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An Oregon provider has medical, legal, and ethical obligations to his or her patients. In light of these obligations, it is the philosophy of the Oregon Medical Board that:

1. Regardless of whether an act or failure to act is determined entirely by a provider, or is the result of a contractual or other relationship with a health care entity, the relationship between a provider and a patient must be based on trust, and must be considered inviolable. Included among the elements of such a relationship of trust are:

Open and honest communication between the provider and patient, including disclosure of all information necessary for the patient to be an informed participant in their care.

Commitment of the provider to fundamentally serve as an advocate for the patient's interest, without regard to secondary interests, whether personal, financial, or institutional and in avoidance of any conflict of interest with that of the patient.

Provision by the provider of that care which is necessary and appropriate for the condition of the patient, following community standards and best practices.

Avoidance of the creation or encouragement of any inappropriate relationships outside that of the therapeutic relationship, whether personal, financial, political, sexual, or other.¹

Respect for, and careful guardianship of, any intimate details of the patient's life, which may be shared with the provider.

Dedication by the provider to continually maintain professional knowledge and skills.

Respect for the autonomy of the patient.

Respect for the privacy and dignity of the patient.

Compassion for the patient and their family.

2. Any act or failure to act by a provider that violates the trust upon which the relationship is based jeopardizes the relationship and may place the provider at risk of being found in violation of the Medical Practice Act (ORS Chapter 677).

3. The philosophies expressed herein apply to all licensees regulated by the Oregon Medical Board, as well as those who make decisions, which affect Oregon consumers, including health plan medical directors and other providers employed by or contracting with such plans.

The Oregon Medical Board holds licensees to recognized standards of ethics of the medical profession, specifically for this philosophy: American Medical Association's Code of Medical Ethics: Opinion 1.1.1 Patient-Physician Relationships; American Association of Physician Assistants' Guidelines for Ethical Conduct for the PA Profession: The PA and Patient; and Oregon Association of Acupuncturists' Code of Ethics: 1.5 Personal Relationships with Patients.

- ORS 677.190(1)(a) and ORS 677.188(4)(a)

- Adopted 1998

- Amended July 10, 2020

1. Additional information is available in the Board's Statement of Philosophy on Sexual Misconduct.

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An Oregon provider has medical, legal, and ethical obligations to his or her patients. In light of these obligations, it is the philosophy of the Oregon Medical Board that: 1.

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Five Legal Issues in Nursing Everyone Needs to Know

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Legal issues in nursing may be the last thing on your mind. Whether a new grad, or a seasoned vet, one needs to be alert of these encounters.

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Healthcare administrators play a significant role in establishing the programs and environments that directly affect patient care delivery within their health systems. Dilemmas often arise that require an administrator to make difficult decisions, many of which include an ethical component. Being prepared for these situations is why ethical leadership and decision-making are key components of healthcare administration degree programs. Both skills are necessary for future leaders who are seeking to navigate evolving U.S. healthcare challenges.

Advanced degree programs, such as a Master of Health Administration, can help mid-career professionals develop a fundamental, durable ethical compass, along with the ability to analyze situations for their ethical components.

Ethical Issues in Healthcare Management

Healthcare managers focus on optimizing care to improve patient outcomes while ensuring a facility performs efficiently as a business. In doing so, they must also be sure that their policies adhere to the four principles of biomedical ethics. These were first outlined by Thomas Beauchamp and James Childress in 1979. They are respected for autonomy (acknowledging patients' rights to make their own decisions), beneficence (intending to do good), nonmaleficence (doing no harm), and justice (providing equitable care).

For healthcare professionals in administrative roles, being aware of potential ethical dilemmas is a key part of what a health administrator does. They must incorporate ethics in healthcare into their facilities' policies and procedures and mitigate the chances of ethical concerns negatively impacting patients.

Examples of Ethical Dilemmas in Healthcare

While the many ethical issues in healthcare involve patient care, factors outside the patient/provider dynamic can also be involved. Economic decisions, legal concerns, and the evolution of technology can place health professionals in ethical dilemmas.

1. Balancing Fiscal Responsibility and Ethics

Health administrators are responsible for making financial decisions that can impact their healthcare organizations and the quality of care delivered. It can be very challenging to balance competing demands, and it sometimes pits budget, quality, and ethics against each other. For example, administrators often must choose between hiring more staff to decrease nurse-to-patient ratios and buying or repairing equipment.

2. Mitigating Legal Risks Ethically

Malpractice and medical lawsuits can be devastating to any medical practice. Health administrators may be asked to work with staff to resolve complaints before these issues escalate to the point of legal action and to help ensure their organization's providers are basing their decisions on patients' needs over other factors. Appropriate actions may include disciplining or terminating a member of a healthcare team.

Fear of legal action may also cause providers to engage in "defensive medicine," or practice strategies specifically designed to avoid potential malpractice suits. While this can conceivably prevent or minimize the potential for litigation, it can come at the expense of providing optimal care that can determine a patient's short- and long-term health. Balancing these legal issues requires careful ethical analysis, a process that medical administrators can help guide.

3. Ethics of Privacy

An important ethical principle involves maintaining the right to privacy and autonomy. Healthcare organizations are increasingly reliant on technology, such as electronic health records (EHRs), for recording, storing, and transmitting sensitive health information. Administrators may play a role in ensuring these systems adhere to all relevant laws and regulations, such as the Health Insurance Portability and Accountability Act (HIPAA).

On the other hand, health administrators must ensure staff members are aware of the circumstances under which confidentiality must be broken, such as to prevent patient self-harm or harm to others. A thorough understanding of these rules and the ability to analyze the ethical principles involved is important for professional leadership.

4. The Ethics of Managing Influential Relationships/h3>

Healthcare administrators may work to manage relationships with many stakeholders, including external vendors who provide products and services including brand-name medications, diagnostic equipment, and software solutions. This attention is necessary to ensure staff members are making decisions in patients' best interests without outside influence. One example to avoid is an instance where a doctor or nurse may prescribe medication - even if it's not medically necessary or there are better alternatives - to obtain a benefit promised from a pharmaceutical sales rep.

Health administrators are industry leaders with the education and authority to shape ethical policy in healthcare. By understanding and integrating principles of ethical leadership into your organization, you can help educate your colleagues and promote the highest quality of patient care.

Guide a More Ethically Sound Future

Health administration is about more than optimizing care delivery. It's about doing so in a way that is mindful of the importance of ethics in healthcare. Ohio University's online Master of Health Administration degree can help prepare you to lead healthcare facilities grounded in ethically sound principles. Our program is designed to provide you with the knowledge and skills to solve problems and implement innovations in an ethical manner.

Learn how Ohio University can help guide you to become a trusted leader in a vital field.
Recommended Reading How to Become a Health Care Administrator Human Resource Management in Health Care Types of Community Engagement Strategies for Health Care Organizations
Sources:
American College of Healthcare Executives, Ethical Decision-Making for Healthcare Executives Etactics, "10 Examples of Patient Confidentiality (Exceptions Included)" HIPAA Journal, HIPAA Compliance and Medical Records MedicineNet, "Medical Definition of Defensive Medicine" National Conference of State Legislatures, Mental Health Professionals' Duty to Warn Oxford University Press, Principles of Biomedical Ethics

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Ethics in Health Care: What You Need to Know | Ohio University

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Healthcare administrators play a significant role in establishing the programs and environments that directly affect patient care delivery within their health systems. Dilemmas often arise that require an administrator to make difficult decisions, many of which include an ethical component.

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Continuing Education Activity

Patient rights are a subset of human rights. Whereas the concept of human rights refers to minimum standards for the ways persons can expect to be treated by others, the concept of ethics refers to customary standards for the ways persons should treat others. As such, rights and ethics are usually flip sides of the same coin, and behind every 'patient right' is one or more ethical principle from which that right is derived. This activity discusses how the interprofessional team can ensure that ethical principles are followed and the patient's rights are assured.

Objectives:

Summarize the common ethical principles that apply to patient care.

Outline the history of ethical principles applied to patient care.

Review the meaning of the sanctity of human life.

Explain how the interprofessional team can work together to achieve ethical principles in caring for patients.

Access free multiple choice questions on this topic.
Introduction

"Rights aren't rights if someone can take them away. They're privileges." - George Carlin, "It's Bad for Ya," March 1, 2008.

Patient rights are a subset of human rights. Whereas the concept of human rights refers to minimum standards for the ways persons can expect to be treated by others, the concept of ethics refers to customary standards for the ways persons should treat others. As such, rights and ethics are usually flip sides of the same coin, and behind every 'patient right' is one or more ethical principles from which that right is derived, which are defined here but reviewed in more detail in a companion article.[1]

Establishing clearly defined patient rights helps standardize care across healthcare fields and enables patients to have uniform expectations during their treatment. According to the American Cancer Society, organizations should develop patient bills of rights "to empower people to take an active role in improving their health, to strengthen the relationships people have with their health care providers, [and] to establish patients' rights in dealing with insurance companies and other specific situations related to health coverage." As with other bills of rights, modern bills of patient rights establish that persons can expect certain treatment regardless of their socioeconomic status, religious affiliation, gender, or ethnicity.

Commonly established rights tend to derive from a core set of ethical principles, including autonomy of the patient, beneficence, nonmaleficence, (distributive) justice, patient-provider fiduciary (trusting) relationship, and inviolability of human life. The establishment of whether one principle is of greater inherent value than another is a philosophical endeavor that varies from authority to authority. In many situations, beliefs may directly conflict with one another. When a legal standard does not exist, it remains the obligation of the health care provider to prioritize these principles to achieve an acceptable outcome for the patient.

This essay analyzes patient rights with respect to their associated ethical principles with accompanying historical perspectives. It also specifies laws or the lack thereof that affect patient rights in the United States.

Common sense dictates that rights established to preserve the well-being of an individual person do not always trump the well-being of a collective society. A recent applicable phenomenon includes measures that authorities take to minimize public disease caused by the SARS-CoV-2 virus (COVID-19), such as through the use of vaccines and respiratory tract masking. Medical therapy essentially always involves a risk vs. benefit determination. The focus of this article is not to establish ethical and legal heuristics or algorithms for prioritizing measures designed to minimize risks to public health that concurrently expose individual persons to theoretical risks.

Function

Brief History of Medical Ethics and Human Rights

Writings that specify ethical obligations (both in general and by physicians) predate by millennia writings that specify human rights (both in general and for patients). The earliest writings containing ethical principles date from the Old Kingdom of the Egyptians, e.g., the autobiography of Nefer-seshem-re, circa 2340 BCE. Ethics began to flourish as a scholarly discipline during the golden age of Greece (the time of Socrates, Plato, and Aristotle) in the 5th century BCE. In these respective cultures, discussions of physicians' ethical duties date to the same times, e.g., an inscription on the tomb of Nenkh-Sekhmet, "chief of the physicians," circa 2400 BCE, and the writings of the Hippocratic school of Cos in the 5th century BCE. Deontological or "duty" ethics (which stems largely from the writings of Immanuel Kant in the late 18th century and is a dominant form of ethics within modern law and healthcare) imposes on persons the obligation to preserve the rights and freedoms of others.

Whereas the Cyrus Cylinder (from the 530s BCE) is an exception, human rights writings are a relatively late development in civilization. The Magna Carta (1215 CE) first established political rights for citizens living in Western society. However, human rights did not enter into Western scholarly or philosophical circles until the 1600s and 1700s by Enlightenment thinkers such as John Locke. Whereas patients had earlier been enabled to establish "rights" through civil suits against their healthcare providers after adverse outcomes (1374 CE)[2], the first policy-making body to establish prospective patient rights was the First Geneva Convention (1864) on behalf of wounded soldiers. The World Health Organization (formed in 1948) created the first bill of health rights intended for application to all persons. Since then, the movement to expand patient rights has become increasingly widespread across all of healthcare.

As the rationale for patient rights depends on the ethical principles that predated them, the moral principles will be discussed first, followed by a discussion of how they result in related patient rights.

Overview of Core Ethical Principles in Modern Western Medicine

Beneficence

Beneficence is doing what is in the best interest of the patient throughout the process of diagnosis and treatment. The physicians of the Hippocratic school of Cos vowed to "help the sick" in a general sense. In recent times, such as with the development of osteopathic medicine, Western physicians have begun to renew the call for a more holistic approach to benevolence, which entails addressing the patient's emotional, social, and spiritual well-being in addition to the care of the body.

Of the other principles, a physician's intent for beneficence conflicts most often with patient autonomy. This conflict has led to the development of documentation in which the patient must demonstrate their understanding of the predictable consequences of his decision to act against medical advice. When disagreements arise between a healthcare provider and a patient, the health care provider must explain the reasons for their recommendations, allowing the patient to make a more informed decision.

Patient Autonomy

Autonomy (literally "self-rule") refers to the capacity to live according to one's own reasons and motives. Concerning the autonomy of ordinary citizens, Western society has undergone a radical change in the last 350 years. Before the Enlightenment, most persons lived under the rule of a monarch or similar type of authority. Early Enlightenment philosophers (e.g., Hobbes, Locke, and Rousseau) advocated what is now called social contract theory. This is the view that persons' moral and political obligations should depend on an agreement amongst themselves regarding which rules will hold in their particular society.

Later philosophers argued for individual autonomy to an even greater degree than championed by social contract theorists. Autonomy as a fundamental aspect of human existence was advanced largely by two moral and social philosophers, John Stuart Mill (early 19th century; of the school of utilitarian liberalism) and Immanuel Kant, respectively. Kant proposed that each person of sound mind should first live in moral autonomy, imposing moral decisions regarding himself or herself on himself or herself and secondly allow all other persons to do the same. Mill advocated autonomy with regard to a person's capacity to govern oneself, and subsequent philosophers and jurists expanded autonomy to include other forms of self-expression. Western post-Enlightenment philosophy and law are no longer concerned with whether autonomy in its various forms is a human right but instead to whom it extends and to what degree.

