with outstanding service, it reduces the run around effect other veterans experience due to not using the service, and this practice reassures the veteran that his needs are being heard and met.

Most notable in this dataset, clinical care team member reports identified best practices that benefitted both veterans and clinical care teams. Reported best practices were related to creating an SM culture . SM culture is best defined as the standard by which meaningful use of SM is fully integrated throughout the process of health care delivery. Creating an effective SM culture is critical for effective and sustained implementation because accepted and expected SM use is a cultural standard by user groups in delivering and receiving health care.

Responses indicate creating a positive SM culture is an assimilation of proactively educating staff about SM and endorsing its proactive use, asking staff to prioritize SM enrollment, integrating SM into daily clinical practice, using SM for proactive outreach, leveraging SM across VA services, and integrating SM into their current workflow. Respondents described the most effective practices for integrating SM into provider's workflow or "triaging." These include: establishing SM responsibilities by clinical role; requiring that clinical care team members designate protected time to review and respond to SM; using SM to follow up after a visit or missed appointment; using SM for preappointment preparation; and responding to SMs within 24 hours. Two other subdomains that emerged from the data were practices of managing population health and reflecting on correspondence to promote patient-centered tone and language. Subdomains and exemplar quotes relevant to clinical team members-identified SM best practices are presented in Table 3 .

Table 3

Best practices

Exemplar quotes

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Providers create a proactive, integrated SM culture by:

Currently, when the patients check in for their appointments during the screening process, the registered nurse or licensed practical nurse educates the veteran about MHV and gives them a pamphlet regarding the services provided. When the veteran is checking out, the clerk at the front desk then directs the veteran to the second floor where the MHV kiosk and authentication center is located. The nurse on our team has been trained to authenticate the users (TMS Training and One and One training by the MHV coordinator). She prints out the list of veterans who have appointments for the week that need to be authenticated, and when the veterans come in for their appointments, they are authenticated in person. Then they are instructed to send their PACT a test message. This process is done for all five PACT members located in the clinic.

5. Proactive outreach

The Bless Her Heart Campaign is part of a Whole-hearted Women Veterans Initiative that was funded by VA Central Office in January 2015 by the Women's Health Service. There are three components: (1) identify high-risk women veterans for heart disease using CPRS parameters and laboratory data in 4,072 women in primary care; (2) design new specific interventions (e.g., tobacco, stress, diabetes, move) for "women veterans only" to raise awareness of risks and offer medical center resources to help the veteran make lifestyle changes; (3) complete a quality improvement assessment of all women veterans who have been identified as having heart or vascular disease ( n  = 263) in the problem list to verify accuracy and check for quality of care from clinical practice guidelines. All women who are contacted are encouraged to be enrolled in MHV and complete their Healthe Living Assessment which we enter into our database with their permission. We also complete a risk estimate online for arteriosclerotic cardiovascular disease using the American College of Cardiology/American Heart Association heart risk formula for each woman. We have passed out arteriosclerotic cardiovascular disease risk checklist handouts about heart disease to all women veterans and women employees on "Wear Red Day" on the first February. We are now developing an SM group just for our Bless Her Heart Campaign where all women veterans can communicate directly with our nurse care manager and providers about their progress.

6

As a speech pathologist, we provide education and rehabilitation on communication and cognitive skills. I incorporate SM training into sessions with veterans to ensure they have access to their providers when they need it.

7. Integrating SM into providers' workflow

I use SM to: receive queries from the patients; answer patient concerns in a timely manner; receive and respond to medication renewals and changes; triage some symptoms; notify patients about their test results; improve patient access by offering same day appointments, since it is a quick and efficient way to contact patients; reduce the number of unnecessary emergency room visits by effectively communicating with the patients; address necessary test orders prior to follow-ups to improve workflow; and reduce the number of phone calls.

When a veteran fails to show up for an appointment, I can use SM to contact him.

In Health Promotion and Disease Prevention, SM is used for ongoing weight management and nutrition care. Veterans can participate in ongoing follow-up, and share their successes, questions, issues related to these via SM.

I use PACT distribution groups to send SM to announce our "Healthier Living with Chronic Conditions" workshops. We offer these only periodically and do not have a formal consult for this reason. Secure Messaging has worked great to fill the workshops with veterans who are interested and motivated to improve their health and self-management abilities.

Infectious processes in one patient can place other patients and their providers at risk. Secure Messaging enables me to share important information securely and timely with our entire health care team. Secure Messaging also enables providers, laboratory personnel, therapist, etc., to utilize me as an assist in obtaining research materials, public health information, or data they may need. Quick communication is one of the tools that I, as an Infection Preventionist, use to help stop the spread of communicable diseases.

For most patients, I have found that they comprehend the messages better when they can read it and then review it later. I have also found that when a patient is called and provided a message, the message can be misunderstood, which can lead to several returned telephone calls. By being able to go back and review the message, they can usually answer any questions that may come up on their own, which allows another veteran to be taken care of during that time slot.

Discussion

As part of an organization-wide initiative to integrate SM into VA care delivery, this project was designed to solicit input directly from front-line VA clinical care teams to identify benefits and best practices related to using SM. The overall goal was to disseminate these findings nationally to demonstrate how VA clinical care teams can use SM in a useful and meaningful way to support proactive veteran-centered care and enhance clinical workflow. This article leverages the experiences of this user group to give insight into their SM experience to inform a peer-driven approach to innovation implementation and adoption, by tapping into the practices of motivated users who perceived benefits from their experience using SM. The purpose of this project was to seek insights about the meaningful use of SM by front-line clinicians and staff to inform efforts to promote its sustained proactive meaningful use nationally across all clinical care teams. As such, the quantity of responses is not as meaningful as the quality of the responses. A benefit and/or best practice does not need to be reported multiple times to be meaningful and/or innovative.

First, data indicate clinical care team member respondents perceived several benefits to using SM. When compared with previous data collected from veterans, 16 17 about their perceived benefits associated with SM use, these clinical narratives mirror that SM enhances access, trust, quality, and continuity and illustrated a multitude of workflow benefits including efficient use of time and resources. Beyond benefits, the core objective of this project was to identify and disseminate best practices which would demonstrate the usefulness of SM. The most compelling practice was clinical care team members' use of SM proactively to create an integrated SM culture in which SM is viewed as an integral part of providing clinical care, no less important than taking a patient's blood pressure or writing a prescription. Clinical care teams created a culture of SM by integrating the use of this electronic communication tool into clinical practice and service provision before, at, and after the point of care. Proactively educating staff and enrolling patients, endorsing use among user groups, and integrating the tool into daily practice and workflow provided a multiprong approach to integrating this tool into the culture of care delivery. Establishing this culture enhances provider buy-in, utilization, and promotion through communication.

SM represents a communication platform that can be used in varying ways by local clinical care teams. Using SM to manage population health is a critical example of the broad reach and efficiency this tool can provide, particularly to areas of specialty care such as health promotion and disease prevention, and infectious disease management. Finally, and maybe most notable, the practice of perceiving SM messages as correspondence can enhance communication by using patient-centered tone and language. Synchronous communication, such as face-to-face conversations and phone calls, does not allow for editing of tone and language, whereas SM, an asynchronous communication method, allows the sender to review and edit message content to ensure language is appropriate, supportive, and patient-centered. This surfaced as a best practice which can promote the vital and often unstated values of trust and high-quality communication among veterans and their health care team members. In a large system, with the potential for patients to feel lost and depersonalized, this practice promotes the personal touch of empathy, compassion, and respect that can be crucial in all forms of communication when delivering care to patients.

Dissemination of Findings

Several products were developed to support dissemination of these findings throughout the VA. Findings were distilled into a 7-minute educational video for national dissemination. This product was designed and produced with VA clinical team members to optimize relevance and usefulness. Survey responses were also used to create a "Secure Messaging Best Practices Playbook" as a companion reference piece to the SM Best Practices video. The "Secure Messaging Best Practices Playbook" provides the user with a comprehensive document that can be easily navigated to sections of the users' interest ( Supplementary Material , available in the online version). The video was published on VA's national MHV and SM workgroup web sites and disseminated to VA's national primary care, social work, patient advocate, dental, patient care services, caregiver support, and case manager networks. This content was also presented by VA Office of Connected Care leadership on a national MHV coordinator teleconference meeting. This work was also used to develop a chapter in the VA Undersecretary's book entitled, "Best Care Anywhere."

Limitations

The limitations of this project should be considered when interpreting these data. First, this is a convenience sample of VA staff; the representativeness and generalizability applies only to the VISNs and VA clinical care team members who responded. Nor do we have specific data on how local coordinators solicited responses to the survey item from clinical care team members. Second, there is threat for response bias. It should also be noted that this sample is heavily weighted toward the nursing perspective, but it also supports anecdotal reports that nursing staff are often the care team members communicating through SM. Additionally, data represented approximately 65% of VA VISNs with more than 53% of respondents from three VISNs; thus, ability to generalize findings to those underrepresented is limited. However, qualitative data responses are based on representation and saturation; as such, data findings present meaningful results for informing SM benefits and best practices. Third, as with any descriptive study that is cross-sectional in nature, the study does not allow statements on motivation for SM use. However, it does provide much needed descriptive data to understand clinical team members' experiences in using SM to deliver health care. Fourth, the generalizability of these findings outside the VA system, within private sector clinical care systems, is limited and does not account for economic incentives, which are different across these care settings. Finally, although this study provided important data on clinical care team members' experiences and perceptions about the use of SM, we cannot comment on how veterans' experience these reported practices. However, insight into veterans' experiences using SM has been explored by this research in previous studies. 16 17

Future Research

Future research would benefit from this field of inquiry by addressing contextual facilitators and barriers to clinical team members' use of SM and interventions to overcome barriers. To enhance use of SM by care team members other than nursing (e.g., physicians, specialty care), future research should focus on provider experiences and needs, specifically targeting responses from these clinical roles for a more well-rounded perspective. Future implementation research should evaluate specific implementation strategies for promoting adoption and sustained integrated and meaningful use by clinical care teams, and determining if different groups respond to different implementation strategies. Additionally, future research should explore the appropriate use and effects of SM in care management and patient outcomes, and costs associated with the use of SM in care delivery.

Conclusion

SM represents a communication platform that can be used in many useful and meaningful ways by local clinical care teams. Soliciting input directly from local clinical care teams can be an effective strategy for identifying best practices that can then be further disseminated to encourage implementation of these practices throughout the national system. Local clinical care teams report that the proper use of SM can improve veterans' care and clinical team workflow, and support the efficient use of the VA's health resources. However, knowing how to effectively use the tool to deliver health care is a key factor in its sustained proficient use. Using actual clinical narratives to inform other VA providers about how to benefit from SM use is critical to improving provider uptake of the tool. Front-line providers have the credibility and contextual knowledge to describe acceptable and useful practices for integrating SM into their daily practice to provide veteran-centered care and improve workflow. With adequate knowledge and motivation, efforts to proactively use SM can be replicated by all clinical team members within the VA system.

