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Who runs Primary Care?
...FNPs!

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With approximately 234,000 nurse practitioners licensed in the United States, they are increasingly playing a key role in both rural and nonrural primary care practices, according to study results published in Health Affairs. Increases were especially significant in rural practices, with the number of practices with nurse practitioners increasing from 35% in 2008 to 45.5% in 2016.

In primary care, nurse practitioners can perform most services that physicians provide, often at a lower cost. The use of nurse practitioners in primary care has been implemented at a growing rate as a way to address growing patient demand and improve care delivery.

"The number of physicians choosing primary care careers remains insufficient to replace those retiring," wrote the authors of the study. "This concern is especially acute in rural areas with long-standing provider shortages. Thus, it is plausible that practices will increase the use of providers other than physicians, such as nurse practitioners."

The increased amount of newly insured people seeking care due to Medicaid expansion has also highlighted the need for more providers in the primary care workforce, according to the authors. However, despite the growing need, little evidence exists on how nurse practitioners have been incorporated into primary care practices in recent years.

Researchers compiled data from SK&A, a commercial data set that includes practice-level characteristics of office-based physician practices in all states and the District of Columbia. These data were then combined with data from the Area Health Resources Files to determine each practice's rurality.

There was a significant increase in the presence of nurse practitioners among rural practices from 2008 to 2016, jumping from 17.6% to 25.2%. Although physicians continuously accounted for the largest proportion of providers in rural areas, they saw a relative decrease in the presence, from 69.4% to 60.5%.

With regard to the presence of nurse practitioners in nonrural settings, there was an increase from 15.9% in 2008 to 23% in 2016. At the same time, the presence of physicians decreased from 75.2% to 66.3%.

The researchers also examined the increase in the percentage of practices with nurse practitioners and the average number of nurse practitioners per practice. Compared with nonrural practices, a significant amount of rural practices employed at least 1 nurse practitioner. During the study period, the presence of nurse practitioners increased from 31.4% to 43.4% in rural practices and from 18.3% to 26.5% in nonrural practices. The average number of nurse practitioners increased in both rural (1.34 to 1.64) and nonrural (1.47 to 1.67) practices.

"Adding nurse practitioners is a useful way for practices to align themselves with contemporary efforts to improve access and performance," concluded the authors. "Our findings imply that primary care practices are embracing a more diverse provider configuration, which may strengthen healthcare delivery overall."
Reference
Barnes H, Richards M, McHugh M, Martsolf G. Rural and nonrural primary care physician practices increasingly rely on nurse practitioners. Health Aff (Millwood). 2018;37(6):908-914. doi: 10.1377/hlthaff.2017.1158.

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Nurse Practitioners Play an Increasing Role in Primary Care

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With approximately 234,000 nurse practitioners licensed in the United States, they are increasingly playing a key role in both rural and nonrural primary care practices, according to study results published in Health Affairs. Increases were especially significant in rural practices, with the number of practices with nurse practitioners increasing from 35% in 2008 to 45.5% in 2016.

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The knowledge work of nurses is data intensive. Today, healthcare organizations use multiple, complex information systems, technology, and networks to manage clinical, financial, and claims data that must be integrated in a meaningful way. This sets the stage for the big data challenge. The ability to integrate disparate data and analyze these data to better understand outcomes is no small task. Having access to the right information at the right time to support clinical decisions is essential in planning and providing the right care for patients. All clinicians need real-time information to make timely, critical, clinical decisions.

Understanding big data is a priority for nurses, as the profession aims to provide the best possible care to patients. Capturing healthcare data in a structured way helps build the foundation for accurate, reliable information regarding a patient across multiple systems and settings of care. Using data elements consistently and reliably also allows for information to be collected once and reused for multiple purposes, including outcomes measurement, practice level improvements, surveillance, population health, research, and decision support. 1 The use of big data technologies can help nurses and other healthcare providers improve care quality, optimize outcomes, and reduce healthcare costs. 2

Defining big data

The McKinsey Global Institute defines "big data" as "datasets whose size are beyond the ability of typical database software tools to capture, store, manage, and analyze." 3 While the data that comprise the elements of health information continue to expand in volume and complexity, the crux of big data is within the ability to analyze and use it in a meaningful way for continual improvements. 2

The movement from paper-based systems to electronic health record (EHR) systems is enabling nurses to consider the potential power of big data. According to the Institute of Medicine, an EHR system has several key capabilities: 4

* It is a longitudinal collection of electronic health information for and about persons.

* It provides immediate electronic access to person and population-level information by authorized users.