A patient who can defend his or her judgments has the right to make decisions that do not coincide with what the physician believes is beneficial to that patient. This philosophical concept has become a legal right essentially throughout the Western world. As legal precedents have advanced the requirements for patient autonomy to a greater degree than the requirements for health care provider beneficence, patient autonomy has arguably become the dominant principle affecting patient rights. For example, a patient may refuse treatment that the physician deems to be an act of beneficence. In such cases, the unwritten social contract between patient and physician requires that medical professionals still attempt to inform the patient of the potential consequences of proceeding against medical advice. A patient's autonomy is violated when family members or members of a healthcare team pressure a patient or when they act on the patient's behalf without the patient's permission (in a non-emergency situation).

Nonmaleficence ("Do no harm")

Complementary to beneficence, nonmaleficence seeks to ensure a patient will be no worse off (physically, emotionally, or otherwise) after treatment than before. Nonmaleficence dates to the medical writings of the early Egyptians, as cited earlier. The Hippocratic Oath lists specific acts of nonmaleficence, including euthanasia, abortion, an attempt by the professional at procedures outside of his area of expertise, stealing property, making sexual advances, and violating privacy. The consideration of whether a treatment plan is or is not malevolent extends to all types of treatments, including medications, physical manipulations, and invasive procedures.

In relation to other ethical principles, nonmaleficence most commonly conflicts with beneficence. Despite the potential reason why treatment is beneficial, the therapy may have unintended harm. Examples include procedures in which a surgeon must cut into a patient's body or certain drugs (such as chemotherapies). The body is essentially poisoned so that it may have the opportunity to heal later. In each case, the odds and degree of harm must be weighed against that of benefit. Physicians cannot control all responses to treatments. So a physician can still act under the principle of nonmaleficence if the physician takes measures to minimize the chances of harm and informs the patient prior to taking the risk so that the patient has the opportunity to accept or decline the risk.

Patient-Provider Fiduciary Relationship

Not unique to the medical profession, but essential to its function, is the trust (Latin: fiducia) that the client places in the professional. Primarily, the belief is that the health care professional will act in such a way as to serve the client's best interests. There is inherently unequal power within a patient-physician relationship. As stated by the Illinois Supreme Court,

"[T]he physician-patient relationship has its foundation is on the theory that the former [physician] is learned, skilled and experienced in those subjects about which the latter [the patient] ordinarily knows little or nothing, but which are of the most vital importance and interest to him since upon them may depend the health, or even life, of himself or family. [T]herefore, the patient must necessarily place great reliance, faith, and confidence in the professional word, advice, and acts of the physician."

Patients have different preferences for the degree of passiveness or proactiveness they exert within the fiduciary relationship. Emanuel and Emanuel described four patient-physician relationship models within the history of Western medicine.[3] In each model, the patient and physician relate to each other with a varied degree of passiveness or proactiveness.

In a paternalistic relationship, the doctor decides on behalf of the patient. This is the most passive type of relationship for the patient.

In an interpretive relationship, the doctor first discerns what the patient's goals and values are and then offers options that help achieve those goals and preserve those values. The patient remains passive in this relationship.

In an informative relationship, the doctor provides information to assist with a decision and, without swaying the patient, allows the patient to decide for himself or herself.

In a deliberative relationship, the doctor and patient collaborate essentially as equals. They work together. This model is the most likely to achieve a patient's healthcare goals and preserve his or her values.

Justice

In healthcare, justice refers explicitly to the philosophical concept of "distributive justice." This principle asserts that all persons (patients) will be treated fairly and equitably. It means not only respecting the rights of individuals but also treating all patients in a given situation the same regardless of who they are. Laws and policies within healthcare use the principle of justice to establish access for all persons to obtain healthcare required to preserve life, such as in emergency situations. Some governing bodies, such as the Canadian federal government, have extended justice in healthcare far beyond emergency care. Distributive justice, as defined by John Rawls, can be understood as equal respect for persons - entitlement of persons having a given need to the same rights and services as other persons with the same need and the provision of services of a more significant benefit offered to persons with greater need.

Sanctity and Dignity of Human Life

In addition to prescriptions regarding moral behavior, the belief in a creator god or set of gods is a common feature of the earliest literate societies. Before 500 BCE, the Egyptians, Babylonians, Assyrians, Vedic culture of ancient India, Greeks, and Israelites all wrote regarding theology in conjunction with moral behavior. The ethical codes of all these early civilizations condemned lay citizens from ending the lives of other citizens. For the ancient Egyptians, preparation for one's afterlife was a major facet of one's present life. Ancient Greek philosophers, including Plato and Aristotle, taught extensively about the notion of humans having souls.

The Israelites (founders of a nation of people later to be known as Jews) originated the notion of a divinity that placed value on the lives of all members of the society of believers. The Jews also were original in believing that the divinity itself was the author of the societal moral code, that the divinity created humans in Its image, and that human life itself, therefore, is sacred or holy. The idea that a person is created "in the image of God" is mentioned several times in the book of Genesis alone (Genesis 1:26-27, 5:1-2 9:6). The book of Psalms echoes this concept when the psalmist asserts that God was responsible for his formation, in contrast to the notion that the psalmist may have been formed solely by biological mechanisms (Psalms 139:13). Christianity retained the holy books of Judaism and much of the Jewish philosophy pertaining to the sanctity of the lives of its adherents. The book of Acts, for example, professes that God gives life and breath to all people (Acts 17:25).

Christian culture fused with Greco-Roman culture in the 4th century CE upon the Christianization of the Roman emperor Constantine and his family. This fusion eventually resulted in the dissemination of Judeo-Christian beliefs on the value of human life throughout the West geographically and, later, through the development of all three North American countries. Judaism has become the second-largest religion in North America. Islam, the third-largest religious group in North America, shares much in common with Judeo-Christian belief, not only concerning its traditional stories and notions of God but also concerning notions of the sanctity of human life. Although Mexico, the United States, and Canada's governments were based on the separation of religion and state, the moral teachings of the Abrahamic religions have nevertheless greatly influenced the health care policies of these three countries both in the laws at the national and state level and in the rules set by medical societies and medical administrative organizations. Furthermore, the belief that human life is sacred is not limited to the Abrahamic faiths. As just one example, the Hippocratic physicians (living in a polytheist society) vowed not to take measures to end human life either prior to conception or in the setting of gravely ill patients.

Despite the division of religion and national government politics that occurred in many Western countries starting after 1700 CE, the fusion of religion and politics that existed for thousands of years had effects that persist in the ethical and legal codes of secularized countries today. Western secularization has resulted in the substitution of religious terms (e.g., sanctity) with secular terms (e.g., inviolability) and arguments. The right of a person to maintain one's property, originated by Jean Jacque Rousseau and championed by John Locke, continues to be used in secular defenses that one human cannot legally destroy the life of another.

Scientific advancement has allowed the study of human fetal development and options for the care of sick and dying adults to a more advanced degree than what was possible for ethicists of earlier centuries. The question of modern debate no longer is at what point an embryo is biologically human but at what point it be granted rights as one. Similarly, in many instances, physicians no longer wonder how to prolong the life of a suffering individual but whether it is the morally correct decision to do so. Instances have occurred in which patients have been pronounced clinically dead or in a persistent vegetative state but have later regained consciousness.[4] It is not possible to determine when a soul departs a body.

Drafted shortly after World War II in response to Nazi experimentation on humans and an increased need for international standards, the Declaration of Geneva (Physician's Pledge) and the International Code of Medical Ethics both included language protecting the rights of the unborn. However, revisions in subsequent decades gradually made the language vaguer prior to eventually removing it altogether. Under the 1948 Declaration of Geneva, it was the duty of a doctor to respect human life "from the time of conception."[5] In 1994, the phrase was revised to respect human life "from its beginning" and in 2005 was retracted altogether.

As with all ethical dilemmas, reaching a morally acceptable decision requires examination of how each of the core ethical principles interplay. For example, while abortion proponents emphasize beneficence and patient autonomy for the mother, pro-life supporters place greater emphasis on nonmaleficence and the sanctity of human life for the unborn child.

Issues of Concern

Patient Rights in Modern Medicine

The 1973 American Hospital Association patient bill of rights was the first bill of rights written specifically for patients. Patient bills of rights by non-legal entities are limited with respect to the enforceability of the number of people they protect.

Many North American laws do protect a variety of patient rights. In 1984, Canada passed the Canada Health Act. In 1986, the United States passed 42 Code of Federal Regulations § 482.13, which established a list of patient rights that hospitals must provide patients in order to be eligible for Medicare reimbursement. In 2005, Mexico passed Article 4 as part of its national Constitution. In 2010, the United States improved patient rights when passing the Patient Protection and Affordable Care Act.

However, none of these countries has a specific law outlining a general patient bill of rights, in contrast to multiple European countries. The North American countries' "bills of rights" protecting human rights do not relate to healthcare per se. The closest that a North American government has come to pass an actual patient bill of rights was in 2001. That year saw the failed American Bipartisan Patient Protection Act, the failed Canadian Standing Senate Committee on Social Affairs, Science and Technology bill, and the failed Canadian C-261 bill. Many individual states and provinces have created their own specific patient rights policies. In the states that have no plans, the decision regarding whether or not to use such a system is up to the individual hospital. Thus, there continues to be considerable variation in standards from region to region and from hospital to hospital.

Examples of patient rights in common usage are listed below. Some rights are influenced by only one of the principles discussed above, some rights are affected by several principles, and others are influenced by other ethical principles altogether.

Informed Consent

The right to informed consent is composed of two parts: first, the right to be informed of potential harm to one's property (one's body) caused by a hired agent, and second, the right to autonomy. It was not until the 19th century that physicians began to advocate that a patient should be given an adequate amount of information to understand his or her state of health.[6] After landmark decisions by judges in the 20th century, especially in the 1970s with Canterbury v. Spence, Cobb v. Grant, and Wilkinson v. Vesey, in 1981 the American Medical Association recognized for the first time informed consent as "a basic social policy" necessary to preserve patient autonomy even at the expense of a healthcare provider's desire for beneficence.

The roots of informed consent stem from the social contract theorists of the 1600s and 1700s mentioned previously. Under this social construct, patients own their bodies (their "property"). Medical therapies may have a genuine possibility of damaging that property. Thus, patients have the right to decide independently what risks and costs to incur regarding their bodies. The null assumption by physicians for most of the history of Western medicine has been that the patient is interested in whatever plan the medical professional thinks is best. In contrast, the null assumption under the moral philosophy of autonomy is that the physician has no way of knowing what patients want and must enable patients to decide for themselves whenever the patient has the mental capacity to do so. American courts have established that a risk of harm as low as 1% must be explained to the patient before submitting the patient to any therapy if that harm entails death or a life-altering complication, such as paralysis (Canterbury v. Spence).

In the United States, the right to informed consent is protected to some degree by legislation at both the state and federal levels. 42 CFR § 482.13 states that the "patient or his or her representative (as allowed under State law) has the right to make informed decisions regarding his or her care. The patient's rights include being informed of his or her health status, being involved in care planning and treatment..." Nearly all states have statutes that provide more detail than the US Congress with respect to the specifics of informed consent rights in their particular jurisdictions. Nevertheless, in large part, because of the vagueness of informed consent rights specified by executive branches of government, the judicial branch of government is usually the branch that enforces informed consent rights as part of tort claims brought by patients against healthcare providers.

The following are the required elements for documentation of the informed consent discussion: (1) the nature of the procedure, (2) the risks and benefits and the procedure, (3) reasonable alternatives, (4) risks and benefits of alternatives, and (5) assessment of the patient's understanding of elements 1 through 4.

Refusal of Treatment (by both patient and physician)

A patient may refuse treatment that the healthcare provider deems to be an act of beneficence out of the principle of autonomy. In the United States, the right to refuse treatment is protected by 42 CFR § 482.13. Conversely, a physician may refuse to offer a treatment out of nonmaleficence because the physician believes the treatment would cause greater harm than good. Physician refusal of treatment is a more complicated issue that depends on the situation. That is primarily addressed in the United States at the state level by statutes or judicial decisions. Either way, the medical professional must provide informed consent regarding the issue when it is possible to do so (i.e., in a non-emergent condition).

Medical Treatment in an Emergency

The right to medical care in an emergency is derived from the principles of beneficence and justice, and its concept dates to the Hippocratic Writings. Life-saving measures must be taken to the point of stabilization, regardless of the patient's ability to pay for treatment. At present, medical institutions differ in their extension of this right. First, institutions differ regarding precisely what constitutes an emergency. Second, they differ in the determination of what point a patient is considered stable enough to discontinue treatment or to require some guarantee of future compensation before providing additional treatment. These differences mean that patient experiences can vary from institution to institution when receiving care in an emergency. In the United States, the right to obtain medical treatment in an emergency was established in 1986 by 42 U.S. Code § 1395dd, better known as the Emergency Medical Treatment and Active Labor Act (EMTALA) as part of the Consolidated Omnibus Budget Reconciliation Act (COBRA).

Confidentiality

Confidentiality stems both from the physician-patient fiduciary relationship and from autonomy. The patient trusts that the physician will not allow others to know particulars of the patient's illness or situation, information that could be used by others to take advantage of or otherwise harm the patient. By preservation of confidentiality, patients are granted autonomy in that they retain control over who has access to know the status of their health. In the United States, the right to confidentiality is protected by 42 CFR § 482.13.

Exceptions to confidentiality include imminent danger to the patient or others and certain infectious diseases. Reportable sexually transmitted infections (STIs) include Human papillomavirus, genital herpes, Chlamydia, gonorrhea, HIV/AIDS, and syphilis. Parental consent is needed in most situations but this excludes contraception, STI treatment, and mental health care.

Continuity of Care

Continuity of care refers to a patient's ability to choose future relationships with healthcare providers. A patient has the right to have a role in accepting or refusing referrals to certain specialists and home treatment plans. This is in the patient's best interest (beneficence) because it empowers the patient to make decisions over his or her own treatment (autonomy). Additionally, continuity of care helps preserve a patient-physician fiduciary relationship. Joint physician-patient decision-making is enhanced when care is continued beyond the initial encounter. Continuity of care also necessitates that a patient receives an explanation of how to maintain health beyond the hospital or clinical setting. If transfer to another facility or provider is required, then the patient is entitled to an explanation of why this is necessary. In the United States, continuity of care is not currently a legal right, but the Affordable Care Act did create payment incentives for organizations in order to promote continuity of care.