Clinical Relevance Statement

VA clinical care team members can use SM proactively to create an integrated SM culture . Care team members generate this culture by incorporating SM into their clinical practices, using it to provide services before, at, and after clinical encounters. An integrated SM culture can support the efficient use of health resources, improve clinical team workflow, and foster excellence in veteran care.

Multiple Choice Questions

When creating a culture of Secure Messaging, which of the following must be considered?

1. Educating clinicians about benefits and best uses of Secure Messaging.

2. Educating veterans about benefits and best uses of Secure Messaging.

3. Promoting use of Secure Messaging by clinicians and veterans.

4. All of the above.

The correct answer is D. Educating clinicians and veterans about benefits and best uses of Secure Messaging and promoting their use of Secure Messaging is critical to supporting sustained meaningful use of Secure Messaging in delivering and receiving clinical care services.

Acknowledgments

The development of this article was supported by the Department of Veterans' Affairs, Veterans Health Administration, and in part by the Center of Innovation for Disability and Rehabilitation Research at the James A. Haley Veterans Hospital and the Veterans and Consumers Health Informatics Office.

Conflict of Interest None.

Note

The contents of this article do not represent the views of the Department of Veterans' Affairs or the United States Government. All the authors have approved the final version of the article.

Protection of Human and Animal Subjects

Procedures were reviewed and are in compliance with ethical standards of the local institutional review board and with the World Medical Association Declaration of Helsinki on Ethical Principles for Medical Research Involving Human Subjects.

References

13. Goldzweig C L, Towfigh A A, Paige N M . Washington, DC: Department of Veterans Affairs; 2012. Systematic Review: Secure Messaging Between Providers and Patients, and Patients' Access to Their Own Medical Record. Evidence-based Synthesis Program (ESP) Center, West Los Angeles VA Medical Center. [ Google Scholar]

15. Beebe J. Walnut Creek, CA: AltaMira Press; 2001. Rapid Assessment Process: An Introduction. [ Google Scholar]

Articles from Applied Clinical Informatics are provided here courtesy of Thieme Medical Publishers

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Clinical Practice Informs Secure Messaging Benefits and Best Practices

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Jolie N. Haun, 1, 2 Wendy Hathaway, 1 Margeaux Chavez, 1 Nicole Antinori, 1 Brian Vetter, 3 Brian K. Miller, 4 Tracey L. Martin, 5 Lisa Kendziora, 6 Kim M. Nazi, 7 and Christine Melillo 1 Background  Clinical care team members in Department of Veterans' Affairs (VA) facilities nationwide are working to integrate the use of Secure Messaging (SM) into care delivery and identify innovative uses.

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This week emphasized and discussed the importance and steps taken to secure personal patient health information. Beyond leaking of personal data this health information can be sold and be used for fraud, directly impacting the care of the patient. An interesting portion of the asynchronous material was the movement towards universal use of patient portals and patient electronic access to copies of their health records. Programs like "MyChart" give patient's access to their EMR within the health system including results, procedures, current medication lists, and past medical history. As a personal user of MyChart in the role of a patient as well as a user of MyChart from the perspective of a provider it is apparent the communication barriers this technology breaks down. The user interface is fairly simple for those who are used to interfacing with smartphone applications and provides notifications of appointments, immunizations, and a direct line of up to date communication with providers via the provider messaging services. Using MyChart from the provider side is very helpful in securely communicating not just with patients but often family members heavily involved in their care. It allows us to get up to date information about the patient, triage, and make plan of care adjustments virtually immediately and all done securely. Of recent OpenNote access was granted too all patient's making provider notes accessible to all patients. This provided patients with a wealth of knowledge about their disease and as the asynchronous material touched on creates great discussion and talking points between practitioner's and patients. The downside is that unfettered access to these notes can scare patient's if they do not fully understand the information they access.

Link: The link provided is for the OpenNotes website, campaigning and advocating to introduce patient access to notes increasing transparency in healthcare.

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OpenNotes - Patients and clinicians on the same page

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OpenNotes is the international movement dedicated to making health care more open and transparent by encouraging doctors, nurses, therapists, and others to share their visit notes with patients.

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Alarm Fatigue & Patient Safety - Anesthesia Patient Safety Foundation (APSF) Podcast 

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Alarm Fatigue & Patient Safety - Anesthesia Patient Safety Foundation (APSF) Podcast 

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In this 14 minute podcast by the APSF, alarm fatigue and its affect on patient safety (with focus on patient anesthesia safety) are discussed. Provided are key terms within the realm of alarm fatigue, causes of alarm fatigue, and what we can do to avoid alarm fatigue and ensure patient safety.

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Welcome to the next installment of the Anesthesia Patient Safety podcast hosted by Alli Bechtel. This podcast will be an exciting journey towards improved anesthesia patient safety.

Today, we are going to review a featured article from the June 2019 Newsletter, "Alarm fatigue and patient safety" by Keith Ruskin and James Bliss. This topic was requests by several of our twitter followers as well. Thank you for the great suggestion!! You can find the article here: https://www.apsf.org/article/alarm-fatigue-and-patient-safety/
Bliss and Gilson's taxonomy of signaling terms:

Signal refers to all stimuli that occurs for an emergency notification in general

Alarm refers to a transient sensory signal (auditory or visual) that indicates ongoing danger that requires immediate corrective action

Alert refers to a transient sensory signal (auditory or visual) that indicates an adverse event may occur in the future

Xiao and Seagull's taxonomy of alarms:

False alarms occur when no danger exists, often because sensor thresholds are set too conservatively.

Nuisance alarms may indicate a problem in a specific context, but they have been activated in a different context (e.g., an arterial catheter low pressure alarm that activates when a blood pressure cuff is inflated).

Inopportune alarms occur at the wrong time, perhaps as alerts that signal a condition far in the future.

Be sure to check out the APSF website at https://www.apsf.org/
Make sure that you subscribe to our newsletter at https://www.apsf.org/subscribe/
Follow us on Twitter @APSForg
Questions or Comments? Email me at[email protected] .
Thank you to our corporate supporters https://www.apsf.org/donate/corporate-and-community-donors/
Alarms sounds: https://freesound.org/s/65401/
Wolf howl sound: https://freesound.org/s/398430/

© 2020, The Anesthesia Patient Safety Foundation

Hello and welcome back to the Anesthesia Patient Safety Podcast. My name is Alli Bechtel and I am your host. Thank you for joining us for another show.

(Insert alarm noises)

Today, on the show we are going to have a very important discussion so I need you all to pay attention like someone's life depends on it...wait, what's that...it's hard to pay attention with all these alarms...are those important alarms? Are they false alarms?

(Alarms off)

Okay, that's better, where were we? Today, we are going to talk about a very important topic and our patient's lives are at stake because we are talking about alarm fatigue and patient safety.

Before we dive into today's episode, we'd like to recognize Medtronic, a major corporate supporter of APSF. Medtronic has generously provided unrestricted support as well as research and educational grants to further our vision that "no one shall be harmed by anesthesia care". Thank you, Medtronic - we wouldn't be able to do all that we do without you!"

And now, if you are near your computer, we hope that you will check out APSF.org and click on the Newsletter heading, the 4th one down is the Newsletter archives and then you can click on the June 2019 issue. Alarm fatigue and patient safety by Keith Ruskin and James Bliss is the first featured article so let's check it out. This topic was requested by several of our twitter followers as well. Thank you for the great suggestion!!

We have come a long way from paper charting and manual blood pressure measurements in the operating room. Electronic medical devices provide monitoring, charting, and life support and have improved patient safety throughout the hospital. Each of these devices may have different audible and visual alerts and alarms whenever there is a change from the normal physiological parameters as an early warning for clinicians so that action can be taken to before a patient is harmed. Another type of alarm is a device function alarm that is intended to alert healthcare providers about device malfunction or failure. These alarms are absolutely essential for life support devices such as ventilators and cardiopulmonary bypass machines. In the clinical setting and especially in areas of high acuity such as the operating room and the intensive care unit, there is a high frequency of alarms. Studies have been done in this area. In one study, almost 9,000 alarms occurred during 25 consecutive procedures and 359 alarms during each procedure which ended up being about 1.2 alarms each minute of the procedure. One explanation for the high frequency of alarms is that equipment manufacturers set alarm defaults to a high sensitivity to prevent missing true events, but as a result many alarms have a low specificity and low positive predictive value and are often silenced or ignored. In addition, there are alarms for so many parameters that the result is often a loud and potentially distracting clinical care area which may compromise patient safety.

Alarm fatigue is defined as an increase in a health care provider's response time or a decrease in his or her response rate to an alarm as a result of experiencing excessive alarms. This is not unique to medicine and may occur in other professions as well, such as transportation. When there is such a high frequency of alarms (false and real), the professionals may ignore or actively silence the alarms in order to continue their work. Alarm fatigue has led to medical accidents and patient harm and the Joint Commission made clinical alarm management a National Patient Safety Goal. Alarm fatigue also falls into 2 of APSF's Perioperative Patient Safety Priorities related to preventing, detecting and mitigating clinical deterioration in the perioperative period and distractions in procedural areas.

Let's look a little closer at the research into excessive alarms. Bliss and Gilson created an early taxonomy of signaling terms taking into consideration the time between he signal and the associated situation. Signal refers to all stimuli that function as a general emergency notification. More specifically, alarm is defined as a transient sensory signal that may be auditory or visual in the face of a specific danger that requires an immediate action to correct it. An alert refers to a signal for an adverse event that may occur in the future. The purpose of the alert is to give the operator more time to react in order to prevent a problem from occurring while by the time the alarm sounds, the patient is in danger and needs corrective action immediately.

There is a current standard for medical alarms, IEC 60601 1-8 that addresses basic safety and performance requirements with alarm categories that are prioritized by degree of urgency and consistency of alarm signals. This standard does not report on high sensitivity of alarm setting and the resultant low specificity. The goal for a medical alarm is to give the health care professional ample time to act to prevent an adverse outcome. Let's look at a couple more definitions. Clinical alarms refer to a signal that the patient needs immediate attention and technical alarms refer to a signal that the biomedical equipment needs attention. For example, if the EKG displays ventricular fibrillation, this will lead to a clinical alarm but if the EKG is inadvertently disconnected this may lead to a technical alarm. The authors reports on another taxonomy of alarms by Xiao (Shee-owe) and Seagull for medical professionals monitoring clinical situations. I will include this table in the show notes as well. False alarms occur when there is no danger and are often the result of low sensor thresholds. Nuisance alarms occur when there may be a problem in a specific context, but the alarm is activated for a different reason that does not indicate patient danger. Inopportune alarms occur at the wrong time such as for a condition that may occur far in the future. These alarms must be distinguished quickly from actionable alarms that occur when there is a physiological change in the patient's condition that requires immediate action by the anesthesia professional whether it is increased monitoring for a mild deviation or mediation administration or initiating CPR for a life-threatening problem. Other alarms may occur that are nonactionable alarms in the setting of a monitoring artifact from skin prepping or electrocautery or a true deviation during a clinically insignificant abnormality such as holding ventilation at the surgeon's request and the hearing the ventilator apnea alarm.