* It provides knowledge and decision support to enhance the quality, safety, and efficiency of patient care.

* It supports efficient processes for healthcare delivery.

As knowledge workers, nurses must leverage clinical data from the EHR in order to:

* optimize workflow and support clinical decision making

* tell the patient's story

* collaborate to foster knowledge translation

* leverage analytics to extract actionable knowledge

* use sharable, comparable data

* build evidence out of nursing practice.

However, in spite of our best collective efforts, and after decades of implementing EHR systems, nurses still cannot consistently use big data for research or for reporting quality and patient safety outcomes. To advance the vision of a transformed healthcare system, nurses need a more coordinated structure where information can be easily and safely shared among patients, consumers, clinicians, and providers to enable improved outcomes, quality of care, and lower costs. This vision requires access to real-time, accurate, and actionable health information.

Preparing for the future of big data

EHRs are not the only source of big data today. Increasingly, medical data are being generated by patients and processed by computers. Wearable wireless sensors allow smartphone use to generate medical data, including measuring blood-oxygen and glucose levels, BP, and cardiac rhythm. Sophisticated medical imaging devices are being miniaturized to replace the stethoscope. 5 These smart devices are a part of the Internet of Things (IoT) in healthcare, and most of them can communicate with a smartphone app. 6

Monitoring data from a patient's wearable technology or maintaining an accurate, up-to-date inventory of the right supplies and equipment will allow healthcare organizations to stay ahead of the curve. Implementing innovative solutions that capture and analyze data in real time can improve healthcare quality by finding common patterns and anticipating outcomes. With IoT technologies rapidly maturing, the healthcare industry stands to benefit from this enhanced intelligence to improve performance through innovation.

During his 2015 State of the Union address, President Barack Obama launched the Precision Medicine Initiative. This effort is intended to accelerate our understanding of individual uniqueness and the subsequent effect on disease onset, progression, prevention, and treatment. 7 While cloud-based computing makes it possible to rapidly analyze large amounts of data, precision medicine will require universally accessible EHRs and scalable technology to process the data as useful information.

Patient-generated data are also flowing into traditional EHRs. When this abundance of data is properly assembled, integrated, and analyzed, it will offer huge new potential at two levels: the individual and the population as a whole. Once the relevant data are tracked and analyzed, nurses will be able to identify trends and interactions that no single EHR system or program could detect alone. But to do so, the privacy and security of the data need to be protected, and analytics need to be in place to extract meaningful information.

This iterative flow of personal data will also require changes in the mindsets of healthcare stakeholders. For example, patients, consumers, and clinicians must be willing and able to trust and learn from person-generated data; this is a personal evolution as much as an analytical one. The potential value that can be gained from these innovative technologies is great, but the necessary behavior change to depart from traditional practices will be challenging for both individuals and professionals. 3

How can nurses leverage big data?

The ability of nurses to make optimal clinical decisions is dependent upon having access to accurate, real-time information. Executive dashboards can be used as tools that enable a leadership team to visualize metrics to guide decision making based on actionable information. 8 Data must also be structured in standard ways to enable sharable, comparable information. The value of consistent and accurate data can be realized through interoperable systems, advances in EHRs, and alignment on standards and terminologies. Nurses were among the first professional groups to standardize this terminology, but the pure number of terminology options makes such standardization difficult.

The Healthcare Information and Management Systems Society (HIMSS) chief nursing officer-chief nursing informatics officer Vendor Roundtable Big Data Principles Workgroup has identified the following recommendations to guide the ability to capture and use big data in nursing. 2

Promoting standards and interoperability

* Nurses should promote the use of standardized and accepted terminologies that address the documentation needs of the entire care team regardless of care setting. All care delivery settings should create a plan for implementing an American Nurses Association (ANA)-recognized nursing terminology that is mapped to national standards (for example, Systematized Nomenclature of Medicine-Clinical Terms [SNOMED-CT] and Logical Observation Identifiers Names and Codes [LOINC]).

* Nurses should recommend consistent use of research-based assessment scales and instruments that are standardized through an international consensus body. The lack of standardization makes data comparison challenging and adds to the burden of cost for copyright permissions and/or licensing of such instruments.

* ANA-recognized nursing terminologies should be consistently updated and made available to international standards organizations for translation and complete, comprehensive mapping.

* Consistent use of discrete data elements in support of research, analytics, and knowledge generation is necessary, thereby minimizing free text use and "within defined limits" documentation.