Ability to Speak Against Unfair Treatment

The ability for a patient or patient representative to elevate a concern to a higher level when it is not being addressed by healthcare providers in the immediate care setting helps protect a patient from persons who have been subjected to treatment that places provider desires above patient well-being. This right is protected in the United States by 42 CFR § 482.13.

Other Patient Rights in the United States at the Federal Level

In 1986, 42 CFR § 482.13 established patient rights

In 2010, the US Congress passed the Patient Protection and Affordable Care Act (ACA), although many of its rules did not go into effect until years later. Patient rights established by the ACA are distributed across 45 CFR § 146, 147, 148, 153, 155, 156, and 158.

The ACA established the following patient rights:

Right to insurance despite having a preexisting health condition, in many circumstances (e.g., for children) without facing additional charges

Right to many types of preventive care for free

Right to essential health benefits without lifetime and yearly dollar limits on coverage

Right to maintain health insurance despite becoming sick

Right to have some choice of healthcare provider

Right to some protections against employer retaliation

Right to some previously non-insured health benefits, such as benefits for mental conditions

Patient Rights in Other First World Countries

Patients in many other first world countries have rights beyond those established by the American laws cited above. In contrast to many other first world countries, in the United States, a citizen's ability to obtain treatment for a common non-life-threatening condition at a heavily subsidized cost is not a right. Some countries protect a patient's finances from seizure by the patient's healthcare provider and healthcare insurer.

Patient Declaration or Bill of Rights at Private Institutions

Some organizations, as a way of educating patients about their rights and/or as a way of adding rights not stipulated by law, use their own declarations of patient rights. The term "bill," when used formally, refers to a document that contains language that legislators evaluate for merit in becoming a law. However, when used informally, the expression "bill of rights" can refer to any declaration of rights (usually published in the form of a list). As an example of a current patient bill of rights, Indiana University Health's patient bill of rights includes the following patient rights:

To receive information about care.

To have confidential medical care.

To have continuity of care.

To have pain treated to a level of toleration.

To be free from physical restraints (unless the patient is thought to be likely to hurt himself or others)

To view medical records.

To have explanations about bills.

To provide advanced directives.

Patient rights are a subset of human rights applied in the specific context of medical treatment. The establishment of patient rights standardizes the application of ethical principles to particular medical situations. Such documents provide the framework by which a patient can expect to receive care and by which members of a healthcare team can best promote the rights for each patient. Knowledge of certain ethical principles is necessary for understanding the context by which patient rights are created. Patient rights extend to the whole person. They preserve aspects of the patient's relationship with their health care provider, even extending to third parties such as a health care insurer in some cases. Although patient rights can be traced to ancient cultures, the idea of a formal patient bill of rights is a relatively recent development, with the first such composition occurring in 1973. Though American federal laws enforce some patient rights nationally, many issues pertaining to patient rights vary by the healthcare institution.

Clinical Significance

Ethical principles and patient rights should guide all encounters with patients. Although there is not formal legal guidance in North America, many individual states and provinces have created their own specific patient rights policies. In the states with no plans, the decision regarding whether or not to use such a system is up to the individual hospital. The failure to observe ethical and legal guidelines can lead to patient dissatisfaction, medical malpractice, and loss of clinical privileges or medical license. Issues that need to be addressed are patient competence, consent, right to refuse treatment, emergency treatment, confidentiality, and continuity of care.

Proper awareness of the ethical principles and the ability to apply them to specific circumstances is relevant to all clinical specialties and settings. Without the appropriate understanding of patient rights, the healthcare team may at times be prone to various tendencies such as a failure to respect patient autonomy. This can be demonstrated in several ways, such as acting in an overly paternalistic manner or making clinical decisions on behalf of the patient and or family. Other ways it can occur include cases in which not all options, risks, or side effects may be appropriately disclosed. Furthermore, there is the ethical question when the provider chooses to leave out certain options, risks, or side effects due to an extremely low likelihood or clinical judgment. While it may be timesaving or be in what the provider believes to be the best interest of the patient, there is the question as to whether the patients are indeed fully informed, or simply informed enough. Examples of this can occur in emergent situations, when there is little time, as well as in end-of-life decisions when the family may not know the extent to which "doing everything" may entail, but it can happen in all settings. While a failure to respect patient autonomy is only one example, other patient rights can be infringed upon if members of the healthcare team are not careful. By clearly and regularly collaborating with the patient regarding their goals of care and engaging in shared decision making, as well as properly understanding the various components of patient rights, such infringements can be minimized.

Enhancing Healthcare Team Outcomes

It is imperative that the interprofessional team is aware of patient rights and ethical issues. Each physician, nurse, and allied health provider must strive to provide care within these guidelines. In this way, optimum medical outcomes are more likely. Legal or ethical consequences are less likely.

As discussed above, utilizing shared decision-making is a helpful tool for both patients and members of the healthcare team to incorporate elements of patient rights. Working together to arrive at an appropriate plan is helpful to balance between two extremes. On one end is paternalism, in which the patient has no say, and the opposite is when the patient is given an overwhelming amount of information and options then left to decide on their own without an appropriate clinical recommendation. When used appropriately, shared decision-making can encourage the patient rights of beneficence, non-maleficence, patient autonomy, patient-provider trust, and more, particularly as it helps to clarify patient goals for both the patient and the members of the healthcare team. This is becoming increasingly well-documented as studies continue to look at shared decision-making.

In a review of 105 randomized controlled studies that involved over 31,000 participants, Stacey et al [7] (Level I) found that utilizing shared decision-making exposing patients to decision aids led to them feeling more knowledgeable, better informed, and having increased clarification of values. They also probably have a more accurate perception of risk and a more active decision-making role. Other studies show shared decision-making as effective across the spectrum of medicine. In the treatment of depression in the primary care setting, utilizing a shared decision-making approach resulted in better treatment and increased patient satisfaction without longer consultation times[8] (Level 2). Benefits are not limited to primary care; a recent study published in the Journal of the American Academy of Orthopaedic Surgeons also demonstrated shared decision making to increase patient knowledge and satisfaction by empowering them to make decisions that are educated and align with their views[9] (Level 2). Numerous other studies regarding the efficacy of shared decision-making have shown similar positive results in many other specialties and settings. By using this tool and others, as well as continuing to educate on and encourage best ethical practices, providers and other members of the healthcare team will enable the flourishing of patient rights and improved outcomes.

Nursing, Allied Health, and Interprofessional Team Interventions

Every member of the interprofessional team should observe patients' rights. Allied health providers are often the first to have contact with patients and their families. They must maintain patient confidentiality and observe the right to refuse treatment. Nurses should also be involved in informed consent, medical treatment in an emergency, and continuity of care. Physicians are ultimately responsible for ensuring ethical care and for adherence to hospital policies and legal requirements.

References

1.

Young M, Wagner A. StatPearls [Internet]. StatPearls Publishing; Treasure Island (FL): Sep 18, 2022. Medical Ethics. [ PubMed: 30570982 ]

2.

3.

4.

5.

6.

Physician and Patient; or a Practical View of the Mutual Duties, Relations, and Interests of the Medical Profession and the Community. Br Foreign Med Chir Rev. 1850 Oct;6(12):503-510. [ PMC free article: PMC5199443 ] [ PubMed: 30164814 ]

7.

Stacey D, Légaré F, Lewis K, Barry MJ, Bennett CL, Eden KB, Holmes-Rovner M, Llewellyn-Thomas H, Lyddiatt A, Thomson R, Trevena L. Decision aids for people facing health treatment or screening decisions. Cochrane Database Syst Rev. 2017 Apr 12;4(4):CD001431. [ PMC free article: PMC6478132 ] [ PubMed: 28402085 ]

8.

9.

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Patient rights are a subset of human rights. Whereas the concept of human rights refers to minimum standards for the ways persons can expect to be treated by others, the concept of ethics refers to customary standards for the ways persons should treat others.

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Often data can be more easily understood in the form of visualizations than spreadsheets of numbers. The links below can help you understand and create your own health data visualizations so that you can more effectively communicate your message.

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MGH Guides: Health Data Sources: Visualizing Health Data

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A collection of frequently used health data sources.

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Data visualization and analytic tools are now critical for every healthcare organization to succeed and become more competitive. The global healthcare data analytics market is estimated to grow 3.5 times in only six years. The increase in the use of data visualization is due to the COVID-19 pandemic bringing information and data into the spotlight to provide meaningful insights. Visualization of data helps healthcare organizations operate effectively by using charts, tables, or graphs to interpret data promptly and support the decision-making process.

What is Health Data Visualization?

Medical data comes from various sources such as Electronic Medical Records (EMR) Software systems, hospital management systems, laboratories, IoT devices, and billing and coding software solutions. Data visualization tools help this data to transform into visuals so healthcare professionals can recognize various trends and interpret analytics results faster.

Examples of Data Visualization is Healthcare

Visualization tools of today can be used by different stakeholders in the healthcare sector such as healthcare providers, government officials, and patients converting complex data into visuals that can be easily comprehended. Here are some examples of modern visualization tools used by healthcare organizations in the US.

1. Interactive Dashboards

Interactive dashboards are the most regularly used data visualization tools by healthcare organizations. They provide the functionality of interactive reports which can help to display real-time data or show trends over time.

2. Interactive Websites or Apps

Interactive apps, websites, and widgets can be used by users to focus on data according to relevance and importance. For Example, the BBC website displaying updated stats on COVID-19 cases.

3. Infographics and Motion Graphics

Infographics help to visualize complex data in a format that can be easily understood by the public. Infographics can be used to provide critical information related to disease and references can be offered for prevention methods. An example of an animated infographic is motion graphics which is used extensively in tutorials and educational videos to provide higher engagement levels.

Benefits of Data Visualization in Healthcare

Data visualization is a robust tool that can facilitate the improvement of patient care by helping medical staff members identify different dangers. Real-time visualization of patient health status can help doctors categorize patients according to treatment plans. Patient records in a single dashboard provide all critical information in a single place which improves treatment effectiveness.

Data from remote patient monitoring devices and fitness apps can be used to recognize trends and reveal any health risks to the surface. Healthcare data visualization also helps governments to enhance decision-making related to public health.

Data visualizations tools along with Artificial Intelligence (AI) and machine learning can be used to eliminate errors and detect any scam in medical billing and patient prescriptions to provide seamless patient services and keep them satisfied.

Moving Ahead

Implementing healthcare data visualization tools is not an option but a need for healthcare organizations to help with patient diagnosis and treatment which is the ultimate goal of healthcare. By understanding data better decision-making can enhance which leads to improved performance.

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3 Examples of Data Visualization in Healthcare

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Data visualization and analytic tools are now critical for every healthcare organization to succeed and become more competitive. The global healthcare data analytics market is estimated to grow 3.5 times in only six years. The increase in the use of data visualization is due to the COVID-19 pandemic bringing

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TATEEDA | GLOBAL - Full cycle custom software development services and outsourcing in the USA and Ukraine

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💬 Healthcare data visualization is still a bit underrated in terms of health data analytics and representation. However, this superior method has gradually become a mega-trend and game-changer in the industry.

For many years, data visualization in healthcare has not been widely adopted and has remained massively underused by the majority of medical professionals...

Everything has changed, however, since digital health management technologies were introduced to the market. 💻

In this article, we'll discuss...

✅ How health data visualization works.
✅ How data visualization for healthcare improves processes.
✅ Methods and tools used to visualize healthcare information.
✅ Development of patient data visualization software, and more.

📈 📊 Why is healthcare data visualization a booming practice these days?
Data visualization in the healthcare industry is the representation of complex medical stats in a digestible graphical format. It allows users to easily obtain qualitative insights, changes, patterns, and trends while drawing valuable conclusions...even without developed math skills.

The medical professional community has recently discovered the additional opportunities and powers granted by digital medical data graphical representations of different types, such as...

Previously: In the pre-computer era, clinical data visualization was only available to a very narrow group of medical scholars, administrators, and analysts.

Nowadays: Thanks to the global digitization of healthcare, almost all physicians, nurses, and even patients can benefit from using data visualization tools and methods.

Why? Learn from this table...

Data collection & aggregation
Tedious manual work that required a significant amount of time and effort. 😫

A mostly automated process that requires connection to an electronic database and a few clicks. 🖱️

Calculations

Slow handwritten calculations in paper notebooks. 🧮 📓

Electronic operations; instant calculations. ⚡

Building graphs with conclusions
Visualizing and interpreting medical data required sufficient mathematical skills. 👨‍🏫

Charts are built automatically along with auto-generated analytical conclusions. 🧑‍🚀

Access to data
Hours spent working with dusty papers in a patient documentation archive or medical-record repository. 📚📚

Permission-based (login/pass) access to a database from any location or smart device. 🔃

More and more medical data is being ingested, stored, and organized in electronic systems like custom EMR/EHR software. This makes clinical data visualizations accessible through an increased number of digital tools, including...

Smartphones, tablets, and computers.

Apps, websites, modules, and widgets.

Interactive software dashboards.

Exported documents and spreadsheets.

Before you proceed to read about the visualization of data in healthcare and the development of software solutions capable of this function, take a minute to learn about our company or book a free chat with our chief technical specialist: 👇

Book a Chat

Who are we? TATEEDA GLOBAL is a custom software development company based in the United States. We can help you implement data visualization in your healthcare technology product.

✔️ Deep expertise in the engineering of custom healthcare applications with Cloud, IoT, mobile, web, and desktop components.

✔️ HQ in San Diego, California: onshore healthcare software development in a convenient time zone for North American customers.

✔️ Dedicated project managers and engineers offering personalized communications and unlimited tech consulting. Contact us today for a free consultation!

Let's check through the major components necessary for visualizing health data on computers and smart devices for medical professionals and other healthcare stakeholders.