With all that talk about different alarms and opportunities for false alarms, it is no wonder that alarm fatigue may occur and we are going to review this important topic now. Remember, alarm fatigue occurs when there is failure to respond to an alarm and this may lead to patient harm and it has led to patient harm. The US FDA reported more than 500 alarm-related patient deaths over a 5 year time period. This is an important area for improved patient safety and research has shown that improved alarm and alert design can lead to improved patient safety. It is a complicated area since we need alarms that are attention-grabbing to alert the provider that an abnormal event has occurred, but when there are many nonactionable attention-grabbing alarms, this leads to the cry-wolf effect.

[Insert wolf howl]

The cry-wolf effect is when health care providers become desensitized to frequent false alarms and this is more likely to occur during periods of high workload when providers mistrust or ignore subsequent alarms from the same or similar devices.

Alarms are often loud, right? They have to be to grab your attention, but loud and intrusive auditory alarms may lead to increased stress levels for the health care providers especially at night and may affect patients leading to poor sleep in the hospital and increased ICU delirium. Ruskin defined the term alarm flood to refer to the large number of alarms which may be in different patient care areas.

The scope of the problem of alarm fatigue is immense and the solution will also be complex. The authors write that "The overarching goals for a comprehensive solution to alarm fatigue should be to clearly and accurately indicate potential hazards while minimizing false or nuisance alarms." There are several areas for potential solutions including organizational aspects of the patient care area and layout, workflow and processes, and safety culture. There are things that we can do right now to help minimize the negative consequences from alarm fatigue with technical and engineering solutions, workload considerations, and practical changes to the way we use technology and ultimately these changes will lead to new initiatives in training, clinical workflow, and organizational policies. One simple solution is consistent signals across all equipment in the health care environment. In addition, it is important to minimize distractions due to noise, lighting, competing task demands, distrust, and inattentional blindness or deafness. New medical equipment should be designed to decrease the clinician's workload so that they can respond to alarms and alerts in a timely fashion.

Another solution that the authors propose involves changes to the alarm processing algorithms of our physiologic monitors to minimize the number of nonactionable alarms. For example, delaying alarm activation for short and clinically-irrelevant time periods due to minor violations can improve alarm reliability. This delayed alarm activation has been shown to decrease false alarms by 74% and values returned to within normal limits without clinician action before alarm activation. Improving alarm design may also be a good area for machine learning to simultaneously look at multiple parameters and minimize false alarms and improve alarm accuracy. There is definitely room for improved medical equipment monitoring design to increase the positive predictive value of clinical alarms leading to decreased false alarms and mitigating alarm fatigue.

What if we look at the sound of the alarm itself? Historically, it was thought that alarms needed to be loud if operators were going to notice them and respond in a timely manner. A recent study on alarm volume by Schlesinger and colleagues reported that physicians were able to distinguish alarms that were below the ambient noise level. The benefit of decreased alarm volume for non-life-threatening alarms is to lower the overall noise in the clinical environment.

Is there anything that we can do in anesthesia patient care areas right now with regards to alarm fatigue? It is important to use appropriate alarm limits for each patient rather than just the default clinical settings and disable non-essential alarms. This can decrease the perceived workload and help to improve alarm accuracy, operators' experience, and overall satisfaction. Some specific examples include using pediatric profiles for peds cases or using the paced mode for ECG in a patient with a pacemaker. Another solution to decrease false alarms from artifact is to replace disposable sensors when they need to be repositioned such as pulse oximeters or electrodes. Finally, it is important to use the appropriate monitors for each patient to avoid over-monitoring and the resultant increased number of alarms.

Alarm fatigue is an important consideration for patient safety during anesthesia care. Going forward, we can take some simple steps in the OR to minimize false and nonactionable alarms. In addition, this promises to be an exciting area for innovation with medical equipment manufacturers developing alarm processing algorithms to improve alarm accuracy and minimize alarm fatigue.

That's all the time we have for today!! Thank you so much for joining us today on this journey towards improved patient safety. If you have any questions or comments from today's show, please email us at [email protected] .

Visit APSF.org for detailed information and check out the show notes for links to all the topics we discussed today. Plus, you can find us on twitter @APSForg. Follow along with us for additional patient safety information tweets!! If you are enjoying listening to this podcast, please rate us and leave us a review!! We are so excited to continue to grow our Anesthesia Patient Safety Foundation Family!

Until next time, stay vigilant so that no one shall be harmed by anesthesia care.

© 2020, The Anesthesia Patient Safety Foundation

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Welcome to the next installment of the Anesthesia Patient Safety podcast hosted by Alli Bechtel. This podcast will be an exciting journey towards improved anesthesia patient safety. Today, we are going to review a featured article from the June 2019 Newsletter, "Alarm fatigue and patient safety" by Keith Ruskin and James Bliss.

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Personal Health Records for Patient's - How & Who It Helps

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Personal Health Records for Patient's - How & Who It Helps

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This video describes an example of a patient who would especially benefit from creation and use of a PHR. As the asynchronous material touched on PHR provided by patients can be extremely helpful in continuity and quality of care. The example in this video focuses on a patient in the military required to move frequently and transfer care between multiple providers and health systems.

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Personal Health Record (PHR) for Patients

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Week 10 - Quality and Unintended Consequences

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Week 10 - Quality and Unintended Consequences

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In our current healthcare setting, where national interoperability and EHR's are still years away from universal use, there becomes room for patient quality and safety issues. As the asynchronous material touched on, there have been increasing fail safes added to EMR's to ensure safe medication administration as well as avoid medication errors. Multiple warning notifications will pop up requiring physical reviews of medicine administration and overrides yet still, serious mistakes are made from human error and alarm fatigue contributing to these protocol overrides. It may be prudent for these software designers working on the fail safes and alerts in EMR's to consult healthcare workers and providers to understand alarm fatigue. They can take this information as well as data from studies regarding alarm fatigue to possible redesign the EMR alarms so that providers and healthcare workers will be more adherent to following protocols. Another possible solution could be to add algorithms that are applied when an medication administration alarm is override. This can ensure that even if a medication needs to be given outside of normal dosage parameters it can still be electronically verified to avoid possibly harming the patient from a mistake of human error.

Link: This study looked to further understand the root cause of alarm fatigue. It is believed for many that alarm fatigue is highly related to medication administration override rates. Two hypotheses were tested to find possible alert fatigue mechanisms: cognitive overload related to work and alarm desensitization related to repeated alarm exposure.

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Effects of workload, work complexity, and repeated alerts on alert fatigue in a clinical decision support system

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Although alert fatigue is blamed for high override rates in contemporary clinical decision support systems, the concept of alert fatigue is poorly defined. We tested hypotheses arising from two possible alert fatigue mechanisms: (A) cognitive overload ...

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Improving Sleep Hygiene with Gamification

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Improving Sleep Hygiene with Gamification

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Application of gamification are essentially boundless and can be used for almost any aspect to improve health. The popular Pokemon videogame franchise aimed to work its way further into gamification and improving players health. Previously the company incorporated health related gamification with PokemonGO, a phone application that has players catch Pokemon by walking around and provides in game improvements and incentives for those who are more active during gameplay. Pokemon Sleep is the next installment of the companies health gamification. This new game will encourage good sleep habits by tracking sleep and providing rewards for improved sleep and can help players identify changes needed to improve sleep habits to perform better in game.

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Do you find yourself struggling to get energized in the morning? Has the same old bedtime routine grown tiresome? Now, you can turn your sleep into entertainment with Pokémon Sleep! Playing this game is simple: just place your smartphone by your pillow, then go to sleep. Just like that, waking up in the morning becomes something to look forward to!

Your adventure takes place on a small island where you'll carry out research on how Pokémon sleep. You'll work with large Snorlax who live on the island and Neroli, a professor who's studying Pokémon sleep styles.

Place your smartphone by your pillow when you go to bed to record and measure your sleep. The longer you sleep, the higher your score in the morning, and the more Pokémon you'll see appear around Snorlax. Your nights of sleep will be classified as one of three sleep styles-dozing, snoozing, or slumbering-and in the morning, Pokémon that sleep in similar ways will come gather around Snorlax.

You may even discover a Pokémon sleeping in a rare style you'd hardly ever get to see normally. Each Pokémon has a number of different sleep styles, so see how many you can discover to complete your sleep style dex.

Researching Pokémon sleep styles is even easier when you use Pokémon GO Plus +, which allows you to keep track of your sleep with the press of a button. Pokémon GO Plus + uses Bluetooth Low Energy technology to link with Pokémon Sleep. When it's time for bed, just hold the device's central button, place it by your pillow, and then go to sleep. Not only will Pokémon GO Plus + help measure your sleep data, but Pikachu's familiar voice can give you cute prompts when it's time to wake up or go to sleep.

Pokémon Sleep is planned to launch worldwide, excluding certain regions, in summer 2023, and Pokémon GO Plus + releases on July 14, 2023.

Keep checking Pokemon.com for more details on Pokémon Sleep!

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Do you find yourself struggling to get energized in the morning? Has the same old bedtime routine grown tiresome? Now, you can turn your sleep into entertainment with Pokémon Sleep! Playing this game is simple: just place your smartphone by your pillow, then go to sleep.

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In todays day most every person has a smartphone with the ability to download applications and games. This technology tool has helped to create a more interconnected society and the medical and research world would be remiss to not capitalize on this wide distribution and utilization of technology. Games on smartphones and tablets range in difficulty, genre, an are always advancing creating more real world interaction between players and their environment. Incorporation and application of gamification can help usher in a new era of health care. Application of gamification can be incorporated in ways as discussed in the asynchronous material such as "Jerry the Bear" child Type 1 Diabetes teaching tool providing hands on learning in fun and understandable ways for children. Other gamification application includes applications requiring the player to perform physical activity to progress in virtual game world. This encourages promotion and creation of positive health habits while also providing patients with the virtual in-game rewards. Gamification can also be used to collect research data as phone applications are accessible around the world and in most languages, casting a wide net for researches to gather large data quickly. Further development and incorporation of gamification in the scope of health care and personal practices provide another key tool in the healthcare arsenal.

Link:
This site provides background of gamification within healthcare and some pertinent history on its development. The link also provides 15 examples of how gamification can be utilized in healthcare.

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The Top 15 Examples of Gamification in Healthcare - The Medical Futurist

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Gamified apps and devices will appear in every field making behavior change easier and fun. Here are the greatest examples of gamification in healthcare!

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This short video provides a description of the relationship between telehealth and interoperability. It touches on how these concepts integrate with each other and the importance of that relationship to better patient access and outcomes of care.

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Telehealth experience like never before with interoperability

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This video provides a brief overview of how the CardioMEMS remote PA Pressure monitoring system works in patients with heart failure. CardioMEMS remote transmission device technology is a relatively new advancement in remote monitoring in CHF patients to reduce and proactively prevent heart failure exacerbations and ideally lower CHF hospital readmission rates.