Advancing quality eMeasures

Measuring quality data, including clinical quality measures and nursing-sensitive performance indicators, is a complex process. The data needed to populate these measures come from multiple sources, some of which are not available in the EHR today. Therefore, data alignment, how data are collected, and the use of terminologies are critical to sharing data across settings and organizations.

Clinical quality electronic measures (eMeasures) are integral to these efforts and, as they evolve, will become essential for enabling analytics and big data initiatives to generate new evidence and knowledge.

The following are big data principles for quality eMeasures:

* Efforts to develop and design quality eMeasures must ensure the data to be collected and measured are aligned with the clinician's workflow, not as additional documentation.

* Paper measure sets must be evaluated for appropriateness to advance nursing-sensitive quality eMeasures; expectations should be set for which data are to be collected, how the data are collected, and the required terminologies to be used.

* Initiatives and programs that define and promote new quality eMeasures and their requirements should allow time for testing and piloting with defined timeframes that consider all stakeholders.

* Clinical quality eMeasures must support evidence-based, cost-effective care that follows clinical practice guidelines and minimizes the negative impact on clinician workflow.

Leveraging nursing informatics experts

Nursing informatics is a specialty that integrates nursing science with information management and analytical sciences to identify, define, manage, and communicate data, information, knowledge, and wisdom in nursing practice. 9 Informatics nurses support the entire healthcare team by applying their knowledge of information structures, information processes, information technology (IT), and analytics.

Applying nursing informatics knowledge is essential to capturing health and care data in a structured way to achieve accurate, reliable, clinically meaningful measurement across systems and settings of care. Big data principles for nursing informatics include the following:

* Healthcare organizations should use nurse informaticists who will provide valuable insight into concept representation, design, implementation, and optimization of health IT to support evidence-based practice, research, and education.

* Nurse informaticists should have formal informatics training education and certification to achieve the desired outcomes.

Influencing patient outcomes

Large amounts of data are collected today through genomic research, registries, EHRs, and devices; additionally, data are captured in a wide variety of locations, including the home. Now we can analyze these data to identify patterns, trends, and gather evidence that will in turn influence patient outcomes. But to be of value, these data must also reflect nursing, medical, and other healthcare professional information and knowledge. The use of big data offers a tremendous opportunity to accelerate the growth and synthesis of new knowledge to make a positive impact on nurses and the individuals/populations nurses serve.

REFERENCES

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Understanding the impact of big data on nursing knowledge

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The knowledge work of nurses is data intensive. Today, healthcare organizations use multiple, complex information systems, technology, and networks to manage clinical, financial, and claims data that must be integrated in a meaningful way. This sets the stage for the big data challenge.

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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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Social media has become widely used by individuals and businesses to stay connected, communicate and even market products or services. As these sites evolve and become a prevalent way of reaching out to consumers, healthcare professionals are finding new, effective ways to utilize social media.

Social Media and Healthcare

Many healthcare managers are working to effectively utilize social media to engage patients and consumers. Through effective marketing and communication tactics, organizations are able to move away from traditional advertising techniques, and use the internet to connect with consumers in the healthcare field. Consumers heavily rely on information found online and use the internet to gather healthcare information and connect with other patients to garner support and learn about similar conditions. Others utilize these resources for research or to share experiences with healthcare providers and other related organizations. Patients also have a tendency to seek information via social media that assists in the selection of doctors, specialists and hospitals to make informed decisions on the best practices to seek care. Individuals will use social media to post reviews or other comments that support or possibly deter others from choosing that type of healthcare in the future. It is essential for providers to be active on social media and provide accurate information, connect with readers and implement marketing techniques where applicable.

Avoiding HIPAA Violations

The Healthcare Insurance Portability and Accountability Act (HIPAA) was enacted by Congress in 1996 with the intent of providing patients more control over their healthcare records. HIPPA encompasses a variety of key points including:

Reducing healthcare fraud

Implementing industry-wide standards for information provided on electronic billing

Providing health insurance to individuals that are changing or have lost their jobs

In terms of protecting healthcare information, HIPPA sets guidelines that pertain to the protection and confidential handling of an individual's health records. These guidelines have become somewhat of an issue in terms of social media. Healthcare professionals cannot directly address patients through these outlets as it violates the privacy and confidentiality regulations outlined by HIPPA. Other healthcare facilities are encouraged to implement strict policies and guidelines for what employees are allowed to post on social networking websites. Some ways to avoid HIPPA violations include:

Distribute clear social networking policies to employees

Avoid any discussion of patients, even in general terms

Speak generally about conditions and treatments

Prominently post your policies and procedures on all social media platforms

Do not practice medicine online by responding to patients offline

Utilizing Social Media

There is a variety of ways that healthcare managers are utilizing social media to enhance their services and provide patients with accurate medical information. Here are the top ways professionals in the field are using social media:

#1: Share Information

Social media is intended to provide individuals the ability to access information quickly and communicate with others. Healthcare organizations utilize these tools and websites to share information with consumers in a variety of ways such as sharing general information about flu shots and tips to avoid a cold. Sharing news regarding outbreaks or health hazards is an effective way for healthcare facilities to provide accurate information to patients. It is important to note that all patient specific information requires permission along with a signed release. Other forms of sharing information through social media include:

Provide updates on new technologies

Introduce new doctors in a practice on social networks

Answer questions on various topics (e.g. how to reach doctors or hours of operation)

Deliver generic pre- and post- operative care information

Offer patients any updates that relate to the practice itself

#2: Compare and Improve Quality

Another effective way that healthcare managers utilize social media is by spending time evaluating their competitors to get an insight into the services they offer and overall patient satisfaction. By taking a look into different practices and their social media involvement, professionals have the ability to mimic these methods to enhance their own. Some organizations will do better through social media; providers can determine whether or not they need to take more appropriate action to quickly respond to patient requests and improve customer service.

To gather feedback and improve quality, social media interaction can provide doctors and physicians with immediate responses from individuals to help understand common reactions to medications, as well as overall consensus from patients on new techniques in the industry. Using this information that is readily available on social media allows for healthcare organizations to learn from patient reactions and adjust accordingly. By following feedback on these sites, healthcare professionals also have the opportunity to evaluate the possibility of additional services in the industry.

#3: Train Medical Personnel

Some healthcare organizations have begun to utilize social media channels as part of their training process. During presentations, trainees are encouraged to use certain hash tags on Twitter or join other groups to engage one another to make training processes more enjoyable and interactive. These training techniques provide trainees a central location to ask questions and quickly receive answers. Social media gives participants the power to provide presenters with immediate feedback on training sessions.

Trainees are not the only people who benefit from this social media technique. Organizations can use training videos and pictures from training sessions to engage audiences and enhance their social media channels by marketing their facilities and exemplifying their innovating training processes.

#4: Live Updates during Procedures

Although somewhat controversial, there has been an increase of doctors and surgeons providing updates from the operating room. Through Twitter and other social media outlets, healthcare professionals have the ability to deliver up-to-date information during procedures to fellow doctors, medical students or simply curious individuals. Some say these updates are a distraction in the operating room, while others argue that it is an innovation and provides educational value that should be embraced.

The use of social media during operations also provides healthcare facilities the ability to gain attention from industry specific outlets as well as mainstream media. As a marketing approach, organizations create a buzz on social media with these updates, creating excitement and enhancing public awareness of an individual organization to attract patients and medical personnel.

#5: Communicate in Times of Crisis

In times of crisis, the use of social media has increased to provide minute-by-minute information to consumers. Through social media, hospitals and other organizations are able to deliver real-time updates on hospital capacity, operation status and emergency room access. Having an active social media presence allows healthcare professionals to pass along information shared by organizations such as the Red Cross, and the Centers for Disease Control or communicate with news outlets.
As social media continues to become a valuable asset to healthcare organizations and new methods of use are implemented, the industry requires administrators to set guidelines and procedures for effectively managing these channels. To provide the best customer service and accurate information while adhering to HIPAA regulations, organizations need individuals versed in the healthcare administration. For those interested in pursuing an online Master of Health Administration, The University of Scranton provides the most effective tools and resources to advance a healthcare career.

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Top 5 Ways Social Media is Used by Healthcare Professionals

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Social media has become widely used by individuals and businesses to stay connected, communicate and even market products or services. As these sites evolve and become a prevalent way of reaching out to consumers, healthcare professionals are finding new, effective ways to utilize social media.

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Nicole Granados, BSN, RN - Registered Nurse - Utah Valley Hospital | LinkedIn

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View Nicole Granados, BSN, RN'S profile on LinkedIn, the world's largest professional community. Nicole has 2 jobs listed on their profile. See the complete profile on LinkedIn and discover Nicole's connections and jobs at similar companies.

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The Healthy People 2020 Social Determinants of Health topic area is organized into 5 place-based domains:

1. Economic Stability

2. Education

3. Health and Health Care

4. Neighborhood and Built Environment

5. Social and Community Context

Health Literacy is a key issue in the Health and Health Care domain.