Where to find health data for efficient visualizations: potential sources

Data visualization for the healthcare industry relies on details found in electronic patient record systems and a variety of other statistically sound and legally valid sources, including...

🖥️ EHR and EMR solutions

These systems can include enormous arrays of both qualitative and quantitative patient health data incorporating...

individual medical histories

diagnoses and treatment plans

physiological data captured through IoT medical devices

medical examination and lab test results.

About EHR and EMR: " With this type of technology, public health workers can achieve healthcare quality that has never been achieved before. It is much safer for patients, results-focused, evidence-based, time-efficient, and highly productive..." Learn more: ➡️ Custom EHR/EMR Software System Development.

📋 Data collection forms

Patient data and feedback can be extracted from patient data-capture solutions, like...

health surveys

scorecards for health tracking

patient self-reporting forms

insurance claims

insurance company databases.

💉 Clinical trials

Examination of innovative medical products and methods can generate ample amounts of valuable health information that is recorded in a database, then rigorously analyzed and interpreted for deeper insights.

⌚ Wearable and portable devices

Customer-grade or professional-grade wearables can collect large amounts of health data from people of various groups, genders, ages, conditions, and lifestyles. These wearable devices can be...

fitness trackers

pulse oximeters

wearable ECG monitors

wearable blood pressure monitors

biosensors of different types.

💾 Third-party platforms

This covers a wide range of technologically diverse data repositories:

external Big Data platforms

cloud-based datastores

digitized patient archives

API-based integrations.

Learn more: ➡️ Data Mining in Healthcare: Examples, Techniques & Benefits

Necessary provisions for healthcare data visualization

📈 Sufficient amount of medical data

Healthcare data visualization works the best when it is fueled by volumes of well-structured information. This enables health professionals to view different types of combined charts covering prolonged periods in order to come up with comprehensive conclusions.

For example, if you have the ability to tap into the massive data repository of an EHR/EMR system, you can mine it for valuable insights by visualizing information in a multitude of ways.

📊 Well-elaborated mathematical models supporting patient data visualization

The digital brain or software component underlying healthcare data visualization must contain well-founded mathematical logic and algorithms. This mechanism must work flawlessly to convert medical data into accurate, comprehensive graphics. Life-changing decisions could be made based upon these visualizations, so there should be an abundance of information to connect the dots and NO chance for mistakes.

Medical specialists don't need to possess advanced math skills or know the algorithms behind health data visualizations. Instead, they require actionable takeaways that are applicable to their goals and activities.

There exist numerous ready-made modules, programming libraries, and other software components that provide the required mathematical models and can be integrated into software solutions to save you time and money.

✅ Convenient clinical data visualization toolbox and interface

It's necessary to design and build a highly usable interface with easy-to-use controls so medical professionals can benefit from visualized healthcare data without struggling with a steep learning curve. The visualization management panel can be implemented into existing medical solutions like EHR/EMR client software or created as a standalone application for web or mobile platforms.

Do you want to develop a solution for medical data visualization?

⚡ We can help you build an application or a new software module for medical data collection, aggregation, and data visualization in the health industry.

If you have some ideas and want to discuss technical implementation, please book a free chat with our seasoned health-tech expert: 👇

Book a Chat

Thanks to the ultimate flexibility and endless possibilities offered by custom software development, there are many ways for healthcare professionals to process and view medical data with smartphones, tablets, and desktops.

Let's consider some of the most popular clinical data visualization tools in healthcare...

Interactive Dashboards

One of the most common and efficient patient data-visualization methods is building charts through multi-functional dashboards. A custom dashboard can include dozens of tools and controls for efficient visualization of healthcare data...

vitality metrics instantly captured from patients via electronic sensors

adjustable filters and zooming scales

chart type selectors

detail level settings

search capabilities

...and much more.

Medical data visualization dashboards are powerful instruments that work best for advanced users, including healthcare analysts/experts and healthcare facility administrators. Thanks to numerous settings, they provide sophisticated capabilities for deep clinical process analysis. 📉 📊

These dashboards can be developed as a standalone app and connected to an existing EMR/EHR system or created as an additional module within a software system. Please see dashboard example here: 👇

ECG graphs: One bold example of professional medical data visualization is ECG charts or cardiograms that require specific medical education and skills in order to be read and interpreted. These charts can help cardiologists not only detect a cardiac illness early but also prevent acute conditions in cardiac patients.💓👨‍⚕️

Learn more: ➡️ Remote ECG-monitoring Software Development.

Interactive Apps, Websites, and Widgets

Sometimes, healthcare specialists and/or nurses require a very specific or limited set of data visualization functionalities. They do not necessarily need complex, multi-functional dashboards.

In this case, it's better to build a neat, fast, narrowly-focused application (web or mobile) that allows medical specialists to generate a certain type of health data visualization with minimal settings.

This type of app, web interface, or widget can be also connected to EMR or EHR and/or any other patient database to extract and visualize medical data according to predefined rules and permissions. The advantages of this solution include simplicity for users and limited role-based access permissions. 💡🔐

Developing this type of app or widget normally takes less time and effort than building a powerful dashboard. Here you can see an example: 👇

Learn more: ➡️ Healthcare Product Development: How to Build an MVP

Infographics and Motion Graphics

Infographics is a simple yet powerful educational tool that works best with a wider audience, especially when it's necessary to represent important medical information in a popular format. Infographics or animated videos are easy to understand, and have greater potential for going viral on the web. This tool helps tell a story that catches the attention of non-professionals and spreads the word.

Good healthcare infographics do not require qualifications in order to be interpreted and understood. They are created by visual designers, but can be also generated with the help of specific template-based software products that allow users to assemble infographics from existing graphical objects. 👪🖼️

This type of data visualization in healthcare usually contains public educational data that can be communicated to people with limited medical knowledge. Here's an example of medical infographics: 👇

Are you interested in developing a custom healthcare data visualization tool?

⚡ At TATEEDA GLOBAL, we can help you design and develop a web-based or mobile application, widget, or full-fledged dashboard for visualization of data in healthcare, biotech, science, and/or other relevant fields.

We are seasoned professionals in custom biotech and healthcare software development, including comprehensive UI/UX design.

Please see a complete list of our medical software development services here: 👇

The main reasons for using data analytics and visualization in healthcare activities include a wide range of measurable advantages that improve medical practices on a daily basis. With this type of visual information, medical facility administrators can access a fuller grasp of the situation and be empowered to take corresponding actions...

#1. Improved patient care for clinics and medical centers

With the help of clinical data visualization, it's easier to track changes in patient satisfaction levels. For example:

You can easily build a graph showing numbers of satisfied and dissatisfied patients or clients over a period.

Compare the numbers from a monthly or annual perspective.

Visualize a decrease or increase in patient satisfaction metrics according to specific groups of patients.

Analyze the reasons for these changes as reported.

#2. Professional-grade visualization for medical devices

Whether traditional or innovative, medical equipment requires a comprehensive user interface that can be easily read by healthcare professionals. With the help of professional medical data visualization, physicians and nurses can...

Perform biosignal monitoring or organic tissue scanning in patients.

Interpret received signals in the form of diagrams and animated visualizations to identify patient health conditions.

Manage and tune up medical devices to improve visual detail and angle until the best diagnostic quality is achieved.

#3. Improved access to data insights for physicians

Medical professionals can use data visualization dashboards connected to EMR/EHR systems to generate different types of diagrams and charts. For example, they can be used to...

Visualize performance and hours spent with individual patients or certain medical activities (personal consultations/ sessions, telemedicine, working with documents, and more.)

View stats and metrics per patient in terms of conditions, treatment plans, and results.

Create health data graphical reports, print/share healthcare stats, and visualize outcomes for patients and colleagues.

Medical data visualizations can be used with the mental health EHR/EMR system because with their help, "... mental health professionals can identify patterns, correlations, and/or trends in patient behaviors and symptoms. This provides a profound background for deeper scientific research and helpful medical conclusions." Learn more: ➡️ Mental Health EMR Development

#4. Trends and pattern recognition in healthcare activities

If you have access to vast volumes of medical data, visualization can give you unlimited opportunities to observe trends and patterns-basically, find and understand correlations between events and their consequences in medical practice. For example:

You can discover declines, inclines, or plateauing in certain disease incidences.

Identify the frequency of specific symptoms in different patient groups.

Find relevance to seasonal, gender, social, economic, and other factors.

Define how your activities, like the adoption of new treatment protocols, affect patient conditions, treatment outcomes, and feedback.

Draw prognoses for future periods based on current trends and data patterns.

#5. Various levels of medical data presentations

Different audiences require different approaches to medical data visualizations. With the help of custom clinical data visualization tools, you can create a wide range of visual data representation types, including...

Graphs showing professional medical service metrics for investors and stakeholders that demand reports on your hospital, clinic, or medical center's accomplishments and achievements.

Popular infographics for current patients and healthy citizens who should be informed about potential disease symptoms and prevention methods.

Depiction of medical facility business metrics over periods of time in the context of inner reporting for healthcare staff and administrators.

#6. AI-powered predictive analytics in healthcare

Health data visualizations can be based on more than current data or archived records. With AI and ML technologies, visualizations can also help predict future situations in healthcare. For example...

Spikes or decreases in diseases and health problems based on trends, patterns, and factors.

Upcoming workforce shortages in the medical industry, in general or locally.

Expected efficiency of certain treatment plans and methods in specific patient groups.

Business forecasting and intelligence for medical centers and private healthcare providers.

Interested in improving your medical practice with data visualization?

TATEEDA GLOBAL can help you design, develop, and/or implement a medical data visualization tool. This can include integration with your current EMR or EHR solution. We have deep expertise in engineering interoperable healthcare software.

Learn more about our medical software projects: 👇

🎯 The key objective for all data visualization tools in healthcare is to acquire actionable insights and valuable takeaways to drive reasonable change. There are a number of situations and reasons that could cause medical visualization to fail in these areas.

Let's check them out...

Insufficient data or poor structuring of data objects in the database.

Incorrect mathematical models behind graphical figures and charts.

Flawed code quality that leads to incorrect conversion of data into visualizations.

Poorly designed and/or inefficiently debugged user interface.

Inaccurate interpretation of visualized data.

Erroneous data visualization settings.

1. Assign an experienced CTO or software specialist who can find and compare existing software packages for graphical representation of clinical/healthcare information, and choose the best one for your requirements.

2. If you have an EHR or EMR system, decide whether you need to connect an additional graphical module or application with data visualization functionality.

3. Identify if you need custom software development for your data visualizations: Draw a plan of desired features, tools, platforms, and functionalities.

4. Hire an IT team capable of developing a custom healthcare data visualization application or addition. There are different types of software developers and collaboration models available in the healthcare sector.

5. Consider full-scale software development outsourcing, including project management, software engineering, and quality assurance for healthcare data visualization tools.

Healthcare data visualization examples: Body scan and diagnostic charts within application dashboards

Learn more about our projects in data analytics and visualization in health care: 👇

TATEEDA GLOBAL is here to help you build a custom software solution. Our team includes skilled software engineers in web, mobile, and desktop development, plus UI/UX designers who know how to visualize information perfectly.

We have developed or assisted as software engineers and outsourcers in the following healthcare visualization areas...

✅ Dashboards for complicated biotech hardware and software solutions.
✅ ECG visualization development and associated remote monitoring.
✅ Pharmaceutical admin panel development for inventory management.
✅ Electronic patient data capture forms.
✅ Healthcare staff-management dashboards.

Interested? ➡️ Contact us for a free consultation

FAQ: The Importance of Clinical Data Visualization

What types of data visualization are most used in healthcare?

Almost all chart types are used in healthcare, including pie charts, Gantt charts, bubble charts, treemaps, scatter plots, density maps, and more. There exist countless third-party components, programming libraries, and external services that can be integrated into an application, enabling it to build charts along with additional functionalities developed on demand. This approach can save you a great deal of time and money.

Is it possible to create custom data visualizations for hospitals and medical centers?

Of course! Multiple technologies can be used to design and customize health data visualizations and graphical content for presentations, business reporting, and data-driven decision-making in medical practice. In order to create a consistent and powerful solution, it's recommended to seek out the assistance of professional programmers and developers.

What are the benefits of data visualization in healthcare?

Data analytics and visualization in health care are the cornerstone methods of all modern medical practices and processes: healthcare analytics examples include MRI diagnostics, sonography, and other instrumental medical research technologies plus sophisticated computed methods for enhanced data interpretation. Each existing method unveils its specific advantages of data visualization, so medical professionals often combine several diagnostic approaches to get more precise results for each patient. If you are seeking qualified assistance for creating or implementing of data visualization tools in healthcare, TATEEDA GLOBAL can help you with IT consulting and other services. Contact our data visualization development specialists for individualized consulting...

Do you provide clients with pharmaceutical data visualization services?

Sure, we worked with several clients from the pharma industry, including pharmaceutical product distributors and manufacturers, and we helped them implement certain pharmaceutical data visualizations like spreadsheets, Pie charts, business process flowcharts, and other graphical reports and IT solutions for visualizing pharma inventory management. If you represent a pharma company and are looking for a reliable tech partner capable of developing a custom application for building pharmaceutical and/or medical graphs and charts, make sure to contact us for more details!

What about IoT-connected data visualization in healthcare practices?

We can help you design and deliver IoT applications that collect, normalize, organize and visualize medical data from various connected devices. Contact us for more information on our skills and capabilities in the field of IoT-based solutions in healthcare, including health data capture, accumulation, and visualization through web-based dashboards and/or versatile mobile applications.

What is the average cost of a healthcare data visualization development project?

The price of a software development project depends upon multiple factors, including the scale of the software, specific features, the number of different visualization options and functions implemented, the exact composition of the development team, and much more. Estimated project cost can fall within $50,000-100,000, and the time frame can be 6-12 months. However, if you need a more precise estimation, please book a free chat with us!