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Patient Education: How the CardioMEMS HF System Works

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Attached is an article outlining the various barriers and benefits that come with telehealth implementation. These could be used to support furthering the integration of telehealth and highlight the areas of telehealth that could be improved upon. The goal of telehealth is providing efficient, timely, and high quality care and by continuously critiquing and improving telehealth technology we can reach this goal.

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

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Technology continues to progress advancing at rapid and unprecedented rates for industries and consumers alike. It would only make sense for the healthcare industry to adapt to this dynamic change incorporating new technology tools and bringing telehealth into the forefronts of healthcare. COVID, its restrictions, and need to cut healthcare costs (for consumers and providers) forced providers and care networks into the world of telehealth incorporation. This large jump into a relatively new healthcare delivery system provided a great deal of pro's and con's which made the concept and technology a polarizing point. For those unfamiliar or uncomfortable with the technology or working with digital mediums it created much frustration and seemed to be more a hinderance from providing care. For those providers able to adapt and especially patients, telehealth allowed much more flexibility and immediacy of care, reducing unnecessary trips to doctors offices or ER's which could be handled via telehealth. The seemingly largest issues is not the concept of telehealth its self but how it was implemented. Technology's constant progression makes it impossible for medicine to maintain stagnancy in traditional healthcare visit and forces the need to adopt telehealth, which also aides in creating ideal healthcare interoperability foundations. Reflecting, the central issues with telehealth implementation was its immediacy in implementation. There was lack of preparation, communication, education, and training for use for providers and patients that created barriers for both groups and created resistance towards the paradigm shift. Naturally this was related to the urgency of need from COVID so the shift to telehealth use was swift and unorganized. With proper education, training, and implementation telehealth starts to emerge as the future of primary and even specialty healthcare.

Link:
The link attached is the U.S. Government website for telehealth information. Providing information for patients on how and where to access telehealth for care based on insurance and type of care required. Additionally emergency/hotline numbers are provided for patients.

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An official website of the United States government

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Connecticut's HIE "CONNIE" & Outpatient/Clinic Care

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Connecticut's HIE "CONNIE" & Outpatient/Clinic Care

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The state of Connecticut's official Health Information Exchange called Connie, was established May 3, 2021 by an independent, non-profit, neutral, trusted and secure company. Level of information exchange includes: point of care results, blood and lab results, radiographic/imaging exchanges (MRI, CT, US), as well as updates and notifications about patient clinical encounters. According to the "Connie Statute" within a year of Connie implementation commencement (5/3/21) all CT state licensed institutions (hospitals and labs) should begin the HIE application and integration process. Within 2 years of Connie commencement all CT state licensed institutions shall apply and participate in HIE (PCP, specialists, pharmacies, behavior health institutions and SNFs). At our CHF clinic we use the EMR integrated HIE connection portal to access HIE for patients reducing the need for repetitive and redundant echo testing. This also helps patient's PCP's and specialists to stay up to date with all aspects of patient care. Unfortunately not all this information is updated in real time and requires uploading of files which can take time and hinder timely care or communication. In theory the application and use of HIE would streamline, improve efficiency, and increase safety/quality of care within healthcare. As it stand now though there are obvious adoption "growing pains" and evolutions that must be worked out to achieve the most effective evolution towards this new model of healthcare information exchange.

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Our Mission

Connie's mission is to enhance the health and well-being of Connecticut residents through the provision of health information technology services. These services and promote patient-centered care. They also improve the quality, safety, and value of healthcare, while reducing the waste and duplication of services. will support clinical decision-making, keep patient medical health information confidential tate's public health goals

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Connie provides a safe, confidential way of sharing patient health information among doctors and healthcare organizations.

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Connecticut's Health Information Exchange Technology - CONNIE

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Connecticut's Health Information Exchange Technology - CONNIE

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Above is the link to a short and easily digestible PowerPoint presentation by the state of Connecticut's Office of Health Strategy regarding CT state specific HIE called "Connie". Included are data shared, participating organizations within the state, patient's rights and protection of data, and mandated requirements regarding use for healthcare providers and systems.

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Week 7 - Health Information Exchange (HIE)

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Week 7 - Health Information Exchange (HIE)

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Health Information Exchange (HIE) is a vital and increasingly growing aspect of healthcare that is helping to bridge information gaps between providers, hospitals, and specialists alike. HIE provides healthcare providers succinct, universal, and important patient information including history, test results, imaging, office visits or hospitalizations, and provider plan of care updates. This provides the link needed to connect EHR and EMR's information to a secure central database for all care providers to access increasing speed and quality of care, health outcomes, and reducing costs and redundancy in testing. The key aspect of HIE that makes it the missing link is its seamless and easy integration with already existing and verified EMR's reducing the "learning curve" for providers ease of use and immediate HIE integration. This provides comprehensive patient histories accessible for all providers in real time (so long as they participate and integrate HIE) easing and improving care quality and timing. The hope is that eventually these securely collected and protected datasets may be used for medical research purposes or applied in other fields of research. Ideally this leads to reduced costs and spending for medical research and increases data collection to better reallocate medical funding and improve healthcare as a whole. As of now HIE is strictly state specific without interstate communication abilities. Though, with improved outcomes and continued work towards nationally implemented HIE we may one day have a nationally accessible HIE database.

Link:
The attached YouTube video provides a brief overview of HIE and how it is used within healthcare. It additionally touches on some of the patient options for sharing of health information and requirements needed for organizations to adopt and implement HIE.

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What is Health Information Exchange (HIE)?

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This is the link to the HealthIT Clinical Decision Support (CDS) page, providing further information about current CDS used and strategies for implementation of CDS.

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This short 2 minute video provides a simplified and clear understanding of the differences between EHR and EMR's as well as how they evolved and developed over the years.

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Week 6 - Electronic Health Records and Clinical Decision Support

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Week 6 - Electronic Health Records and Clinical Decision Support

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EHR:
As is apparent from the asynchronous material patient access to their EHR can be a critically important tool for continuity of care, safe treatment, and patient advocacy. There are many facets to the positive impact this access can have for patient care and safety. It can allow for patients to have a greater understanding of their health and diseases as well as up to date access of treatments (medications/procedures) and plans of care. This wealth of knowledge for the patient allows them to take a more informed role in their care and treatment decisions. On the other side patients access to all this data without the knowledge or medical context/application to fully interpret or understand results, data, and evidence based care. Patients can take their results, medications, and treatment plans and perform a quick internet search and be overwhelmed by information that may or may not directly apply to them without having the full medical background and education. This unbridled access can cause a lot of misinformation and fear for the patient and may cause them to be reluctant with evidence-based treatments. For some this can delay treatment or cause patient's to even refuse indicated treatments. What this issue does highlight is the need for good patient education of diseases and treatments to ensure the patient is getting correct evidence-based information regarding their specific care plan rather than what a cursory internet search reports.

EMR:
The second aspect of this weeks content touches on system-specific EMR's and how they can hinder physicians and care of patients. System specific EMR's that do not communicate with outside groups/hospitals creates large gaps in patient's medical history (especially for patients who are poor historians) that can be critical to timely and effective treatments. Lack of centralized patient EMR that can be universally accessed by all providers involved in patient care is one of the most apparent issues. Beyond that having multiple EMR profiles for patient's who seek care from multiple health systems increases the amount of patient medical data out causing a greater security and privacy concern. Lastly EMR implementation can be underutilized or reduce efficiency of clinicians who are unfamiliar with it or how to find pertinent clinical information. This is especially true for specialty medicine who may be looking through years of charting to find specific tests/encounters. Implementation of tools such as the "Sphere" for specialties can help providers more efficiently and effectively adapt to use of EMR's while providing more time for face-to-face patient interaction ideally improving patient satisfaction with care.

Link:
The attached YouTube video discusses how with development and adoption of EHR's within healthcare issues have presented themselves. This includes ease of usability for providers and concerns of safety and protection of patient data.

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Electronic Health Records: Usability and Unintended Safety Issues

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Impacts on the Application of  Meaningful Use

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Impacts on the Application of  Meaningful Use

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Attached is a PowerPoint presentation done in a previous semester but provides a great insight as to the backlash and somewhat cyclical impact incentivized medical reimbursement has created for some. The presentation focuses on the ACA section regarding Hospital Readmission Reduction and how incentivized programs can so diversely affect providers. The variation in application is greatly impacted by location, funds, and feasibility, disproportionately impacting providers.

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ACA Hospital Readmission Reduction Program Presentation - NURP 450

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Meaningful Use (currently Version 1) application has produced helpful, powerful, and positive changes within healthcare. It has allowed the initiation and movement of healthcare toward more comprehensive patient medical profiles, reduced redundancy of testing, and improved patient through transition and use of EHR's. Unfortunately, as the second asynchronous YouTube video highlights that the "all or nothing" aspects of Meaningful Use have create a disciplinary system rather than incentive based system for adoption and progression of EHR use. Being limited by things such as cost, universal total application to a practice, and meeting the requirements of 100% compliance makes Meaningful Use a double edged sword. It can become impossible to logistically achieve for some practices and punishes those unable to meet 100% of the 10 requirements (each with various subsets or requirements) despite making efforts to move toward Meaningful Use application within their practice. Even for those practices doing everything right to push for this evolution and change they are still penalized and consequently can disincentive certain providers and practices. The positive effects create huge strides in interoperability of healthcare, sharing of health data information, and improving communication between patient and providers alike. The positive health outcomes after application support continued application of Meaningful Use but negative impacts go underappreciated contributing to reduction in amount of available providers for public health, critically impacting the accessibility of healthcare for some. Revision, collaboration, and individualized practice support from those trying to revise and universally implement and apply Meaningful Use could help ease some of these issues. Returning to Step 1 for root-cause analysis and data collection regarding Meaningful Use (especially now having data from current implementation) can help to create an even more sustainable revised version of Meaningful Use before moving directly to revision of Step 2.

The link attached is a research article identifying key points in assessing a practice's ready and preparedness for adoption and implementation of Meaningful Use within community clinics. This is particularly interesting as the general community clinics are the more underfunded and difficult practice settings to try to prepare for and implement Meaningful Use. More to that these clinics are the ones who could most benefit from receiving incentives seeing a vast portion of the population who use Medicare and Medicaid as their primary insurance providers.

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Systematic Review of IoT in Healthcare: Applications, Techniques, and Trends

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Systematic Review of IoT in Healthcare: Applications, Techniques, and Trends

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This systematic review details the underutilization, application, evolution, barriers, and concerns associated with the introduction of IoT and growth currently and in the future in the context of healthcare.