The U.S. Department of Health and Human Services (HHS) defines health literacy as "the degree to which individuals have the capacity to obtain, process, and understand basic health information needed to make appropriate health decisions."Adequate health literacy may include being able to read and comprehend essential health-related materials (e.g., prescription bottles, appointment slips, etc.). Adequate health literacy may increase a person's capacity to take responsibility for their health and their family's health. However, health literacy is not just the result of individual capacities but also the health-literacy related demands and complexities of the health care system. For example, individuals with low literacy may not be able to understand prescription labels-but an organization that values health literacy makes it a priority to implement systems and interventions such as visual aids and counseling that increase understanding and thereby advance patient safety.

Low overall literacy may impact health literacy; however, the relationship between them is complex. For example, an individual may have high overall literacy and still have low health literacy. Low or limited health literacy skills are more prevalent among certain population groups and may be linked to many poor health outcomes. Health literacy has the potential to impact a broad array of functional skills that are required to make health decisions in various settings. Although there are many ways in which health literacy may impact health and health outcomes, this summary focuses on health literacy related to reading and comprehension of essential health-related materials.

A number of factors impact health literacy including a patient's receipt of appropriate written health communication materials, ability to accurately interpret written health-related information, and communication with providers. When patients receive written health communication materials that don't match their reading level, patient education is not effective. Additionally, when patients have low overall literacy skills but high verbal fluency, their verbal fluency can mask their inability to interpret written information. Potential communication barriers between patients and health care providers created by low health literacy may lead to a variety of negative health outcomes for the patient. For example, such communication barriers have been associated with patients being more likely to be hospitalized.

The impact of health literacy on skills needed to make health-related decisions may affect a patient's adherence to a treatment regimen (e.g., medication), which may decrease its benefits. Patients with low health literacy also tend to use the emergency department more often and are more likely to return to the emergency department after 2 weeks. One systematic review found low literacy (used as a proxy for health literacy) may impact parent/caregiver behavior (e.g., medication dosing, duration of breastfeeding). The study also found some evidence of an impact of parents' low literacy on children's health outcomes (e.g., depressive symptoms, persistent asthma).

A number of factors may influence an individual's health literacy, including living in poverty, education, race/ethnicity, age, and disability. Adults living below the poverty level have lower health literacy than adults living above the poverty level. Certain characteristics influenced by poverty, including insurance status, may impact health literacy more than other factors. For example, uninsured and publicly insured (e.g., Medicaid) individuals are at higher risk of having low health literacy. Studies have found that older adult Medicare beneficiaries with low health literacy have higher medical costs, increased ER visits and hospital admissions, and decreased access to health care. Education may also impact health literacy. For example, in a nationally representative sample, almost half of adults who did not graduate from high school had low health literacy.

Some of the greatest disparities in health literacy occur among racial and ethnic minority groups from different cultural backgrounds and those who do not speak English as a first language. Results from the National Assessment of Adult Literacy demonstrated that Hispanic adults have the lowest average health literacy scores of all racial/ethnic groups, followed by black and then American Indian/Alaska Native adults. People with low health literacy and limited English proficiency are twice as likely as individuals without these barriers to report poor health status. One study found that 74% of Spanish-speaking patients have less-than-adequate health literacy as compared to 7% of English-speaking patients. Cultural beliefs may also impact communication between patients and providers and affect a patient's ability to follow a physician's instructions.

Health literacy challenges may impact older adults more than other age groups. On average, adults age 65 and older have lower health literacy than adults under the age of 65. Low health literacy among older adults is associated with increased reports of poor physical functioning, pain, limitations of daily activities, poor mental health status

Improvements in health practice that address low health literacy are needed to reduce disparities in health status. As limited health literacy is common and may be difficult to recognize, "experts recommend that practices assume all patients and caregivers may have difficulty comprehending health information and should communicate in ways that anyone can understand." Examples include: simplifying communication; confirming comprehension for all patients to minimize risk of miscommunication; making the health care system easier to navigate; and supporting patient's efforts to improve their health.

Additional research is needed to increase the evidence base for what works to improve the effects of health literacy on health outcomes and disparities. This additional evidence will facilitate public health efforts to address health literacy as a social determinant of health.

Disclaimer: This summary of the literature on health literacy as a social determinant of health is a narrowly defined review that may not address all dimensions of the issue. i, ii Please keep in mind that the summary is likely to evolve as new evidence emerges or as additional research is conducted.

Endnotes

i Terminology used in the summary is consistent with the respective references. As a result, there may be variability in the use of terms, for example, black versus African American.

ii The term minority, when used in a summary, refers to racial/ethnic minority, unless otherwise specified.