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Healthcare Data Visualization Development | Tateeda Global

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📈 📊 Why is healthcare data visualization a booming practice these days? Data visualization in the healthcare industry is the representation of complex medical stats in a digestible graphical format. It allows users to easily obtain qualitative insights, changes, patterns, and trends while drawing valuable conclusions...even without developed math skills.

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Roberta Pastorino, 1, ⁴ Corrado De Vito, ² Giuseppe Migliara, ² Katrin Glocker, ³ Ilona Binenbaum, 3, ⁴ Walter Ricciardi, 1, ⁵ and Stefania Boccia 1, ⁵

Abstract

Healthcare systems around the world are facing incredible challenges due to the ageing population and the related disability, and the increasing use of technologies and citizen's expectations. Improving health outcomes while containing costs acts as a stumbling block. In this context, Big Data can help healthcare providers meet these goals in unprecedented ways. The potential of Big Data in healthcare relies on the ability to detect patterns and to turn high volumes of data into actionable knowledge for precision medicine and decision makers. In several contexts, the use of Big Data in healthcare is already offering solutions for the improvement of patient care and the generation of value in healthcare organizations. This approach requires, however, that all the relevant stakeholders collaborate and adapt the design and performance of their systems. They must build the technological infrastructure to house and converge the massive volume of healthcare data, and to invest in the human capital to guide citizens into this new frontier of human health and well-being. The present work reports an overview of best practice initiatives in Europe related to Big Data analytics in public health and oncology sectors, aimed to generate new knowledge, improve clinical care and streamline public health surveillance.

Introduction

Data have become an omnipresent concept in our daily lives with the routine collection, storage, processing and analysis of immense amount of data. This characteristic is cross-sectorial, ranging from the domain of machine learning and engineering, to economics and medicine.

Over the last decades, there has been growing enthusiasm of the potential usefulness of these massive quantities of data, called Big Data, in transforming personal care, clinical care and public health.1

An overview of Big Data definitions

Despite the term Big Data having become ubiquitous, there is no universal definition until now on the use of this term. According to McKinsey the term Big Data refers to datasets whose size is beyond the ability of typical database software tools to capture, store, manage, and analyse.2 Gartner proposed the popular definition of Big Data with the '3V': Big Data is volume, high-velocity and high-variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making.3 According to other definitions, instead, Big Data is also characterized by a fourth dimension: Veracity, concerning the quality, authenticity, 'trustworthiness' of data. 4

Furthermore, there is an emergent discussion that 'Big' is no longer the defining parameter, but rather how 'smart' the data are, focusing on the insights that the volume of data can reasonably provide.5 This aspect is fundamental in the health sector. The potential of Big Data in improving health is enormous. However, its potential value is unlocked only when leveraged to drive decision making and, to enable such evidence-based decision making, it is necessary to have efficient processes to analyse and turn high volumes of data into meaningful insights.

A specific definition of what Big Data means for health research was proposed by the Health Directorate of the Directorate-General for Research and Innovation of the European Commission: Big Data in health encompasses high volume, high diversity biological, clinical, environmental, and lifestyle information collected from single individuals to large cohorts, in relation to their health and wellness status, at one or several time points.6

Big Data analytics for health systems

The complexity of Big Data analysis arises from combining different types of information, which are electronically captured. The last years have seen an explosion of new platforms, tools and methodologies in storing, and structuring such data, followed by a growth of publications on Big Data and Health ( figure 1 ). To date, we can collect data from electronic healthcare records, social media, patient summaries, genomic and pharmaceutical data, clinical trials, telemedicine, mobile apps, sensors and information on well-being, behaviour and socio-economic indicators.

Healthcare professionals can, therefore, benefit from an incredibly large amount of data. Recent reports suggest that US healthcare system alone stored around a total of 150 exabytes of data in 2011 with the perspective to reach the yottabyte.7

Starting with the collection of individual data elements and moving to the fusion of heterogeneous data coming from different sources, can reveal entirely new approaches to improve health by providing insights into the causes and outcomes of disease, better drug targets for precision medicine, and enhanced disease prediction and prevention.

In light of this, opportunities and potential are enormous for the benefit of patients and, in general, of the healthcare system.

The potential benefits of Big Data for healthcare in the European Union

Big Data is a sensitive issue for European Union (EU) institutions: the availability of health-related Big Data can have a positive impact on medical and healthcare functions. EU is faced with several changes that may affect the sustainability of its healthcare system. By 2025 life expectancy is expected to further increase, and this may result in more people living longer, but not necessarily in a healthy and active condition. This will put further pressure on Europe's healthcare costs and economic productivity.

In this context, the data sharing approach can improve outcomes for patients and evidence-based healthcare decision making as reported during the workshop on 'Digitalisation and Big Data: implications for the health sector', held on 19 June 2018 at the European Parliament.8

The use of Big Data in healthcare, in fact, can contribute at different levels as reported by the Study on Big Data in Public Health, Telemedicine and Healthcare of the European Commission:9 (i) increasing earlier diagnosis and the effectiveness and quality of treatments by the discovery of early signals and disease intervention, reduced probability of adverse reactions, etc. (sector 1); (ii) widening possibilities for prevention of diseases by identification of risk factors for disease (sector 2); (iii) improvement of pharmacovigilance and patient safety through the ability to make more informed medical decisions based on directly delivered information to the patients (sector 3); (iv) prediction of outcomes (sector 4).

In the next paragraphs, examples of EU initiatives in the four macro sectors are listed.

Big Data have the potential to yield new insights into risk factors that lead to disease. There is the possibility to engage with the individual patient more closely and import data from mobile health applications or connected devices. These data have the potential to be analysed and used in real-time to prompt changes in behaviours that can reduce health risks, reduce harmful environmental exposures or optimize health outcomes.

Finally, Big Data can help identify and promptly intervene on high-risk and high-cost patients.10 Effective ways of managing these data can therefore facilitate precision medicine by enabling detection of heterogeneity in patient responses to treatments and tailoring of healthcare to the specific needs of individuals.11 All these aspects should eventually lead to a reduction in inefficiency and improvement in cost containment for the healthcare system.

Examples of Big Data analytics for new knowledge generation, improved clinical care and streamlined public health surveillance are already available. Below we report a selection of best practices in Europe in the public health and oncology fields.

Big Data in public health

Efforts to improve the availability and accessibility of data in the EU appear to be driven mainly by socio-economic purposes. However, great importance is placed on the need of using data and new information and communication technology (ICT) in public health to improve quality of prevention and care.

In the next years, European health systems must respond more efficiently to the exponential increase of chronic patients identifying the most efficient interventions and releasing the full potential of ICT. The e-health platforms that many European governments are trying to implement can be effective in improving the management of chronic patients in the community setting by interfacing between different health professionals and specialists and with the patients. Furthermore, a large part of EU citizens uses the internet looking for information on health and access to health services.

Moreover, Big Data and predictive analytics can contribute to precision public health by improving public health surveillance and assessment, therefore, in a public health perspective, the gathering of a very large amount of data, constitute an inestimable resource to be used in epidemiological research, analysis of the health needs of the population, evaluation of population-based intervention and informed policy making.9

Many projects across the EU are exploring the potential of available Big Data in a wide range of fields. A systematic review published in 2016 from the European Commission identified at that time 10 priority projects on Big Data implemented in Europe that fall in the four macro sectors described above and are aimed to support the sustainability of health systems by addressing the improvement of the quality and effectiveness of treatment, fighting chronic disease and supporting healthy lifestyles.9 Some of these projects focussed on gathering a very wide range of data types, from GP records, hospitalizations, drug prescription and laboratory and radiology analyses in order to create comprehensive national data warehouses. Among them, the 'Decision Support for Health Policy and Planning: Methods, Models and Technologies based on Existing Health Care Data' (DEXHELPP), the eHealth project in Estonia, the ARNO observatory in Italy and the Hospital Episode Statistics in the United Kingdom. The DEXHELPP project (mainly regarding sectors 1 and 4) used routinely collected health data sources to analyse the performance of the health system, to forecast future changes and to simulate the application of policy and interventions. The Estonian eHealth project (mainly regarding sectors 1, 2 and 3) was more oriented toward the improvement of the quality and efficiency of health services, aiming to digitalize all the information and prescription of each patient. Furthermore, in order to facilitate data collection, they provide an environment called X-Road to which all healthcare providers can link while using their own ICT solutions. The ARNO project (mainly regarding sector 4), was committed to epidemiological research, giving the possibility of deep stratification of the general population. The Hospital Episode Statistics (mainly regarding sector 4) was in charge of the Secondary Uses Service that publishes reports and analyses to support the National Health Service in the delivery of healthcare services.

Beside these projects characterized by a comprehensive approach, other initiatives focused on specific conditions (e.g. chronic conditions, rare diseases and psychiatric disorders) are also available in the EU.9 Among them, the PASSI surveillance project (mainly regarding sectors 1 and 2) in Italy provides large amount of information on the life style of almost the 90% of the population, enabling to individuate specific targets to implement and assess public health actions. The Shared Care Platform (mainly regarding sectors 1 and 3) in Denmark is focused on chronic patients, aiming to harmonize the course of treatment among health and social care providers. The Spanish Rare Diseases Registries Research Network (SpainRDR) (mainly regarding sector 1) focuses on the development of clinical research on rare diseases, providing the harmonization and unification into one comprehensive platform of pre-existing databases and registries of rare diseases. CEPHOS-LINK (mainly regarding sectors 1, 2 and 4), is a platform dedicated to mental health that involves six EU countries. It is committed to collect data on psychiatric hospital admissions and re-admissions, with the aim of finding determinants of re-admissions and to harmonize the psychiatric care pathways across the EU.

In addition to the projects reported above, the EU's framework programmes for research and innovation funded a large number of initiatives on Big Data in public health. In table 1 , we list 11 projects funded from the EU between 2012 and 2018 with a contribution over €499.999 that are captured from the Cordis website (source: cordis.europa.eu).

Table 1

Project acronym

Full title

Coordinator country

Start date

End date

Big Data in oncology

Cancer is one of the major health problems affecting our society, a situation that is set to deteriorate globally as the population grows and ages. According to the State of Health in the EU reports, cancer is recognized as one of the major contributors to premature deaths in the EU. It also has an impact on the economy in terms of lower labour market participation and productivity. Advances in Big Data analytics are given cancer researchers powerful new ways to extract value from diverse sources of data.

These diverse sources include a huge amount of data for one patient. As cancer is a molecularly highly complex disease with an enormous intra- and intertumoral heterogeneity among different cancer types and even patients, the collection of various different types of omics data can provide a unique molecular profile for each patient and significantly aid oncologists in their effort for personalized therapy approaches.12

The approach of combining these sources of data is implemented in Comprehensive Cancer Centres (CCCs).13 One of 13 CCCs in Germany is the National Center of Tumor Diseases, where the Molecularly Aided Stratification for Tumor Eradication Research (MASTER) trial is conducted (mainly regarding sector 1, 2 and 3). In a recent review article,14 this trial was illustrated as an example of a highly successful programme addressing the molecular profiling in cancer patients. Within the MASTER trial data relevant to diagnostic information of young patients with advanced-stage cancer diseases is collected by performance of whole exome or whole genome sequencing and RNA sequencing, analysed and discussed.

Another example for a success story given in the review is the INdividualized therapy FOr Relapsed Malignancies in children (INFORM) (mainly regarding sector 1, 2 and 3) registry which aims to address relapses of high-risk tumours in paediatric patients. Data from whole-exome, low-coverage whole-genome, RNA sequencing and microarray-based DNA methylation profiling are utilized to identify patient-specific therapeutic targets. The INFORM registry started as a national effort in Germany and has been extended with the participation of eight European countries, as well as Australia.

Next to the described projects there are many other initiatives which focus on the value of Big Data in oncology, the EU alone funds more than 90 projects working on this topic (projects with a funding over €499.999 are listed in table 2 ). A potential umbrella for bringing together national efforts such as those mentioned above at the European level is Cancer Core Europe.15

Table 2

Project acronym

Full title

Coordinator country

Start date

End date

Collaborations are of extremely high importance especially in the case of paediatric or other rare types of cancer, where the data collected for one patient is indeed enormous, however the number of patients a single centre can have access to is too low to obtain statistical power high enough to reach meaningful results. One of the main challenges of these collaborations is the access to the data as well as the opportunity to analyse the huge amount of data in an efficient way. Physicians, researchers and informatics experts can only benefit from collected data and expert knowledge when they get easy and intuitive access to own data or data of partners. For example, at the German Cancer Research Center, tools are developed to grant ways to access and analyse own data together with data from partners.

Additionally, international exemplary approaches of sharing data among partners or the public are done by The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) which provide researchers with access to thousands of sequenced patients with different types of cancer. The availability of these data jointly with data by other partners has enabled large meta-analyses and machine learning algorithms, integrating different types of cancer that led to the identification of novel cancer driver genes that belong to specific pathways and can be possible therapy targets. Furthermore, there are a number of public databases that provide access to catalogues of mutations found to be involved in cancer, such as the Catalogue of Somatic Mutations in Cancer (COSMIC).

All these multiple sources of information combined and the establishment and support of CCCs across Europe offer the potential to increase the number of patients that can be offered molecular profiling and individualized treatment based on Big Data analysis.

Ethical and legal issues for the effective use of Big Data in healthcare

The use of Big Data in healthcare poses new ethical and legal challenges because of the personal nature of the information enclosed.

Ethical and legal challenges include the risk to compromise privacy, personal autonomy, as well as effects on public demand for transparency, trust and fairness while using Big Data.16

Data heterogeneity, data protection, analytical flows in analysing data and the lack of appropriate infrastructures for data storage emerged as critical technical and infrastructural issues that might endanger a Big-Data-driven healthcare.

Recently, Skovgaard et al.17 explored attitudes among people living in the EU toward the reuse of health data. The review indicates that the use of health data for purposes other than treatment enjoys support among people, as long as the data are expected to further the common good. In this context, the recent call reported in Science from a number of eminent scientists worldwide, for the unrestricted use of public genomic data, finds a fertile ground from the public.18 Concerns evolve around the commercialization of data, data security and the use of data against the interests of the people providing the data.