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Author links open overlay panel, , , , Ebrahim Mahdipour b

Show more

Abstract

(IoT) is an ever-expanding ecosystem that integrates software, hardware, physical objects, and computing devices to communicate, collect, and exchange data. The IoT provides a seamless platform to facilitate interactions between humans and a variety of physical and virtual things, including personalized healthcare domains. Lack of access to medical resources, growth of the elderly population with chronic diseases and their needs for remote monitoring, an increase in medical costs, and the desire for telemedicine in developing countries, make the IoT an interesting subject in healthcare systems. The IoT has a potential to decrease the strain on sanitary systems besides providing tailored health services to improve the quality of life. Therefore, this paper aims to identify, compare systematically, and classify existing investigations taxonomically in the Healthcare IoT (HIoT) systems by reviewing 146 articles between 2015 and 2020. Additionally, we present a comprehensive taxonomy in the , are included. Finally, based on the reviewed studies, power management, trust and privacy, fog computing, and resource management as leading open issues; tactile Internet, social networks, , SDN/NFV, Internet of nano things, and Internet of Things HIoT, analyze the articles technically, and classify them into five categories, including sensor-based, resource-based, communication-based, application-based, and security-based approaches. Furthermore, the benefits and limitations of the selected methods, with a comprehensive comparison in terms of evaluation techniques, evaluation tools, and evaluation metrics big data analytics blockchain as important future trends; and interoperability, real-testbed implementation, scalability, and mobility as challenges are worth more studying and researching in HIoT systems.

Introduction

Today's world has been involved with many challenges related to public health issues of chronic diseases due to threatening infections such as COVID-19. The rise in health problems along with high healthcare costs encourage everyone, especially elderly and disabled people, to use remote health management via computer-aided technologies (Garg et al., 2020), (Hosseinzadehet al., 2020a). In recent years, the Internet of Things (IoT), as a network of connected devices that interact with one another, plays an important role in enabling automation in many fields such as remote and smart healthcare systems. Technologically, the IoT consists of some popular technologies such as wireless body area networks (WBANs), wireless sensor networks (WSNs) (Mainetti et al., 2011), and radio frequency identification (RFID) to transfer the obtained data to the cloud for analyzing and extracting meaningful data for on-time proper decision making (Al-Fuqaha et al., 2015), (Asghari et al., 2019a), (Baker et al., 2017). With a rise in the desire to make healthcare more personalized, proactive, and cost-effective, the IoT can be considered and employed as a significant technology in health management systems (e.g., mental health and global pandemic (COVID19)) (Garg et al., 2020), (Al-Fuqaha et al., 2015). In this regard, the implementation of IoT in healthcare can be classified into three parts (Tsiounia et al., 2019): i) tracing people and other objects (staff, medical teams, and patients), ii) people authentication and identification, and iii) automatic data sensing and collection. For example, the IoT provides health monitoring anytime and anywhere around the human body with WBAN technology, and can prevent from hospital infections, emergencies management, and post-discharge care. Therefore, the IoT completely redefines devices, applications, and people in the healthcare domain (Habibzadeh et al., 2020), (Atzori et al., 2010).

Henceforward, healthcare environments can be extensively revolutionized by the use of IoT opportunities such as the Internet of Medical Things (IoMT) technology that includes connected medical sensors or special medical devices to provide a personalized approach to healthcare delivery (Gatouillat et al., 2018). The use of IoMT technology in healthcare systems, called Healthcare Internet of Things (HIoT) creates appropriate therapeutic strategies for patients by connecting medical devices to the Internet and carrying out various telehealth services such as the supervision of elderlies, telemonitoring, teleconsultations, and computer-assisted rehabilitation. In most articles, the terms IoMT and HIoT have been applied as alternatives for the integration of medical devices and applications that can be connected to health care information technology systems in an IoT-based environment (Habibzadeh et al., 2020). On the other hand, the convergence of big data and IoT is a new concept in sanitary systems that leads to smart management of the healthcare processes (Lu and Liu, 2011), (Karimi et al., 2021). Big data analytics has enabled prescriptive, autonomous, and predictive analysis of healthcare approaches. To this end, personalized healthcare enables remote monitoring of patients, diagnosis, early detection, and diseases prevention, especially chronic diseases such as diabetes, obstructive pulmonary disease, cancer, arthritis, and heart disease (Lloret et al., 2017).

To date, a comprehensive systematic literature review (SLR) of research on HIoT that specifies the advance in general and identifies research questions, trends, and future directions of HIoT in particular has rarely been conducted. With the increasing desire for IoT in healthcare, exploring a study scheme for HIoT systems is necessary. A systematic review identifies, categorizes, and synthesizes a comparative method of investigation and leads to knowledge transfer in research associations. To this end, we aim to answer the following research questions (RQs) by reviewing the existing studies:

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What are the major practical motivations behind associating IoT in healthcare systems?

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What research scopes exist in IoT-based healthcare systems? Besides, what achievements are there in this area?

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What are the existing techniques and approaches that enable IoT in healthcare systems?

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What are the existing open issues, future trends, and key challenges in HIoT? And what research directions should form in HIoT systems in the future?

We followed guidelines in (Brereton et al., 2007), (KitchenhamKeele, 2004) to conduct an SLR with the aim of identification, classification, and systematic comparison of the existing studies concentrating on IoT in healthcare. In other words, this study intends to present, systematically identify, and taxonomically classify the investigations on HIoT that leads to providing an exhaustive comparison with the analysis of limitations and potentials of the current papers. This review presents a systematic review of the recent researches focusing on applied techniques, methods, and tools in HIoT. Therefore, to determine the need for improving HIoT systems and introducing open issues and future trends, 146 studies are selected, categorized, and compared.

Here, the current study strategies, techniques, methods, best experiences, and practices used in HIoT are investigated. Additionally, this paper shows that studies in HIoT are growing rapidly and recognizes the need for using IoT in healthcare systems. In this regard, the outcomes of this SLR are impressive for the following cases:

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Researchers in IoT and modern healthcare who need exploration of the related studies

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Medical field experts interested in realizing the current methods, strategies, techniques, and tools with the existing constraints in HIoT systems

The rest of this paper is organized as Fig. 1: Section 2 describes the background, and Section 3 explains the related reviews and surveys in HIoT. Section 4 provides the research methodology, and Section 5 presents a taxonomy of the selected approaches and compares their features. In Section 6, the analysis of the results is discussed. In addition, Section 7 discusses open issues and future trends. Threats to validity and limitations are presented in Section 8, and finally, the conclusion is included in Section 9. Fig. 2 illustrates a reading map, so readers who are interested in IoT and healthcare can focus on Sections 1, 2, 3, 7, and 9. Sections 1, 5.1, 6, 7, and 9 are for those who would like to gain information about sensor management in HIoT, and Sections 1, 5.2, 6, 7, and 9 are interesting for resource management. Communication management comes in detail in Sections 1, 5.3, 6, 7, and 9. Application management is explored in Sections 1, 5.4, 6, 7, and 9. Finally, we recommend Sections 1, 5.5, 6, 7, and 9 to readers who are interested in gaining an overview of the security management in HIoT.

Section snippets

Research method

Contrary to an unstructured review process, the SLR method was applied to reduce bias and to follow a rigorous and precise sequence of methodological phases to research literature. An SLR depends on the well-defined review protocol to extract, evaluate, and document results (KitchenhamKeele, 2004; Jamshidi et al., 2013; Haghi Kashani et al., 2020b; Songhorabadi et al., 2020; Bazzaz Abkenar et al., 2020b), as depicted in Fig. 4. Then, an SLR method that includes planning, conducting, and

Open issues, future trends, and challenges

Now, there exist significant problems to improve IoT-based healthcare systems that have not been studied much in the current research trends. In this section, to answer RQ4, open issues, future trends, and challenges in HIoT systems have been debated.

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RQ4: What are the key open issues, future trends, and challenges in IoT-based healthcare systems?

After analyzing the collected data through this review on the HIoT field and also regarding the increasing requirements for applying effective HIoT

Threats to validity and limitations

Threats to the validity of this research study are considered in several steps as follows:

Step 1. Threats to the identification of complete primary articles: In the research strategy, the most crucial factor is to collect comprehensive available literature without any bias. To this end, the common strings were searched and combined in the search term. Besides, a review protocol was designed to identify relevant and unbiased studies.

Step 2. Threats to selection and data extraction: In systematic

Mostafa Haghi Kashani received his B.S. in Computer Engineering from Kashan Branch of IAU, Iran, in 1999 and the M.S. in Computer Software Engineering from South Tehran Branch of IAU, Iran in 2002. He is currently a full-time Ph.D. Candidate in Computer Engineering-Software Systems at Science and Research Branch of IAU, Tehran, Iran. He is a researcher and lecturer in the Department of Computer Engineering at the IAU University. His research interests include distributed systems, fog computing,

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Mostafa Haghi Kashani received his B.S. in Computer Engineering from Kashan Branch of IAU, Iran, in 1999 and the M.S. in Computer Software Engineering from South Tehran Branch of IAU, Iran in 2002. He is currently a full-time Ph.D. Candidate in Computer Engineering-Software Systems at Science and Research Branch of IAU, Tehran, Iran. He is a researcher and lecturer in the Department of Computer Engineering at the IAU University. His research interests include distributed systems, fog computing, IoT, healthcare, big data, and evolutionary computing. He serves as an Editor for Frontiers in Big Data and Frontiers in Artificial Intelligence. He has acted as a reviewer in several international journals, including the IEEE Transactions on Industrial Informatics, Journal of Supercomputing (Springer), the International Journal of Communication Systems (Wiley), Multimedia Tools and Applications (Springer), and other top journals in the field.

Mona Madanipour received her B.S. degree in Computer Science from Shahed University, Tehran, Iran, in 2014; she is an M.S. student in Information Technology, Computer Networks, in Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran. Her research interests include IoT, e-Health, fog computing, recommender systems, machine learning, and data mining.

Mohammad Nikravan received his B·Sc. in Computer Software Engineering from the Islamic Azad University, Mashhad Branch, Iran, in 2002. He also received M.Sc. degree in Computer Software from the South Tehran Branch, Islamic Azad University, Tehran, Iran in 2005 and Ph.D. degree in Computer Software from the Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran in 2018. Currently, he is an assistant professor of the Department of Computer Engineering Shahr-e-Qods Branch, Islamic Azad University, Iran. His research interests include the Internet of things, 6LoWPAN networks and cryptography, and network security.

Parvaneh Asghari is a full-time faculty member and Assistant Professor in the Department of Computer Engineering at IAU, Central Tehran Branch (IAUCTB). She received her B.S. degree in Computer Software Engineering from Sharif University of Technology, Tehran, Iran, in 1994, her M.Sc. degree in Computer Software Engineering from Iran University of Science & Technology, Tehran, Iran in 1997, and her Ph.D. degree in Computer Software Engineering from IAU, Science and Research Branch, Tehran, Iran, in 2019. Her research interests are in areas of distributed systems, IoT, healthcare, cloud computing, and service-oriented computing. She also serves as a reviewer for various journals such as IEEE Communications Surveys and Tutorials, the Journal of Network and Computer Applications, Cluster Computing, IEEE Access, Human-centric Computing and Information Sciences, IET communications, RVT, and many other top journals in the field.

Ebrahim Mahdipour received the B.S. degree in computer engineering, specialized in hardware engineering, from the Dezful Branch, Islamic Azad University, Dezful, Iran, in 2003, the M.S. degree in computer engineering, specialized in computer architecture, and the Ph.D. degree in computer engineering, specialized in computer architecture, from the Science and Research Branch, Islamic Azad University, Tehran, Iran, in 2006 and 2012. He is the founding director of the Cyber Security Research Center, and he is currently an Assistance Professor with the Department of Computer Engineering, Science and Research Branch, Islamic Azad University. His research interests include cyber security, blockchain, and big data.