References

1 U.S. Department of Health and Human Services, Office of Disease Prevention and Health Promotion. National action plan to improve health literacy. Washington (DC): Author; 2010.

2 Ratzan SC, Parker RM. Introduction. In: Selden CR, Zorn M, Ratzan SC, Parker RM, editors. National Library of Medicine current bibliographies in medicine: health literacy. Bethesda (MD): National Institutes of Health, U.S. Department of Health and Human Services; 2000. NLM Pub. No: CBM 2000-1.

3 Baker DW. The meaning and the measure of health literacy. J Gen Intern Med. 2006 21(8), 878-83.

4 Health literacy: report of the Council on Scientific Affairs. Ad Hoc Committee on Health Literacy for the Council on Scientific Affairs, American Medical Association. JAMA. 1999;281(6):552-57.

5 Sørensen K, Van den Broucke S, Fullam J, Doyle G, Pelikan J, Slonska Z, Brand H. Health literacy and public health: a systematic review and integration of definitions and models. BMC Public Health, 2012;12(1):1.

6 McQueen DV, Kickbusch I, Potvin L, Pelikan JM, Balbo L, Abel T. Health and modernity: the role of theory in health promotion. New York: Springer Science & Business Media; 2007.

7 Brach C. Keller D, Hernandez LM, Baur C, Parker R, Dreyer B et al. Ten attributes of health literate health care organizations. Washington (DC): National Academy of Sciences; June 2012. http://nam.edu/wp-content/uploads/2015/06/BPH_Ten_HLit_Attributes.pdf

8 Rudd R. Objective 11-2: Improvement of health literacy. In: Communicating health: priorities and strategies for progress. Washington (DC): U.S. Department of Health and Human Services; 2003.

9 Easton P, Entwistle VA, Williams B. Health in the 'hidden population' of people with low literacy. A systematic review of the literature. BMC Public Health. 2010;10:459. doi: 10.1186/1471-2458-10-459.

10 Rudd RE. Health literacy skills of U.S. adults. Am J Health Behav. 2007;31(Suppl 1):S8-18.

11 Ngoh LN. Health literacy: A barrier to pharmacist-patient communication and medication adherence. J Am Pharm Assoc. 2009;49(5):E132-49. doi: 10.1331/JAPhA.2009.07075.

12 Buck ML. Providing patients with written medication information. Ann Pharmacother. 1998;32(9): 962-69.

13 Nelson AR, Stith AY, Smedley, BD, editors. Unequal treatment: confronting racial and ethnic disparities in health care. Washington (DC): National Academies Press; 2002.

14 Baker DW, Gazmararian JA, Williams MV, Scott T, Parker RM, Green D, Peel J. Functional health literacy and the risk of hospital admission among Medicare managed care enrollees. American J Public Health. 2002;92(8):1278-83.

15 Baker DW, Parker RM, Williams MV, Clark WS. Health literacy and the risk of hospital admission. Journal General Intern Med. 1998:13(12):791-98.

16 Flores G. Language barriers to health care in the United States. N Engl J Med. 2006;355 :229-31

17 Pignone MP, DeWalt DA. Literacy and health outcomes: is adherence the missing link? J Gen Intern Med. 2006;21:896-7

18 Poss J. Developing a new model for cross-cultural research: synthesizing the Health Belief Model and the Theory of Reasoned Action. ANS Adv Nurs Sci. 2001;23:1-15

19 Becker M, Maiman L. Sociobehavioral determinants of compliance with health and medical care recommendations. Med Care. 1975;13(1):10-24.

20 Wroth TH, Pathman DE. Primary medication adherence in a rural population: the role of the patient-physician relationship and satisfaction with care. J Am Board Fam Med. 2006;19:478-86.

21 Griffey RT, Kennedy SK, D'Agostino McGowan L, Goodman M, Kaphingst KA. Is low health literacy associated with increased emergency department utilization and recidivism? Acad Emerg Med. 2014;21(10):1109-15. doi: 10.1111/acem.12476.

22 Yin HS, Dreyer BP, Foltin G, van Schaick L, Mendelsohn AL. Association of low care-giver health literacy with reported use of nonstandardized dosing instruments and lack of knowledge of weight-based dosing. Ambul Pediatr. 2007;7(4):292-298.

23 Kaufman H. Skipper B, Small L, Terry T, McGrew M. Effect of literacy on breast-feeding outcomes. South Med J. 2001;94(3):293-296.