The recent EU Data Protection Regulation (GDPR) tries to balance patients' privacy while ensuring patient's data can be shared for healthcare and research purposes.

On 23 January 2017 the Consultative Committee of the Council of Europe's data protection convention adopted 'Guidelines on the protection of individuals with regard to the processing of personal data in a world of Big Data',19 the first document on privacy and Big Data which provides suggested measures for preventing any possible negative effects of the use of Big Data on human rights and freedoms.

Therefore, any government that uses Big Data in the health sector needs to establish affirmative policies to protect the health data of individuals, in terms of confidentiality, privacy and security, while ensuring that advancements in science can take advantage from the open use of data for the community well-being.

Conclusions

Big Data is beginning to revolutionize healthcare in Europe as it offers paths and solutions to improve health of individual persons as well as to improve the performance and outcomes of healthcare systems.

The implementation of precision medicine remains contingent on significant data acquisition and timely analysis to determine the most appropriate basis on which to tailor health optimization for individual prevention, diagnosis and disease treatment. Achieving effective and proportionate governance of health-related data will be essential for the future healthcare systems, and it requires that stakeholders collaborate and adapt the design and performance of their systems to reach the maximum innovative potential of information and innovation technology on health in the EU.

In this context, EU Member States should agree on international technical standards, taking also into account openness that is considered as the basic paradigm for digital transformation. Additionally, new approaches must be found for translating the vast amount of data into meaningful information that healthcare professionals can use. Further efforts must be made to make information for doctors and health professionals more accessible and understandable.

To achieve this, existing training and education programmes for healthcare professionals should integrate the issues of data handling in the curricula to ensure the development of the necessary skills and competencies. This is one of the objectives of the 'European network staff eXchange for integrAting precision health in the health Care sysTems' consortium (ExACT)20 project that aims to integrate precision health in European health systems by training a new generation of healthcare professionals across and outside of the EU.

In conclusion, we are living in fast-moving times, not least in terms of healthcare innovation. Whilst there are pressing needs for more personalized and sustainable health services, science and technology are offering a host of potentially invaluable new tools to deliver them. A cooperation at the EU level is needed to facilitate investments both in new technology and in the human capital, in order to guide citizens into this new frontier of human health and well-being where data are becoming a significant corporate asset, a vital economic input and the foundation of new business models.

Conflicts of interest: None declared.

References

2. Manyika J, Chui M, Brown B, et al. Big data: The next frontier for innovation, competition, and productivity. 2011. www.mckinsey.com/mgi (12 September 2019, date last accessed).

3. Beyer M, Laney D The Importance of 'Big Data': A Definition. Gartner report. 2012, 1-9.

5. George G, Haas MR, Pentland A. Big Data and management. AMJ 2014;57:321-6. [ Google Scholar]

11. Hopp WJ, Li J, Wang G. Big Data and the precision medicine revolution. Prod Oper Manag 2018;27:1647-64. [ Google Scholar]

15. Eggermont AMM, Apolone G, Baumann M, et al. Cancer core Europe: a translational research infrastructure for a European mission on cancer. [[ Mol Oncol2019;13:521-7. PMC free article] [ PubMed] Google Scholar]

19. Guidelines on the protection of individuals with regard to the processing of personal data in a world of Big Data Consultative Committee of the Convention for the Protection of Individuals with Regard to Automatic Processing of Personal Data (T-PD) Guidelines 1 on the Protection of Individuals with Regard to the Processing of Personal Data in a World of Big Data 2 Directorate General of Human Rights and Rule of Law. www.coe.int/data-protection (12 September 2019, date last accessed).

20. Boccia S, Pastorino R, Mariani M, Ricciardi W. The European network staff eXchange for integrAting precision health in the health Care sysTems (ExACT): a Marie Curie Research and Innovation Staff Exchange (RISE) project. Epidemiol Biostat Public Heal 2019;16. doi: 10.2427/13122. [ Google Scholar]

Articles from The European Journal of Public Health are provided here courtesy of Oxford University Press

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Benefits and challenges of Big Data in healthcare: an overview of the European initiatives

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Healthcare systems around the world are facing incredible challenges due to the ageing population and the related disability, and the increasing use of technologies and citizen's expectations. Improving health outcomes while containing costs acts ...

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Ethical health research and privacy protections both provide valuable benefits to society. Health research is vital to improving human health and health care. Protecting patients involved in research from harm and preserving their rights is essential to ethical research. The primary justification for protecting personal privacy is to protect the interests of individuals. In contrast, the primary justification for collecting personally identifiable health information for health research is to benefit society. But it is important to stress that privacy also has value at the societal level, because it permits complex activities, including research and public health activities to be carried out in ways that protect individuals' dignity. At the same time, health research can benefit individuals, for example, when it facilitates access to new therapies, improved diagnostics, and more effective ways to prevent illness and deliver care.

The intent of this chapter is to define privacy and to delineate its importance to individuals and society as a whole. The value and importance of health research will be addressed in Chapter 3.

CONCEPTS AND VALUE OF PRIVACY

HISTORICAL DEVELOPMENT OF LEGAL PROTECTIONS OF HEALTH INFORMATION PRIVACY

The medical community has long recognized the importance of protecting privacy in maintaining public trust in doctors and researchers, and codes of medical ethics reflect a desire to increase this public trust. Since the time of Hippocrates, physicians have pledged to keep information about their patients private and confidential ( Feld and Feld, 2005). The Hippocratic Oath states, "What I may see or hear in the course of the treatment or even outside of the treatment in regard to the life of men, which on no account one must spread abroad, I will keep to myself...." This pledge to privacy has been included in the code of ethics of nearly all health care professionals in the United States. For example, the first Code of Ethics of the American Medical Association in 1847 included the concept of confidentiality ( OTA, 1993).

The value of health information privacy has also been recognized by affording it protection under the law (reviewed by Pritts, 2008). The rules for protecting the privacy of health information in the clinical care and health research contexts developed along fairly distinct paths until the promulgation of the federal privacy regulations under HIPAA. Prior to HIPAA, health information in the clinical setting was protected primarily under a combination of federal and state constitutional law, as well as state common law and statutory protections ( Box 2-1).

In contrast, research practices have been governed largely by federal regulations called the Common Rule, which have historically focused on protecting individuals from physical and mental harm in clinical trials (see subsequent sections of this chapter). Although the standards apply to research that uses personally identifiable health information, the protection of information is not their primary focus.

SECURITY OF HEALTH DATA

Protecting the security of data in health research is important because health research requires the collection, storage, and use of large amounts of personally identifiable health information, much of which may be sensitive and potentially embarrassing. If security is breached, the individuals whose health information was inappropriately accessed face a number of potential harms. The disclosure of personal information may cause intrinsic harm simply because that private information is known by others ( Saver, 2006). Another potential danger is economic harm. Individuals could lose their job, health insurance, or housing if the wrong type of information becomes public knowledge. Individuals could also experience social or psychological harm. For example, the disclosure that an individual is infected with HIV or another type of sexually transmitted infection can cause social isolation and/or other psychologically harmful results ( Gostin, 2008). Finally, security breaches could put individuals in danger of identity theft ( Pritts, 2008).

Protecting the privacy of research participants and maintaining the confidentiality of their data have always been paramount in research and a fundamental tenet of clinical research. However, several highly publicized examples of stolen or misplaced computers containing health data have heightened the public's concerns about the security of health data (for a list of security breaches in health research, see Table 2-2). The extent to which these breaches have caused tangible harm to the individuals involved is difficult to quantify ( Pritts, 2008). A Government Accountability Office (GAO) report studying major security breaches involving nonmedical personal information concluded that most security breaches do not result in identity theft ( GAO, 2007). However, the lack of identity theft resulting from past breaches is no guarantee that future breaches will not result in more serious harm. A recent report from the Identity Theft Resources Center found that identity theft is up by 69 percent for the first half of 2008, compared to the same time period in 2007 ( ITRC, 2008). Also, regardless of actual harm, security breaches are problematic for health research because they undermine public trust, which is essential for patients to be willing to participate in research ( Hodge et al., 1999). A recent study found patients believe that requiring researchers to have security plans encourages researchers to take additional precautions to protect data ( Damschroder et al., 2007). Moreover, data security is important to protect because it is a key component of comprehensive privacy practices.

The HIPAA Security Rule and Its Limitations

The goals of security are threefold: to ensure that (1) only authorized individuals see stored data; (2) they only see the data when they need to use it for an authorized purpose; and (3) what they see is accurate. Traditionally, these goals have been pursued through protections intended to make data processing safe from unauthorized access, alteration, deletion, or transmission. The HIPAA Security Rule employs this traditional solution to protecting security, and sets a floor for data security standards within covered entities ( Box 2-2).

The HIPAA Security Rule has several major gaps in security protection. First, like the HIPAA Privacy Rule, the HIPAA Security Rule only applies to covered entities. Many researchers who rely on protected health information (PHI) to conduct health research are not covered entities, and thus are not required to implement any of the security requirements outlined in the Security Rule. Although federal research regulations include protections of privacy, there are no other laws that specifically require researchers to implement security protections for research data. Second, the HIPAA Security Rule only protects electronic medical records; it does not require covered entities to implement any security protections for health information stored in paper records. There is an ongoing effort to implement electronic health records. However, many health records now exist only in paper form and may not be securely protected.

Third, many covered entities apparently are not yet in full compliance with all the requirements of the HIPAA Security Rule, based on surveys of health care privacy officers and other individuals responsible for implementing the HIPAA regulations conducted by the American Health Information Management Association (AHIMA). The surveys found that although the percentage of respondents who believe their facilities are in full compliance with the HIPAA Security Rule is increasing yearly, the number is still not 100 percent. In 2006, 1 year after implementation of the HIPAA security regulations, 25 percent of respondents described themselves as fully compliant with the Security Rule, and 50 percent described themselves as 85 to 95 percent compliant (compared to 17 percent of respondents in 2005 reporting they were fully compliant, and 43 percent describing themselves as 85 to 95 percent compliant). More than half-54 percent-of respondents reported that their covered entity had upgraded its electronic software system to comply with the HIPAA Security Rule. All the respondents reported that their covered entity has an individual responsible for assessing data protection needs and implementing solutions and staff training (compared to 89 percent in 2005), but the number of facilities reporting that they have an entire committee or task related to security decreased from 2005 (59 percent versus 78 percent) ( AHIMA, 2006).

The Centers for Medicare & Medicaid Services (CMS) has the authority to enforce the HIPAA Security Rule, and has received 378 security complaints as of 2008 without issuing any fines or penalties. A recent report issued by the HHS Office of Inspector General evaluated CMS's oversight and enforcement of the HIPAA Security Rule and "found that CMS had taken limited steps to ensure that covered entities adequately implement security protections" ( OIG, 2008). However, a 2008 Resolution Agreement entered into by the U.S. Department of Health and Human Services (HHS) and CMS with Seattle-based Providence Health & Services for breaches of the HIPAA Privacy and Security Rules may indicate that CMS is starting to take a more affirmative approach to enforcement. The agreement requires Providence Health & Services to pay $100,000 and to implement a corrective action plan to ensure electronic patient information is appropriately safeguarded against future security breaches ( OCR, 2008). In addition, CMS has recently partnered with PricewaterhouseCoopers to conduct security audits of covered entities to examine how well they are implementing the requirements of the HIPAA Security Rule. Ten to 20 assessments are planned for 2008 ( Conn, 2008). Together these actions may have a positive effect on the percentage of covered entities fully compliant with the HIPAA Security Rule.

Regardless of whether the HIPAA Security Rule is actively enforced, the other gaps in the HIPAA Security Rule's protection of personal health information are problematic because enhanced security is necessary to reduce the risk of data theft and to reinforce the public's trust in the research community by diminishing anxiety about the potential for unintentional disclosure of information. Thus, the IOM committee recommends that all institutions (both covered entities and non-covered entities) in the health research community that are involved in the collection, use, and disclosure of personally identifiable health information take strong measures to safeguard the security of health data. Given the differences among the missions and activities of institutions in the health research community, some flexibility in the implementation of specific security measures will be necessary.

Examples of measures that institutions should implement include appointment of a security officer on IRBs and Privacy Boards to be responsible for assessing data protection needs and implementing solutions and staff training; use of encryption and encoding techniques, especially for laptops and removable media containing personally identifiable health information; and implementation of a breach notification requirement, so that patients may take steps to protect their identity in the event of a breach ( IOM, 2000). More generally, institutions should implement layers of security protections, so that if security fails at one layer the breach will likely be stopped by another layer of security protection. The publication of best practices combined with a cooperative approach to compliance with security standards-such as self-evaluation, security audits, and certification programs-would also promote progress in this area. Research sponsors could play a role in the adoption of best practices in data security, by requiring researchers to implement appropriate security measures prior to providing funding. In addition, the federal government should support the development of technologies to enhance the security of health information.

Examples of security standards and guidelines already exist in some sectors, but they are not widely applied in health research. For instance, the National Institute of Standards and Technology has developed standards and guidance for the implementation of the Federal Information Security Management Act of 2002, which was meant to bolster computer and network security within the federal government and affiliated parties (e.g., government contractors). These include standards for minimum security requirements for information and information systems, as well as guidance for assessing and selecting appropriate security controls for information systems, for determining security control effectiveness, and for certifying and accrediting information systems (NIST, 2007). However, two recent GAO reports found that although the federal government is improving information security performance, a number of significant information security control deficiencies remain ( GAO, 2008a). HHS, working through its Office of the National Coordinator for Health Information Technology, could play an important role in developing or adapting standards for health research applications, and then encourage and facilitate broader use of such standards in the health research community.

POTENTIAL TECHNICAL APPROACHES TO HEALTH DATA PRIVACY AND SECURITY

The security of data will continue to grow in importance as the health care industry moves toward greater implementation of electronic health records, and Congress has already proposed numerous bills to facilitate and regulate that transition (see also Chapter 6). Advances in information technology will likely make it easier to implement such measures as audit trails and access controls in the future. Although the committee does not recommend a specific technology solution, there are at least four technological approaches to enhancing data privacy and security that have been proposed by others as having the potential to be particularly influential in health research: (1) Privacy-preserving data mining and statistical disclosure limitation, (2) personal electronic health record devices, (3) independent consent management tools, and (4) pseudonymisation. Each seeks to minimize or eliminate the transfer of personally identifiable data ( Burkert, 2001). The advantages, limitations, and current feasibility of each are described briefly below.