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A systematic review of IoT in healthcare: Applications, techniques, and trends

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Author links open overlay panel, , , , Ebrahim Mahdipour b Show more (IoT) is an ever-expanding ecosystem that integrates software, hardware, physical objects, and computing devices to communicate, collect, and exchange data. The IoT provides a seamless platform to facilitate interactions between humans and a variety of physical and virtual things, including personalized healthcare domains.

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Healthcare IoT (DIWK) & Interoperability 

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Healthcare IoT (DIWK) & Interoperability 

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Discussion of DIKW and review of asynchronous material it became clear the connection that application of the DIWK framework had on the development of Internet of Things (IoT) and integration of technology tools using IoT. DIWK involves taking raw data and contextualizing and applying it into useable wisdom. Cloud-based data and storage integrates multiple devices and information sources that can be used from anywhere in the world. Taking that and applying it to medicine brings the concept of interoperability into a more important role. Discussion surrounding use of healthcare IoT raises many concerns and barriers but one that personally is apparent is the need for better interoperability devices gathering and saving health care data and distribution of that data. Having healthcare-centric IoT is a huge step forward but without equal and secure access by providers, researchers, hospitals through seamless interoperability the potential wisdom remains useless or applicable only to those with access.

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Week 4 - Transforming Data, Information, Knowledge,  & Wisdom

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Week 4 - Transforming Data, Information, Knowledge,  & Wisdom

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Each week we are introduced to various pieces of our healthcare puzzle that pull the main concept of health informatics into the forefront of importance. A central idea or theme that I've noted from week to week is the complete, appropriate, and up-to-date integration of healthcare and the ever evolving world of technology. We have been provided an untenable amount of technology and information in regards to healthcare on a personal scope and in the broadest sense. The internet has provided us endless connectivity to reputable medical sources of information and research that is being updated by the minute, accessible at the click of a mouse. Where we continue to fail is the seamless integration and application of this wealth of data, information, knowledge, and technology. We are at a point in society where every day the technology we use provides us with contextualized information and knowledge about our health and well-being that is underutilized or ignored. Smartwatches, Point-of-Care testing, personal EMR and medical histories available on our cell phones; we are at the peak of knowledge collection. At this point we require the next step of taking all this knowledge and contextualized information and applying it to guide treatments and interventions and achieve wisdom. Acknowledging patient's independent understanding and knowledge of their personal health data is the first small step. Next patients will have to utilize this technology and health knowledge an apply it in real time to seek quick treatment, preventative care, or optimization of current therapies. Knowledge is great and we have achieved an excess of it, we now need to add the final puzzle piece and transform and empower patients and providers alike to autonomously process and apply this wealth of knowledge to achieve wisdom.

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Health Information Technology (HIT): Promise and Progress 

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Health Information Technology (HIT): Promise and Progress 

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Commentary research article discussing the benefits economically and individually that HIT currently and can provide. Additionally it highlights various barriers and responsibilities patients, providers, health systems, and data companies face in widespread implementation of HIT to push forward into a new generation of health care.

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ABSTRACT

Healthcare costs have risen faster than the rate of inflation for decades. The resulting resource constrains have placed substantial burden on organizations, governments, and societies. While the problem is most prominent in the United States, it is a substantial problem all over the world as populations age. Health information technology holds great promise at helping to alleviate these problems. In this commentary, we use the lens of health economics to outline how health IT holds the potential reduce of some these inefficiencies, highlight the progress that has been made, and provide a framework to connect the lessons learned in health economics with the current efforts in health IT. We discuss ideas at the population, organizational, and individual levels. To this end, we highlight the papers in this special issue which deal with both organizational level healthcare deliver issues and individual-level self-care.

KEYWORDS: Health Information technology, Health Economics, Healthcare management

1. Introduction

The ever rising cost of healthcare is an ongoing problem for many organisations, governments, and societies. In some countries, such as the United States, the rising cost of healthcare is arguably among the biggest problems the country faces since it consumes a large and ever rising share of available resources. People die due to a lack of care caused by too little prevention, treatment, and management, which are increasingly influenced by cost issues. Since cost is a driving concern in healthcare, lessons from health economics can provide relevant guidance as to what the main issues are.

Costs can roughly be divided into production and transaction costs. Production costs, those associated with producing something of value, are our focus here. Transaction costs are different, associated with coming to an agreement as to what to produce at what price and the costs of enforcing that agreement. While these costs have not received as much attention, they are substantial in healthcare. Administrative costs represent 25% of overall healthcare costs in the U.S. and anywhere from 12% to 19% in other developed economies (Himmelstein et. al., 2014; Rosenthal, 2017). In part this is because healthcare is usually paid for with insurance and highly regulated, which generates substantial paperwork. It is here that IT can make a significant contribution since it can reduce transaction costs (Malone, Yates, & Benjamin, 1987). If paperwork costs could be reduced, those resources could be redirected into actual life-saving care.

In addition to reducing transaction costs, IT holds potential to reduce production costs in healthcare. The late Nobel Laureate, Ken Arrow, is widely attributed with starting the sub-field of health economics with his 1963 article (Arrow, 1963). Fundamental assumptions related to how markets and organisations operate are violated in healthcare, which makes cost containment more difficult than in other contexts. For example, there is generally an obvious answer to the question "who is the customer?". However, in healthcare it is not clear if it is the patient or the insurance company. This is known as a third-party payer issue. Arrow also pointed out that healthcare is dominated by information issues, both between providers and between patients and providers. Since information issues are particularly important and healthcare information technology is explicitly designed to help solve such information-related problems, health IT holds promise to reduce healthcare costs.

2. People, data and information, and devices

Unfortunately, to-date, evidence of cost reduction benefits has been inconsistent. IT investment alone often does not yield value, but rather yield value when used in combination with other complementary investments such as training or new processes (Jones, Heaton, Rudin, & Schneider, 2012). Also, many IT investments are complex technologies that require research, development, learning, and training time; thus, it takes time to yield value. Finally, many of the process improvements come not from IT developers, but rather over a period of time from users. In short, IT investment often requires time, complementary investments, and user input to yield value (Jones et al., 2012). The papers in this issue are several excellent examples of these intermediate steps. In medicine and healthcare, IT affects different groups with diverse needs and preferences: consumers, patients, caregivers, clinicians, and researchers. Because it is healthcare, it touches all of our lives through the different people involved, the data that is created and used, and the devices that are being developed.

3. People

Following the health economics theme, one of the earliest and most fundamental models in health economics is the Grossman Model on the demand for health (Grossman, 1972). The Grossman Model is a human capital model that outlines several of the basic factors driving the demand for healthcare at an individual level. Since this is a "human capital" model, health is seen as an asset and the model outlines key ways that asset could come to be. Among the issues Grossman pointed out was that the demand for healthcare is a latent demand. People do not want healthcare per se, they want health. He also pointed out that health is somewhat unique in that it can be both purchased, through healthcare, and produced by the individual through lifestyle choices. People can make themselves healthier - produce health capital, in Grossman's words - through healthier living. While this seems a simple point, a key observation flowing from this is that people vary substantially in how well they produce their own health. Furthermore, it was observed that there was a strong link between education and health, resulting in arguments that more educated people are more efficient at producing their own health. They are better equipped to process complex medical information, and they more efficiently seek out information as it relates to their health. As context, this link is so strong that in development economics this can imply that once some basic health conditions are met, if you want to improve population health in the long run, the resources might be better spent on education than healthcare. While this highlights only one aspect, the link between the ability to process information and human health is a very old and important one. This matters going forward because the well-known effects of education could be quite similar to the effects of IT designed to help the general public better understand issues related to their health. In short, IT holds great promise for healthcare.

The effects of IT affect all, but are obvious when considering the different stakeholders. For almost a decade now, health information is just a search engine away and 80% of online users (Fox, 2011) read health- and medical-related information on the Internet. This number can be expected to be at least as high today if not higher. However, not all the information is of equal quality. As many physicians will lament, "Dr. Google" is often a source of misinformation for patients. However, one recent nationally representative study in the U.S. showed use of health-related information was positively associated with a wide range of health behaviours, such as exercise, and preventative care measures (Wimble, 2016). This shows early evidence that use of health information online is, on average, a good thing. This information is generally considered available to a broad part of the population, with more than 75% of adult Americans owning a smartphone and more than half (62% of adults, 75% of those 18-29 year olds) using it to look for health information (Smith, 2015). Additional, often complementary, types of information are also shared, eg, through the devices being created, eg, apps to monitor health status, and access to online support and information sharing sites (eg, PatientsLikeMe, CaringBridge.com). In addition to this direct and visible impact on this large group of stakeholders, there are many advances providing indirect benefits such as effects on availability of experts, eg, telehealth for expert diagnosis, record keeping through electronic health records (EHR), and other administrative tasks, eg, through patient portals.

Clinicians and other medical professionals have similarly seen significant changes in their occupation. It may have made executing their profession easier in many cases, but it has also increased the expectations from patients and others they interact with. They benefit from easy access to information for furthering their education, ranging from listservs for professional question-answering to online courses for CMI (continued medical education credit) and online degrees. In addition, many software systems have been introduced to support and improve current care. Examples include EHR to maintain patient information and software to support decision-making, eg, dose calculations, as well as providing alerts and reminders, eg, for medication interaction, clinical trial opportunities, or immunisation. Naturally, expectations have increased, and overall the goal and expectation is to use IT for better care with optimised treatments, fewer errors, and faster response times.

4. Data and information

In addition to the people component, information technology has had enormous effects on the types, diversity, and amounts of data and information being collected as well as its use for research and product development. Medical research information is accessible using the Entrez system, provided by the National Library of Medicine, which provides access to seven different types of information including published research (eg, PubMed, and PubMed Central with free full-text journal articles), gene and protein information, and taxonomies among other types of data. In addition to these federally curated resources, there are several very useful resources, such as Wikipedia and several lesser known online encyclopaedias, that provide high-quality information, such as ganfyd.com (a free medical knowledge base) or the Cochrane site for summaries and reviews of evidence from research ( http://www.cochrane.org).

Besides these information sources, raw datasets are slowly but increasingly being made available for research. A difficulty with making these datasets available is the need for de-identification of the data to protect the privacy of those whose data are contained in it. For example, the MIMIC corpus (Johnson et al., 2016) ( https://mimic.physionet.org/) contains de-identified health records for more than 40,000 critical care patients. Several datasets are available for cost and usage calculation, such as Medicare provider datasets (C.f.M.M. Services, 2017) containing information on services and providers and the H-CUP administrative databases (AHRQ, 2017) containing information on inpatient stays, emergency department visits, and ambulatory surgery in the U.S. Several more are available online, eg, at HealthData.gov.