24 Sleath BL, Jackson E, Thomas KC, et al. Literacy and perceived barriers to medication taking among homeless mothers and their children. Am J Health Syst Pharm. 2006; 63(4): 346-351

25 DeWalt DA, Hink A. Health literacy and child health outcomes: a systematic review of the literature. Pediatrics. 2009;124 Suppl 3:S265-74. doi: 10.1542/peds.2009-1162B

26 DeWalt DA, Dilling MH, Rosenthal MS, Pignone MP. Low parental literacy is associated with worse asthma care measures in children. Ambul Pediatr. 2007;7(1):25-31.

27 Kutner M, Greenburg E, Jin Y, Paulsen C. The health literacy of America's adults: results from the 2003 national assessment of adult literacy. National Center for Education Statistics; 2006. Report No.: NCES 2006-483.

28 Howard DH, Gazmararian J, Parker RM. The impact of low health literacy on the medical costs of Medicare managed care enrollees. Am J Med. 2005;118(4):371-77.

29 Cho YI, Lee SY, Arozullah AM, Crittenden KS. Effects of health literacy on health status and health service utilization amongst the elderly. Soc Sci Med. 2008;66(8):1809-16.

30 Sudore RL, Mehta KM, Simonsick EM, Harris TB, Newman AB, Satterfield S, et al. Limited literacy in older people and disparities in health and healthcare access. J Am Geriatr Soc. 2006;54(5):770-76.

31 Sentell T, Braun KL. Low health literacy, limited English proficiency, and health status in Asians, Latinos, and other racial/ethnic groups in California. J Health Commun. 2012;17 Suppl 3:82-99. doi: 10.1080/10810730.2012.712621.

32 Brice JH, Travers D, Cowden CS, Young MD, Sanhueza A, Dunston Y. Health literacy among Spanish-speaking patients in the emergency department. J Natl Med Assoc. 2008;100(11):1326-32.

33 Shaw SJ, Huebner C, Armin J, Orzech K, Vivian J. The role of culture in health literacy and chronic disease screening and management. J Immigr Minor Health. 2009;11(6):460-67. doi: 10.1007/s10903-008-9135-5.

34 Wolf MS, Feinglass J, Thompson J, Baker DW. In search of 'low health literacy': threshold vs. gradient effect of literacy on health status and mortality. Social Sci Med. 2010;70(9): 1335-41. doi: 10.1016/j.socscimed.2009.12.013.

35 Wolf MS, Gazmararian JA, Baker DW. Health literacy and functional health status among older adults. Arch Intern Med. 2005;165(17):1946-52. doi: 10.1001/archinte.165.17.1946.

36 Baker, D. W., Wolf, M. S., Feinglass, J., Thompson, J. A., Gazmararian, J. A., & Huang, J. (2007). Health literacy and mortality among elderly persons. Arch Intern Med, 167(14), 1503-1509. doi: 10.1001/archinte.167.14.1503

37 Brega AG, Barnard J, Mabachi NM, Weiss BD, DeWalt DA, Brach C, Cifuentes M, Albright K, West, DR. AHRQ Health Literacy Universal Precautions Toolkit, Second Edition. (Prepared by Colorado Health Outcomes Program, University of Colorado Anschutz Medical Campus under Contract No. HHSA290200710008, TO#10.) AHRQ Publication No. 15-0023-EF. Rockville, MD. Agency for Healthcare Research and Quality. January 2015. https://www.ahrq.gov/sites/default/files/wysiwyg/professionals/quality-patient-safety/quality-resources/tools/literacy-toolkit/healthlittoolkit2.pdf

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The Healthy People 2020 Social Determinants of Health topic area is organized into 5 place-based domains: Economic Stability Education Health and Health Care Neighborhood and Built Environment Social and Community Context Health Literacy is a key issue in the Health and Health Care domain. The U.S.

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Centers for Disease Control and Prevention

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What is health literacy? Take action. Find out.

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2020 health literacy definitions, article, podcast, video & resources

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Nurses are the center of patient care, which means nurses need to also be in the center of technology in healthcare.

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University of Minnesota School of Nursing

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My friend and coworker has fought a HARD fight against COVID and after a double lung transplant walked out of the hospital after 106 days. Not informatics related but it is truly inspiring!

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To keep up with the latest local news subscribe to our TV20 newsletter HERE and receive news straight to your email every morning.
GAINESVILLE, Fla. (WCJB) - It doesn't matter if you're old, young, male, or female-- the COVID-19 virus has claimed the lives of people from all walks of life, and that was almost the case for one front-line nurse.

Jill Holker was wheeled into UF Health Shands hospital, dying from COVID-19 but after major surgery, and months of rehab and recovery, Friday she finally was sent home.