Privacy-preserving data mining and statistical disclosure limitation. In recent years, a number of techniques have been proposed for modifying or transforming data in such a way so as to preserve privacy while statistically analyzing the data (reviewed in Aggarwal and Yu, 2008; NRC, 2000, 2005, 2007b). Typically, such methods reduce the granularity of representation in order to protect confidentiality. There is, however, a natural trade-off between information loss and the confidentiality protection because this reduction in granularity results in diminished accuracy and utility of the data, and methods used in their analysis. Thus, a key issue is to maintain maximum utility of the data without compromising the underlying privacy constraints. In addition, there are a very large number of definitions of privacy and its protection in the statistical disclosure limitation and the privacy-preserving data mining literatures, in part because of the varying goals.

Examples of statistical disclosure limitation and privacy-preserving data mining methods include perturbation methods such as noise addition, which attempts to mask the identifiable attributes of individual records, aggregation methods such as k-anonymity, which attempts to reduce the granularity of representation of the data in such a way that a given record cannot be distinguished from at least (k - 1) other records, the release of summary statistics that can be used for actual statistical analyses such as marginal totals from contingency tables, and various approaches to the generation of synthetic data. Several of these are reviewed in Aggarwal and Yu (2008).

Other technologies include cryptographic methods for distributive privacy protection, which operate by allowing researchers to query various databases online using cryptographic algorithms (Brands, 2007; reviewed in Aggarwal and Yu, 2008), query auditing techniques, and output perturbation using methodology known as differential privacy (many of these techniques are reviewed in Aggarwal and Yu, 2008, and Dwork, 2008). These technologies aim to protect privacy by minimizing the outflow of information to researchers, as the providers of the databases do not make any of the actual data available to the researchers. The principal drawback of many of these methods relates to the potentially limited utility of the released information, especially for secondary analyses not planned in advance.

Each of the methods referred to above have strengths and weaknesses for specific kinds of statistical analyses. Precisely how this body of developing methodologies may be effectively used in the types of health research of the sort envisioned in this report remains an open question and this is an area of active research. Thus, alternative mechanisms for data protection going beyond the removal of obvious identifiers and the application of limited modifications of data elements are required. These mechanisms need to be backed up by legal penalties and sanctions.

Personal electronic health record devices. The use of personal electronic health record devices requires that all individuals possess a personal electronic device, such as a personal digital assistant (PDA) or personal computer, to manage their health information. The electronic device is intended to be used by individuals to aggregate all of their health information into one location (i.e., the electronic device). The infrastructure for implementing this privacy-enhancing technology exists, but there are several serious problems with relying on this technology in health research. First, it is unclear who would provide individuals with the devices, how they would be maintained, and who would bear the cost of the maintenance. Second, it is impossible for researchers to query every single individual for permission to access his/her personal electronic health record device in order to determine if he/she meets the criteria for the relevant study. Only individuals who are on the Internet and are involved in health research could easily be queried. Third, the use of personal electronic devices would make it almost impossible to aggregate data because of the difficulty of accessing data from multiple sources. These problems are sufficiently serious that the use of this technology is unlikely to offer a satisfactory solution to the privacy and security concerns in health research (Brands, 2007).

Independent consent management tools. The independent consent manage ment tool (or infomediary) relies on a health trust to store all of an individual's health data. When researchers are interested in accessing an individual's health information for a study, the researchers must contact the health trust. The health trust will then approach the individual and asks whether he/she is willing to give consent for the research. Examples of this technology include Microsoft's HealthVault, Google Health, and Revolution Health.

Independent consent management tools allow individuals to make blanket consents for their health information to be released for certain types of researchers. For example, an individual can have a standing consent that his/her information can be released to all researchers at the Mayo Clinic, or for all research on cancer, etc. Thus, the use of a health trust allows an individual to have the power of consent for all uses of his/her health information, but does not require a specific consent in all instances (Brands, 2007). Some privacy advocates are very favorable about the use of this technology because they see it as a way to give patients complete control over who can see and use their health information ( PPR, 2008).

However, the use of this technology in health research has several major problems. The first problem is that the health trust in this system becomes a "honey pot" (i.e., the health trust holds ALL of an individual's data). This creates serious trust and security issues because a person's entire health record is stored in a single entity (Brands, 2007). A 2006 survey of global financial services institutions found that respondents reported that nearly 50 percent of all security breaches were a result of an internal failure (e.g., a virus or worm originating inside the organization, insider fraud, or inadvertent leakage of consumer data) ( Melek and MacKinnon, 2006). Many security breaches in health care are likely also a result of internal failures. In addition, these organizations are currently not regulated by the HIPAA Privacy Rule, so there are no legal federal privacy restrictions preventing these entities from releasing individuals' data to the government, marketing companies, or others, and no mandatory data security requirements. New legislation or regulation making health trusts liable for security breaches may be necessary before the public is willing to trust these organizations to store personal health data ( Metz, 2008).

The second major impediment to the widespread adoption of independent consent management tools is the difficulty of providing individuals with secure online access to view their health information. The companies marketing this technology need to develop a mechanism where individuals can access their medical information held by the health trust without endangering its security and privacy. The current methods for individual authentication online do not work well ( NRC, 2003), but the use of a strong authentication system in a single domain may solve this problem. The companies will also need to address the fact that a significant portion of the population does not have online access at all (Brands, 2007).

The final problem with using independent consent management systems in health research is the inability to ensure the authenticity and integrity of responses. There is no existing method for the health trusts to provide the researchers with a guarantee that the information contained in their database is accurate. If data are authenticated using existing methods, such as through the use of digital signing, then it is impossible to truly protect the privacy of the individuals' information being disclosed ( NRC, 2003). Cryptographic selective disclosure techniques may be able to solve this problem, but the technology does not exist yet (Brands, 2007).

Pseudonymization. Pseudonymization is a method "used to replace the true identities (nominative) of individuals or organizations in databases by pseudo-identities (pseudo-IDs) that cannot be linked directly to their corresponding nominative identities" ( Claerhout and De Moor, 2005). The benefit of using pseudonymization in health research is that it protects individuals' identities while allowing researchers to link personal data across time and place by relying on the pseudo-IDs.

Most pseudonymization methods use a trusted third party to perform the pseudonymization process. This results in at least three entities being involved in the creation of each database. There is the data source that has access to nominative personal data (e.g., PHI), the trusted third party, and the data register that uses the pseudonymized data for research.

Two methods of pseudonymization are the batch data collection and the interactive data collection. In the batch data collection, the data supplier splits the data into two parts: (1) the identifiers that relate to a specific person (e.g., Social Security number, name), and (2) the payload data, which includes all the nonidentifiable data associated with each individual. The data are prepseudonymized at the data source and transferred to the trusted third party, which converts the prepseudonyms data into a final pseudo-ID. Both the final pseudo-ID and payload data are transferred to the data register, where they are stored and used for research; no data are stored with the trusted third party. Privacy concerns are minimized because the only version of the data that is available to researchers is pseudonymized data.

The interactive data collection is used in situations where neither the data supplier nor the data register has a need for local storage of the data. All the data is stored by a trusted third party in pseudonymous form. Both the data supplier and the data register must query the trusted third party to access the data ( Claerhout and De Moor, 2005; De Moor et al., 2003).

It is unclear how technologies relying on pseudonymization would be implemented under the requirements of the HIPAA Privacy Rule. In order for information to be considered deidentified, the HIPAA Privacy Rule specifically states that covered entities can assign a code or other means of record identification (such as a pseudo-ID), but the code cannot be derived from, or related to, information about the subject of the information. This means that any pseudo-IDs created using this technology must be based entirely on nonpersonal information. Alternatively, any researchers using the pseudonymized data must go through the normal IRB/ Privacy Board review process.

CONCLUSIONS AND RECOMMENDATIONS

Based on its review of the information described in this chapter, the committee agreed on an overarching principle to guide the formation of recommendations. The committee affirms the importance of maintaining and improving the privacy of health information. In the context of health research, privacy includes the commitment to handle personal information of patients and research participants with meaningful privacy protections, including strong security measures, transparency, and accountability. These commitments extend to everyone who collects, uses, or has access to personally identifiable health information of patients and research participants. Practices of security, transparency, and accountability take on extraordinary importance in the health research setting: Researchers and other data users should disclose clearly how and why personal information is being collected, used, and secured, and should be subject to legally enforceable obligations to ensure that personally identifiable information is used appropriately and securely. In this manner, privacy protection will help to ensure research participation and public trust and confidence in medical research.

As part of the process of implementing this principle into the federal oversight regime of health research, the committee recommends that all institutions in the health research community that are involved in the collection, use, and disclosure of personally identifiable health information should take strong measures to safeguard the security of health data. For example, institutions could:

Appoint a security officer responsible for assessing data protection needs and implementing solutions and staff training.

Make greater use of encryption and other techniques for data security.

Include data security experts on IRBs.

Implement a breach notification requirement, so that patients may take steps to protect their identity in the event of a breach.

Implement layers of security protection to eliminate single points of vulnerability to security breaches.

In addition, the federal government should support the development and use of:

Genuine privacy-enhancing techniques that minimize or eliminate the collection of personally identifiable data.

Standardized self-evaluations and security audits and certification programs to help institutions achieve the goal of safeguarding the security of personal health data.

Effective health privacy protections require effective data security measures. The HIPAA Security Rule (which entails a set of regulatory provisions separate from the Privacy Rule) already sets a floor for data security standards within covered entities, but not all institutions that conduct health research are subject to HIPAA regulations. Also, the survey data presented in this chapter show that neither the HIPAA Privacy Rule nor the HIPAA Security Rule have directly improved public confidence that personal health information will be kept confidential. Therefore, all institutions conducting health research should undertake measures to strengthen data protections. For example, given the recent spate of lost or stolen laptops containing patient health information, encryption should be required for all laptops and removable media containing such data. However, in general, given the differences among the missions and activities of institutions in the health research community, some flexibility in the implementation of specific security measures will be necessary.

Enhanced security would reduce the risk of data theft and reinforce the public's trust in the research community by diminishing anxiety about the potential for unintentional disclosure of information. The publication of best practices and outreach to all stakeholders by HHS, combined with a cooperative approach to compliance with security standards, such as self-evaluation and audit programs, would promote progress in this area. Research sponsors could also play a roll in fostering the adoption of best practices in data security.

REFERENCES

1. Aggarwal CC, Yu PS, editors. Privacy-preserving data mining: Models and algorithms. Boston, MA: Kluwer Academic Publishers; 2008.

2. Allen A. Genetic privacy: Emerging concepts and values. In: Rothstein M, editor. Genetic secrets: Protecting privacy and confidentiality in the genetic era. New Haven, CT: Yale University Press; 1997. pp. 31-59.

3. Balch GI, Doner L, Hoffman MK, Macario E. An exploration of how patients and family caregivers think about counterfeit drugs and the safety of prescription drug retail outlets for the National Health Council. Oak Park, IL: Balch Associates; 2005.

4. Balch GI, Doner LMA, Hoffman MK, Merriman MP, Monroe-Cook E, Rathjen G. Concept and message development research on engaging communities to promote electronic personal health records for the National Health Council. Oak Park, IL: Balch Associates; 2006.

5. Burkert H. Privacy-enhancing technologies: Typology, critique, vision. In: Agre PE, Rotenberg M, editors. Technology and privacy: The new landscape. Cambridge, MA: The MIT Press; 2001. pp. 125-142.

6. Dwork CS. An ad omnia approach to defining and achieving private data analysis, proceedings of the first sigkdd international workshop on privacy, security, and trust in kdd (invited). Lecture Notes in Computer Science. 2008:4890.

7. Forrester Research. National survey: Confidentiality of medical records. 1999. [accessed February 12, 2007]. http://www​.chcf.org .

8. GAO (Government Accountability Office). Personal information: Data breaches are frequent, but evidence of resulting identity theft is limited. Washington, DC: GAO; 2007.

9. GAO. Although progress reported, federal agencies need to resolve significant deficiencies: Statement of Gregory C. Wilshusen, Director, Information Security Issues. Washington, DC: GAO; 2008a.

10. GAO. Information security: Progress reported, but weaknesses at federal agencies persist: Statement of Gregory C. Wilshusen, Director, Information Security Issues. Washington, DC: GAO; 2008b.

11. Gostin L. Public health law: Power, duty, restraint. Berkeley, CA: University of California Press; 2008. Surveillance and public health research: Personal privacy and the "right to know."

12. IOM (Institute of Medicine). Protecting data privacy in health services research. Washington, DC: National Academy Press; 2000. [ PubMed: 25057723 ]

13. Lowrance WW. Learning from experience, privacy and the secondary use of data in health research. London: The Nuffield Trust; 2002.

14. Moore A. Intangible property: Privacy, power and information control. In: Moore A, editor. Information ethics: Privacy, property, and power. Seattle, WA: University of Washington Press; 2005.

15. NBAC (National Bioethics Advisory Commission). Research involving human biological materials: Ethical issues and policy guidance, report and recommendations. Vol. 1. Rockville, MD: NBAC; 1999.

16. NBAC. Ethical and policy issues in research involving human participants. Rockville, MD: NBAC; 2001.

17. NRC (National Research Council). Improving access to and confidentiality of research data: Report of a workshop. Washington, DC: National Academy Press; 2000.

18. NRC. Who goes there?: Authentication through the lens of privacy. Washington, DC: The National Academies Press; 2003.

19. NRC. Expanding access to research data: Reconciling risks and opportunities. Washington, DC: The National Academies Press; 2005.

20. NRC. Engaging privacy and information technology in a digital age. Washington, DC: The National Academies Press; 2007a.