Knowledge sources useful to integrate and combine data are also widely available. They provide opportunities for adding meta-information and cross walks between disciplines. One of the most well-known resources is the unified medical language system (UMLS) (McCray & Nelson, 1995), which combines vocabularies from different medical sub-disciplines in a variety of languages. The Gene Ontology (Ashburner et al., 2000) provides descriptions of gene functions and their relationships. Access, creation, and overviews of ontologies are also being facilitated. For example, Bioportal (National Center for Biomedical Ontology, 2017) provides access to hundreds of ontologies useful to biomedical researchers.

5. Devices and algorithms

Devices have undergone an exciting revolution with both inventive and novel hardware and software approaches. There are many options for healthy living, including sensors to track sleep and track movement as exercise or as warning systems with elderly users, and a variety of apps to calculate food intake or display warnings about a variety of conditions (eg, air pollution). Recent progress with the Internet of Things may facilitate and fasten much of the progress, although few projects focusing on the Web of Things are directly related to healthcare at this time (Tran, Sheng, Babar, & Yao, 2017). There are similarly many exciting developments for medical practice and clinical encounters, sometimes held back by policy. Policy and non-IT issues often impede progress; for example, being licensed in one state or country does not allow clinicians to practise remotely in other countries, or services performed using telehealth require different reimbursement approaches.

Several algorithms and knowledge sources are being developed to create and use this data and information. An exciting development is the increasing availability of libraries containing standard, but often sophisticated, algorithms that can be used by everyone with basic programming skills. While some may argue that many misuse the algorithm because they lack understanding, in general, it is an exciting development that using a classification or clustering algorithm does not require days of programming but only installation of a library in the preferred programming environment (Java, Python, R). Today we see this evolution all over again with the popularisation of deep learning and the increasing availability of libraries in Python and Java.

6. Future work needed

Thinking about people, data and information, and devices/algorithms in terms of the mechanisms by which they can reduce cost and inefficiency in healthcare can provide us with guidance as to what future work remains at the intersection of healthcare and IT. These can roughly be divided into the effect on producing health, either by healthcare providers or by patients making healthier choices, and reducing the paperwork costs associated with providing healthcare. Most research in health IT focuses upon the former - things that directly produce health. This category comprises a broad set of processes ranging from medication adherence intervention with patients to data mining in bioinformatics to increase the effectiveness of (personalised) medicine. These are the focus of the studies in this special issue, but we also want to highlight the potential gains from research focusing upon the latter issue. A graphical depiction of this is shown in Figure 1.

IT research and development projects in the medical domain vary widely. Some focus on creating data and knowledge sources, others on evaluating and understanding outcomes of policy and guidelines on usage and costs, and many focus on creating new programmes, applications, and software packages. However, the field is very broad and much work remains to be done. Below we provide a broad overview with example projects that would be beneficial and move the field forward.

As with all IT, there is improvement that can be made in interface, use of new interaction methods, and ease of and opportunity to access. However, in medicine there are two interacting stakeholders in the clinical process: patients and clinicians. For patients, the increase of available information provides them with a significant improvement. However, this is also abused, and fake news, discoveries, and cures are rampant. New technologies are needed that can inform, warn, or protect consumers from this information. Tools to increase healthy behaviours will continue to be important research streams, but there is much room for new tools for different populations, such as those with mental or physical disabilities, different age groups, and different goals for their health and condition. For clinicians, better decision tools are needed that learn and improve through use and adapt to each unique environment. For example, a 2011 review (Jaspers, Smeulers, Vermeulen, & Peute, 2011) showed that slightly more than half (57%) of 91 studies showed improved practitioner performance and only a third (30%) of 82 studies showed improved patient outcomes through use of decision support systems. We should aim for much better impact and approaches to measuring improvements, problems, and errors.

The existing resources would benefit from easier integration, open source access, and more comprehensive coverage. For example, the UMLS does not contain occupational health ontologies. Resources for processing social media language are nearly non-existent, eg, libraries of slang or emoticons. Continued use of software libraries will help increase research and development. These should focus on providing both basic algorithms and development tools for apps for all types of mobile devices, both those that are specifically useful for healthcare, as well as those that can serve indirectly, such as smartphones or watches. With all new development, much more attention should be paid to privacy, security, and protection of data and information. Better tools to avoiding hacking through records, medical devices, or human error and negligence are needed. Cybersecurity and privacy courses should be part of the medical curriculum to provide a minimum knowledge base about proper security methods and are needed even more for people in leadership roles who bear responsibility for others and their information. Finally, seamless integration in practice and daily life cannot be ignored. Current use of IT in clinical encounters is often awkward and clumsy. Research on better interfaces is urgently needed, as is research on workflow in the clinical environment to make optimal use of IT.

7. Conclusion

The papers in this section, while not directly addressing costs, do address intermediate outcomes related to cost and follow the general reasoning we outline above. More specifically, two of the papers address issues related to how healthcare is provided. The third paper addresses how health is produced by individuals. The first paper presents a technology tool to help better understand how processes occur in the healthcare setting, which is a key step in understanding how to improve efficiency and lower costs in healthcare provisioning. The second paper develops a model to forecast factors that are likely to lead to readmission, a well-known cost driver in healthcare. The final paper presents the results from a health infomediary that allows patients to share knowledge with other patients, which should allow patients to better care for themselves. We feel these studies represent important new knowledge that can help researchers and practitioners better understand how to improve health production by both providers and the patients themselves. Additionally, we hope to bring attention to the potential to improve health production as well as the potential for IT to reduce the inefficiency caused by the paperwork associated with healthcare provision.

Disclosure statement

No potential conflict of interest was reported by the authors.

References

Agency for Healthcare Research and Quality (2017). HCUP databases.

Ashburner M., Ball C. A., Blake J. A., Botstein D., Butler H., Cherry J. M., ... Sherlock G. (2000, May). Gene ontology: Tool for the unification of biology. [[Nature Genetics, 25, 25-29. PubMed] PMC free article] [Google Scholar]

C. f. M. M. Services (2017). Medicare provider utilization and payment data: Physician and other supplier.

Fox S. (2011). [Health topics. Washington, DC: Pew Research Center's Internet & American Life Project. Google Scholar]

Himmelstein D. U., Jun M., Busse R., Chevreul K., Geissler A., Jeurissen P., ... Woolhandler S. (2014). A comparison of hospital administrative costs in eight nations: US costs exceed all others by far. [Health Affairs, 33(9), 1586-1594. [PubMed] Google Scholar]

Jaspers M., Smeulers M., Vermeulen H., & Peute L. (2011). Effects of clinical decision-support systems on practitioner performance and patient outcomes: A synthesis of high-quality systematic review findings. [[Journal of the American Medical Informatics Association, 18, 1. PubMed] PMC free article] [Google Scholar]

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Jones S. S., Heaton P. S., Rudin R. S., & Schneider E. C. (2012). Unraveling the IT productivity paradox - Lessons for health care. [New England Journal of Medicine, 366(24), 2243-2245. [PubMed] Google Scholar]

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National Center for Biomedical Ontology (2017). BioPortal. Available: http://bioportal.bioontology.org/

Rosenthal E. (2017, March 29). Those indecipherable medical bills? They're one reason health care costs so much hospitals have learned to manipulate medical codes - Often resulting in mind-boggling bills. New York Times Magazine.

Smith A., (2015, U.S. Smartphone use in 2015. Retrieved from: http://www.pewinternet.org/2015/04/01/us-smartphone-use-in-2015/

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Articles from Health Systems are provided here courtesy of Taylor & Francis

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Health information technology: promise and progress

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Healthcare costs have risen faster than the rate of inflation for decades. The resulting resource constrains have placed substantial burden on organizations, governments, and societies. While the problem is most prominent in the United States, it is a substantial problem all over the world as populations age.

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National Coordinator for Healthcare Technology 

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National Coordinator for Healthcare Technology 

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HealthIT.gov | the official site for Health IT information

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HealthIT.gov is the leading national resource on health information technology (health IT) for both consumers and health care professionals. Come learn more today!

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For true medical interoperability to the greatest, most beneficial and efficient extent to patients, practitioners, and health care systems there is one key problem I keep coming back to. Willingness. This presented ideal interoperable medical world requires willingness amongst patients, practitioners, healthcare systems, security companies, and countless other integral parties. Willingness of patients to share their personal information in a globally available but highly protected and secure manner and adapt and participate with new medical technologies as we progress. Willingness for sharing of information, willingness to collaborate, adapt, and learn interdisciplinary but possibly internationally amongst medical providers. Willingness for large healthcare systems and EMR service providers to work together and evolve using universal language and share information to expedite care, prevent redundancies, and create even greater shifts to preventative medicines. This can be extrapolated to a huge degree if you consider all of the parties that would need to be involved to create the idealistic world of total healthcare interoperability. Technology companies will have to share the patient's health information with the central databases for use by physicians, researchers, and patient's alike.

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This is a 2020 study of the application of CardioMEMS to use as supportive evidence and resource for those interested in the study.

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TIGER INITIATIVE: Stage 4 Application - CardioMEMS

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TIGER INITIATIVE: Stage 4 Application - CardioMEMS

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Summation of Stage 4 is taking active role in incorporating, researching, and designing tools that can be integrated easily and serve the nursing, other interdisciplinary medical professions. At the CHF Disease Management clinic I currently work at we use a relatively new evidence-based fluid management monitoring device called a CardioMEMS (Abbott). We follow patient's PA Pressure's via remote transmissions from the minimally invasive implanted device. We also work very closely with all aspects of their cardiology care in relation to monitoring (primary cardiologist, EP, CHF, and other specialists). Initially it was used for monitoring only and done on a separate unintegrated system, over the years with more information, research, and collaboration with cardiologist's and CardioMEMS specialists we were able to give pertinent nursing feedback from use in practice. Having now used the CardioMEMS device in clinical practice for 3 years I've been able to see its evolution of use. Now the device is used proactively, coverage of implant increased greatly for even CHF Class IV patients, and has become integrated into the EMR for all specialists to see and use.

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Monitoring Pulmonary Artery (PA) Pressures is Proactive and Actionable

The CardioMEMS HF System remotely monitors changes in pulmonary artery (PA) pressure, an early indicator of the onset of worsening heart failure. These early changes can often be addressed through simple adjustments to care, such as titration of medications, most often without requiring an appointment with the patient.

Traditionally, clinicians have focused on physiological markers (patient weight, blood pressure, etc.) to detect worsening heart failure. Unfortunately, these markers appear late in the time course of decompensation. Relying on them leaves little time to respond before hospitalization is necessary.

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The CardioMEMS HF System remotely monitors changes in pulmonary artery (PA) pressure, an early indicator of the onset of worsening heart failure. These early changes can often be addressed through simple adjustments to care, such as titration of medications, most often without requiring an appointment with the patient.

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After this weeks asynchronous materials and reading a bit more about the TIGER Initiative it made me realize the paradigm shift nurses naturally progress through. We start generally in clinical roles, direct patient care, and bedside nursing for a few years growing a wealth of knowledge, assessing the flaws in systems, and seeing direct effects of informatics application on patient care. Naturally nurses experience evolution and are able to take all this experience and knowledge and begin application on a broader scale to improve nursing as a whole and in turn patient care. Now moving towards practitioner roles we can begin even greater contribution and application to informatics to continue pushing towards bettering our healthcare system and promoting health.