This is Jill Holker. Today she is walking out of @UFHealth Shands with new lungs. The COVID patient underwent double lung transplant surgery and after months of rehab and recovery, she's finally head home. @WCJB20 #COVID19 pic.twitter.com/adHoLTsUm8
- Julia Laude (@JuliaMaeLaude) February 19, 2021

The hallways of the UF Health Heart and Vascular hospital were lined with nurses, surgeons, and other staff members waiting to congratulate Holker on her release.

The lobby was quiet, as they excitedly waiting for Holker to take her final steps out the door, and when she finally appeared they couldn't hold back their cheers.

The Utah nurse was diagnosed with COVD-19 back in Nov., and within a week she had to be admitted to the ICU.

It got so bad that she had to be transferred across the country to UF Health Shands, where she would ultimately receive a double lung transplant.

She is one of only 12 COVID patients to receive the surgery at UF Health Shands, and spent 45 days fighting the virus before she was assigned a pair of donor lungs.

Related story: UF Health one of few hospitals to complete COVID-19 related double lung transplants

"Without the transplant, she would have not been able to live. She was that sick," Medical Director of the UF Lung Transplant Program, Andres Pelaez said.

Friday afternoon, Holker received a send-off that brought her to tears.

Charge Nurse on Unit 87, Jessica Casey, looked after Holker during her time at the hospital and helped organize her farewell gathering.

"She is so loved, so loved. She's so sweet She goes above and beyond, even in her worst times, she was so kind and caring and she was going through so much, she's just grace, all in one," Casey said.

For this active marathoner, she said she's received a second chance at life.

"I had no underlying health conditions at all. I ran, I worked out, I worked full time, I didn't take any medications, and it hit me to the point where they called my daughter to tell her to think about withdrawing care," Holker said.
Copyright 2021 WCJB. All rights reserved. Click here to subscribe to our newsletter.
And the first thing she'll do is, "Take a deep breath. It's a whole new life," she said.

She said she'll also be making time for a meal at her favorite Mexican restaurant when she gets back home to Utah.

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Front line nurse saved from COVID-19, receives double lung transplant from UF Health Shands

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To keep up with the latest local news subscribe to our TV20 newsletter HERE and receive news straight to your email every morning. GAINESVILLE, Fla. (WCJB) - It doesn't matter if you're old, young, male, or female-- the COVID-19 virus has claimed the lives of people from all walks of life, and that was almost the case for one front-line nurse.

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Quality improvement is an ongoing, measurable process by which healthcare providers achieve higher quality care. Important steps in quality improvement are:

Provider adherence to evidence-based guidelines and pursuit of clinical quality measures when giving treatment

Efficient communication among providers about shared patients, so that all are on the same page about a patient's diagnoses, medications, tests, treatments, etc. and can give the right care at the right time

Documentation and evaluation of the changes providers make during the quality improvement process

Technology makes quality improvement possible. Electronic health records (EHRs), Direct secure email, and health information exchanges (HIEs) can make it easier to document treatment, communicate between facilities, and follow up with patients. Utah's Clinical Health Information Exchange (HIE), called the cHIE, includes many crucial functions for quality improvement. The cHIE can:

Simplify care coordination by enabling providers to securely and efficiently exchange information about shared patients, so that each provider has the information needed to give the best care.

Improve documentation by allowing providers to incorporate community-sourced patient information into their own EHR records, forming a more complete picture of a patient's health.

Help providers reduce hospital and emergency department admissions. The cHIE includes cHIE Alerts, which notify providers in real-time when their patients are admitted to a hospital or emergency department. The provider can then offer appropriate follow-up care to decrease the risk of readmission and the associated negative health outcomes and higher costs.

Allow providers to better supervise patients who have chronic conditions. The cHIE includes analytics functionality that lets providers monitor specific indicators for cohorts of patients with a particular condition and identify when interventions are needed.

Support shared savings payment/pay-for-performance arrangements, which reward higher quality care by paying providers a percentage of the savings they achieve for a payer.

For more information about how the cHIE supports quality improvement, contact us at chie@uhin.org.

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Quality improvement is an ongoing, measurable process by which healthcare providers achieve higher quality care. Important steps in quality improvement are: Provider adherence to evidence-based guidelines and pursuit of clinical quality measures when giving treatment Efficient communication among providers about shared patients, so that all are on the same page about a patient's diagnoses, medications, tests, treatments, etc.

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Building blocks that result in well rounded wisdom regarding a certain topic.

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Six years of experience and proper education has made a huge difference in the type of nursing care I now provide.