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The Good: This introduction to informatics this highlighted some of the amazing comprehensive care that could be achieved when applied properly. Comprehensive and universal applied informatics can provide more efficient, cost effective, and patient centered  care. We've also seen how technology has affected the boom in medical data collection. So much that we now struggle to keep up with efficient application and use of this data.

The Bad: We also see that there is extreme disconnect between what the patient expects as their idea of patient care and what the larger governments and insurance companies see as "proficient". The irony is that there is such an emphasis on the best way to cut spending and health care costs and unfortunately the solution falls to the detriment of the patient or compromise of their care. When in reality if there were more centralized, universal, and standardized distributions of health care and health records a significant amount of money would be saved.

The Thoughts: No matter how, if we shift to a centralized open communication EHR, and focus more on preventative patient centered models of health care it would not only improve patient health outcomes (which we would think would be the most important goal) but also save money. Money that may have been spent on unnecessary testing or frequent readmissions. This can be extrapolated down to the amount of money an office would save on paper with the removal of excessive faxing. Ideally with this money saved it can be reinvested into programs to focus on health promotion and preventative patient care to boost the health of the patient population as a whole.

The Link: The link attached provides some ideas for thought on approaching EHR with "Plan-Centric EHR".

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In these difficult times, we've made a number of our coronavirus articles free for all readers. To get all of HBR's content delivered to your inbox, sign up for the Daily Alert newsletter.

The Covid-19 pandemic presents the U.S. health care system with a mind-boggling array of challenges. One of the most urgent is coping with a simultaneous glut and dearth of information. Between tracking outbreaks, staying abreast of the latest information on effective treatments and vaccine development, keeping tabs on how each patient is doing, and recognizing and documenting a seemingly endless stream of weird new symptoms, the entire medical community is being chronically overwhelmed.

Sorting through large amounts of information and finding the nuggets that apply to a particular patient's situation is something that computers ought to be good at. But we still have problems of knowing what data is important and what is the right treatment and prevention plan for each patient.

During the Obama administration, the federal government supplied billions of dollars - and providers kicked in billions more - to speed the adoption of electronic health records. But even though up to 96% of hospitals and 86% of physician offices have adopted them, we still don't have EHRs that can rise to the information challenges that clinicians face every day, let alone those posed by Covid-19.

Providers still encounter continual frustration on many levels: user interfaces and usability issues, the quality of the data entered, the limited ability of the data to support discovery and interoperability among systems, just to name a few. These limitations have confounded the ability of clinicians to deliver care during the Covid-19 crisis.

An overhaul of the electronic health record is overdue. It must go beyond fixing the user interface or improving interoperability. It must address the fundamental problems exposed by the pandemic. The overhaul must also support the ability of providers to adopt the new value-based-care business model of health care - one that rewards providers for outcomes rather than the volume of services and that shifts their focus from reactive sick care to the proactive management of health.

To address these needs, the electronic health record must transition from an emphasis on a person's medical record to an emphasis on a person's plan for health and from a focus on supporting clinical transactions to a focus on delivering information to the provider and the patient.

From the Record to the Plan

A redesign of the EHR is essential, but what should it look like? EHRs are reasonably good at the "record" part - keeping track of what happened to the patient - but they must evolve to address the "health" part by helping providers plan for what they want to happen. EHRs could become tools for making those plans and keeping them on track if we design them with that goal in mind.

Intermountain Healthcare, Virginia Mason, and Kaiser Permanente are pioneers in adopting the new health care business model. Their experiences point the way to the next generation of EHRs.

What would a "plan-centric" EHR system look like? It would include:

A library of care plans that covers a wide range of situations. Variations in patient circumstances and preferences would dictate variations in the plans. A patient with well-managed diabetes would have a different plan from one who is still struggling for control. A patient who lived alone would have a different plan from one who lived with a large, supportive family.

Algorithms to form a patient's master plan. Patients hardly ever have just one clear, manageable issue. A master plan would combine appropriate algorithms for treating, say, a patient's asthma, arthritis, depression, and weight reduction, automatically resolving conflicts and redundancies.

Care team support. Each team member - the patient's primary care provider, specialists, nurse practitioners, pharmacists, case managers and the patient - would see both the master plan and their own to-do list. Team members could assign tasks to one another.

The ability to traverse care settings, geographies, and different EHRs. The plan would need to travel seamlessly with the patient. Providers would have interoperable systems that could integrate a patient's plan regardless of its origin.

Decision support and workflow logic.The system must remind team members of upcoming and overdue activities, suggest changes in the plan when patient conditions and care needs change, and route messages to the appropriate team member regarding new test results or patient events.

Analytics for both individual patients and populations. The system must be able to assess how well the plan is achieving its goals, both for the individual patient and for the larger population that may be under the provider's care. It should be able to apply lessons learned in treating one patient to other patients.

Imagine a plan-centric EHR ready to deal with Covid-19, incorporating the latest evidence-based treatments into each patient's care plan based on their current status and underlying health conditions, and then feeding back data on how each patient responded in order to improve the plan for the next patient. Such capabilities could transform outcomes and save many lives.

From Transaction-Oriented to Intelligence-Oriented

As befits systems with origins in billing, the design focus of EHRs has been transactional: documenting a visit, retrieving a lab result, sending a prescription to the pharmacy. This focus is not all bad: It has reduced some types of errors and made it easier to generate work lists and logic to help ensure that the clinical order is complete.

However, exquisite transaction support is not enough to address the challenges that afflict care delivery: failure to follow the evidence, brittle operational and clinical processes, and the near impossibility of keeping up with advances in medicine. EHRs can compound these limitations by being very difficult to update.

We must reimagine the EHR not as a document but as a system that supports the generation and tracking of multiple documents, events, and processes. It must surround each transaction and clinical process with intelligence to ensure clinical appropriateness and sound execution.

It should help ensure that care follows the evidence, identify treatment options that result from the dazzling pace of medical discovery, and alert providers that care processes have deviated from acceptable levels of performance. This intelligence must detect acts of commission (the choice of an outdated treatment approach) and omission (a patient has failed to keep an appointment to see a specialist).

The EHR must provide the ability for clinicians to easily analyze patient data to discover new treatments, uncover safety issues, and identify unusual clinical findings. Such capabilities would have enabled, for example, the much faster discovery of blood clotting in Covid-19 patients.

Intelligence can be leveraged to help address clinician concerns with EHR usability. Logic that presents the physician with data and potential actions tailored to reflect the patient's conditions, the physician's preferences, and the medical evidence can save the physician time and improve the quality of care.

Many of these intelligence and plan capabilities are present to some degree in today's EHR. However, the old fee-for-service business model has not rewarded their refinement and extensive use.

Some providers - including Kaiser Permanente, Geisinger, Intermountain Healthcare, and UPMC - are using their EHRs in this way, but these organizations share a key characteristic: They insure a significant percentage of their patients as well as providing their care, and therefore their financial incentives are more like those of payers. They have already embraced the new value-based-care business model that the rest of the industry is moving toward.

Achieving the Intelligent, Plan-Centric IT Foundation

We will always need medical record documentation and transaction capabilities. An accurate, comprehensive health record is critical to the delivery of care and is also a required legal document.

How can we preserve these functions of the EHR while migrating it to the new intelligent, plan-centric design?

One major obstacle to fixing the EHR problem is that the health care industry is in the middle of a transition to the new business model. But the change is happening so gradually - in fits and starts, depending on the payer and the political environment - that it's difficult for providers and EHR vendors alike to gauge the appropriate moment for a system redesign. Consequently, providers will have to juggle two opposing business models for an unknown period of time, and their information technology portfolio will have to support both.

Exasperated users might support the idea of tossing out what we have and starting over from scratch, but that's not going to happen for a number of reasons: the stupefyingly large cost, the enormous development and implementation time, the disruption of operations, and the potential danger to patients during the transition.

Health care should take a lesson from banking. Instead of rewriting legacy systems, the banking community modified current systems, added complementary applications, and "wrapped" legacy systems with newer technologies and capabilities. To transform the EHR from a (quasi) document into the new design, we need a full array of complementary applications that "wrap around" appropriately modified EHRs and provide significant care-plan and intelligence support. Providers can make these investments as needed to match the pace and address the specific needs of their migration to value-based care and measure the return on investment as they go. These applications and capabilities might include the following:

Population health management. Providers will be accountable for the health and health care of populations of people with common health conditions such as diabetes and asthma. Population-health-management systems combine data from diverse sources (EHRs, claims, patient-monitoring devices, census, and other demographic databases that can track social determinants of health). The population-health-management systems "surround" the EHR so that the provider can view the plan from the record and the population health management system can send alerts and messages to the EHR inbox.

Health information exchanges. Connecting a wide variety of health care organizations in a region or state, the HIE enables them to exchange data about a patient. For example, when a patient presents at an emergency room, the care team can use the exchange to retrieve patient data from other care settings and get a complete clinical picture of the patient. Some HIEs have developed applications that measure regional care quality and costs, portals that enable patients to see their aggregated clinical data, and alert systems that tell a provider when one of its patients has been seen elsewhere.

Patient-health-management applications. These enable consumers to aggregate their health data, view their health status, track their appointments and prescription refills, converse with their care team, participate in care communities, view and alter their shared care plan, and research health issues.

Big data analytics systems. These aggregate very large amounts of health and health care data to compare the effectiveness of treatments, identify medication and device safety problems, facilitate medical discovery, and analyze shifting patterns of patient characteristics and diseases. Artificial intelligence can be used to support automatic correction of data inconsistencies and extraction of data from images, sound, and free text: for example, going through free text and pulling out quality measures or problems that were not previously in a patient's problem list.

Many health systems have begun to adopt the strategy of surrounding their EHRs with the next generation of intelligent, plan-centric capabilities. As the health care business model evolves, organizations such as Mass General Brigham (formerly Partners HealthCare), Memorial Hermann, Geisinger, CommonSpirit Health, and Cedars Sinai are vigorously implementing population health, big data analytics, and patient-health-management applications.

Embracing transformation

Health care delivery is in the early stages of an extraordinary change. This change is being driven by the relentless movement to the value-based care model and the problems exposed by the Covid-19 crisis. This ongoing transformation is paving the way for a new EHR design: a platform that fuses the current EHR with complementary systems, capabilities, and technologies.

Achieving the intelligent, plan-centric health care platform will require a level of industry cooperation that is unlike, and in some ways antithetical to, the way we've always done things. The pandemic has shown us health care collaboration at its best. In that respect, the response to the pandemic mirrors the new business model that we are trying to build.

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It's Time for a New Kind of Electronic Health Record

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Well before the Covid-19 pandemic struck, electronic health records were the bane of physicians' existences. In all too many cases, EHRs seemed to create a huge amount of extra work and generate too few benefits. The pandemic has made the deficiencies of these systems glaringly apparent.