21. NRC. Privacy and information technology in a digital age. Washington, DC: The National Academies Press; 2007b.

22. NRC. Putting people on the map: Protecting confidentiality with linked social-spatial data. Washington, DC: The National Academies Press; 2007c.

23. OIG (Office of Inspector General). Nationwide review of the Centers for Medicare & Medicaid Services Health Insurance Portability and Accountability Act of 1996 oversight. Washington, DC: Department of Health and Human Services; 2008.

24. OTA (Office of Technology Assessment). Protecting privacy in computerized medical information. Washington, DC: OTA; 1993.

25. Regan P. Legislating privacy: Technology, social values, and public policy. Chapel Hill, NC: University of North Carolina Press; 1995.

26. Research!America. America speaks: Poll summary. Vol. 7. Alexandria, VA: United Health Foundation; 2007.

27. Taylor C. Sources of the self: The making of modern identity. Cambridge, MA: Harvard University Press; 1989.

28. Tourangeau R, Rips LJ, Rasinski K. The psychology of survey response. Cambridge, UK: Cambridge University Press; 2000.

29. Turn R, Ware WH. The RAND Paper Series. Santa Monica, CA: The RAND Corporation; 1976. Privacy and security issues in information systems.

30. Wentland EJ. San Diego, CA: Academic Press; 1993. Survey responses: An evaluation of their validity.

31. Westin A. Privacy and freedom. New York: Atheneum; 1967.

32. Willison DJ, Schwartz L, Abelson J, Charles C, Swinton M, Northrup D, Thabane L. Alternatives to project-specific consent for access to personal information for health research. What do Canadians think?. Paper presented at 29th International Conference of Data Protection and Privacy Commissioners; Montreal, Canada. September 25-28. 2007.

1

2

The National Committee on Vital and Health Statistics has noted that the term "secondary uses" of health data is ill defined and therefore urged abandoning it in favor of precise description of each use. Consequently, the IOM committee has chosen to minimize use of the term in this report.

3

The ethical principle of doing no harm, based on the Hippocratic maxim, primum non nocere, first do no harm.

4

The Genetic Information Nondiscrimination Act of 2008 establishes some protections to prevent discrimination based on a patient's genetic background.

5

The " Common Rule" is the term used by 18 federal agencies who have adopted the same regulations governing the protection of human subjects of research. See Chapter 3 for a detailed description of the rule.

6

7

8

Health Insurance Portability and Accountability Act, Public Law 104-191 (1996) (most relevant sections codified at 42 U.S.C. §§ 1320(d)-1320(d)(8).

9

10

11

12

Protected health information (PHI) refers to all personally identifiable health information maintained by a HIPAA covered entity. 45 C.F.R. § 160.103 (2002).

13

Since 2004, the American Health Information Management Association has annually surveyed health care privacy officers and others whose jobs related to the HIPAA privacy function to gain an understanding of where health care organizations stand with regard to implementing the Privacy and Security Rules required by HIPAA ( AHIMA, 2006).

14

15

16

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The Value and Importance of Health Information Privacy

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Ethical health research and privacy protections both provide valuable benefits to society. Health research is vital to improving human health and health care. Protecting patients involved in research from harm and preserving their rights is essential to ethical research. The primary justification for protecting personal privacy is to protect the interests of individuals.

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Commentary

The health-care environment has undergone major changes over the past 20 years, including the expansion of partnerships between commercial insurance carriers and the U.S. Department of Health and Human Services (HHS) through its subagency, the Centers for Medicare & Medicaid Services (CMS). Most orthopaedic surgeons performing arthroplasty procedures have probably observed the growing proportion of their patients with Medicare Advantage (commercially managed Medicare) plans. Few among us were likely aware of the full implication of this transition on clinical practice, patient outcomes, and the effectiveness of registry-based arthroplasty research.

Wang and colleagues provide a thoughtful analysis of patient demographic characteristics, medical comorbidities, and 90-day postoperative adverse outcomes that were identified for 697,317 patients who underwent elective total hip or total knee arthroplasty in hospitals participating in the Premier Healthcare Database. The authors identified a 335% relative increase in patient participation in Medicare Advantage plans from 2004 to 2020, with increased enrollment of non-White patients, a propensity for increased Medicare Advantage enrollment in urban environments, and increased arthroplasty care engagement in teaching hospitals. While differences in medical comorbidities were not substantially different between patients enrolled in the 2 plan types, Medicare Advantage patients experienced an increased risk for stroke and acute kidney injury following surgery.

The authors extrapolate their findings beyond the core data obtained to present their perspectives on the impact of CMS insurance privatization on the future of high-quality joint arthroplasty care. Increased administrative burdens, including pre-authorization coordination for appropriate and clinically indicated surgical treatment, have been encountered by most practicing surgeons and their support teams. The transition of quality reporting through qualified registries-including the American Joint Replacement Registry-has been built around traditional Medicare enrollment reporting mechanisms. At least 1 potentially unintended consequence of the transition toward Medicare Advantage plans is the shielding of patient-based procedural outcomes from these mechanisms-which may serve to undermine innovation and validation.

There are some notable limitations of the study, which the authors have outlined. It is very challenging to use administrative databases to fully identify and investigate all factors that may influence adverse postoperative outcomes. While the database used by the authors identifies postoperative surgical and medical complications, it does not address the long-term performance of the implants and techniques. Moreover, the composite medical complexity of patients may not be fully represented in the analyzed data. The factors and conditions underlying patient decisions to select traditional Medicare or Medicare Advantage plans cannot be assessed, and the potential impact of selection bias on the study findings cannot be determined.

The study highlights the unanticipated consequences that may arise when fiscally driven policy intersects with specialized medical care. Future studies will be valuable to determine whether the observations in this study are sustained as the growth of Medicare Advantage plans continues-especially with the trends suggesting that under-represented minority patients will potentially be the most adversely affected. With potential implications for arthroplasty care delivery and the future ability to utilize qualified registries to assess arthroplasty outcomes, it is crucial for advocacy efforts to guide policymakers toward ensuring the successful provision of arthroplasty care-including transparency of outcomes among patients who have elected to participate in Medicare Advantage programs.

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Unintended Consequences? Medicare Commercial Partnerships... : JBJS

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The health-care environment has undergone major changes over the past 20 years, including the expansion of partnerships between commercial insurance carriers and the U.S. Department of Health and Human Services (HHS) through its subagency, the Centers for Medicare & Medicaid Services (CMS).

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CMS uses quality measures to support a patient-centered health care system anchored by quality, accessibility, affordability, innovation, and accountability. The National Impact Assessment of the Centers for Medicare & Medicaid Services (CMS) Quality Measures Reports (Impact Assessment Reports) examine data-driven results that support progress toward CMS objectives to improve public health, implement measures meaningful to patients and providers, minimize provider burden, focus on outcomes whenever possible, identify significant opportunities for improvement, and support a transition to population-based payment models. These reports are required by section 1890A(a)(6) of the Social Security Act.

2021 Report

The 2021 triennial National Impact Assessment of CMS Quality Measures Report includes a careful analysis of the quality measures used in 26 CMS quality programs. The report demonstrates substantial improvements over the past few years in quality of care, cost efficiency, and burden reduction, as well as reflects positive survey feedback on measures impact.

Key Findings of the measures impact report include:

A 24% reduction in the number of measures used in CMS quality programs.

Increased focus on outcomes measures, which increased from 39% to 46% of total measures since 2015.

Transition to digital measures, accounting for nearly 80% of measures in 2020.

Significant costs avoided calculated for a small subset of 15 Key Indicator measures, yielding total estimates ranging from $29.6 billion to $51.9 billion.

34% of Key Indicator measures analyzed with baseline disparities showing improvement in at least one measure performance comparison.

2018 Report

CMS used multiple analyses of measure performance trends, disparities, patient impact, and costs avoided, as well as national surveys in hospital and nursing home quality leaders, to evaluate the national impact of the use of quality measures. Key Indicators were selected from CMS measures with input from a Technical Expert Panel and a Federal Assessment Steering Committee to assess national performance regarding the CMS quality priorities of patient safety, person and family engagement, care coordination, effective treatment, healthy living, and affordable care. Highlights include these main findings:

Patient impacts estimated from improved national measure rates indicated approximately:

670,000 additional patients with controlled blood pressure (2006-2015).

510,000 fewer patients with poor diabetes control (2006-2015).

12,000 fewer deaths following hospitalization for a heart attack (2008-2015).

70,000 fewer unplanned readmissions (2011-2015).

840,000 fewer pressure ulcers among nursing home residents (2011-2015).

9 million more patients reporting a highly favorable experience with their hospital (2008-2015).

Costs avoided were estimated for a subset of Key Indicators, data permitting. The highest were associated with increased medication adherence ($4.2 billion-$26.9 billion), reduced pressure ulcers ($2.8 billion-$20.0 billion), and fewer patients with poor control of diabetes ($6.5 billion-$10.4 billion).

National performance trends are improving for 60% of the measures analyzed, including a majority of outcome measures, and are stable for about 31%.

Overwhelmingly, hospitals (92%) and nursing homes (91%) surveyed reported they consider CMS measures clinically important. Likewise, 90% of hospitals and 83% of nursing homes agreed that performance on CMS quality measures reflects improvements in care. Respondents also described barriers to reporting, including burden; barriers to improving performance; and unintended consequences of CMS measures.

Disparities by race/ethnicity, income, sex, rural versus urban, and region were identified. The highest percentages of measures with disparities were observed for the following groups: Black (41%), Native Hawaiian/Pacific Islander (46%), Hispanic (37%), low income (42%), noncore or rural (23%), and West North Central region (26%).

2015 Report

The 2015 Impact Report encompasses 25 CMS programs and nearly 700 quality measures from 2006 to 2013 and employs nine key research questions. A Technical Expert Panel of quality measurement leaders from across the health care industry and a Federal Assessment Steering Committee consisting of stakeholders from CMS and other U.S. Department of Health and Human Services (HHS) agencies were convened to provide input into the report.

2012 Report

For the March 2012 report, CMS assessed the impact of quality measures within two categories:

Implemented measures with at least two years of performance information between 2006 and 2010.

Measures under consideration by CMS and made available to the public in December 2011.

Useful Links

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National Impact Assessment of the Centers for Medicare & Medicaid Services (CMS) Quality Measures Reports

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CMS uses quality measures to support a patient-centered health care system anchored by quality, accessibility, affordability, innovation, and accountability. The National Impact Assessment of the Centers for Medicare & Medicaid Services (CMS) Quality Measures Reports (Impact Assessment Reports) examine data-driven results that support progress toward CMS objectives to improve public health, implement measures meaningful to patients and providers, minimize provider burden, focus on outcomes whenever possible, identify significant opportunities for improvement, and support a transition to population-based payment models.

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Guides: Mobile Device Apps: Consumer Health

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What is telehealth?

Telehealth is the use of communications technologies to provide health care from a distance. These technologies may include computers, cameras, videoconferencing, the Internet, and satellite and wireless communications. Some examples of telehealth include:

A "virtual visit" with a health care provider, through a phone call or video chat

Remote patient monitoring, which lets your provider check on you while you are at home. For example, you might wear a device that measures your heart rate and sends that information to your provider.

A surgeon using robotic technology to do surgery from a different location

Sensors that can alert caregivers if a person with dementia leaves the house

Sending your provider a message through your electronic health record (EHR)

Watching an online video that your provider sent you about how to use an inhaler

Getting an email, phone, or text reminder that it's time for a cancer screening

What is the difference between telemedicine and telehealth?

Sometimes people use the term telemedicine to mean the same thing as telehealth. Telehealth is a broader term. It includes telemedicine. But it also includes things like training for health care providers, health care administrative meetings, and services provided by pharmacists and social workers.

What are the benefits of telehealth?

Some of the benefits of telehealth include:

Getting care at home, especially for people who can't easily get to their providers' offices

Getting care from a specialist who is not close by

Getting care after office hours

More communication with your providers

Better communication and coordination between health care providers

More support for people who are managing their health conditions, especially chronic conditions such as diabetes

Lower cost, since virtual visits may be cheaper than in-person visits

What are the problems with telehealth?

Some of the problems with telehealth include:

If your virtual visit is with someone who is not your regular provider, he or she may not have all of your medical history

After a virtual visit, it may be up to you to coordinate your care with your regular provider

In some cases, the provider may not be able to make the right diagnosis without examining you in person. Or your provider may need you to come in for a lab test.

There may be problems with the technology, for example, if you lose the connection, there is a problem with the software, etc.

Some insurance companies may not cover telehealth visits

What types of care can I get using telehealth?

The types of care that you can get using telehealth may include:

For telehealth visits, just like with an in-person visit, it is important to be prepared and have good communication with the provider.

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Telehealth is the use of communications technologies to provide health care from a distance. These technologies may include computers, cameras, videoconferencing, the Internet, and satellite and wireless communications. Some examples of telehealth include: A "virtual visit" with a health care provider, through a phone call or video chat Remote patient monitoring, which lets your provider check on you while you are at home.

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The Oregon Medical Board supports a consistent standard of care and scope of practice for licensees, regardless of the delivery tool or business method enabling provider-patient communication. Telemedicine is an important tool in the delivery of health care.

See the Oregon Medical Board's Statement of Philosophy on Telemedicine for additional guidance.

To support our licensees practicing via telemedicine, here are a few principles to understand about the relevant regulations:

1. Telemedicine is not a separate form of medicine, but rather a delivery tool. It is the practice of medicine, through means of electronic communication or information technology between a health care provider at one location and a patient in another location. The same standards of care must be met regardless of the delivery method (in-person or via telemedicine).

2. The practice of medicine occurs at the patient's location. The Oregon Medical Board regulates the practice of medicinewithin Oregon, and (with some limited exceptions) an Oregon medical license is required when providing care via telemedicine for patients located in Oregon.Physicians and PAs with an Active license a Telemedicine license may practice via telemedicine in Oregon. Oregon Medical Board licensees intending to provide care to patients outside of Oregon must check with the other state's licensing board regarding licensure requirements.

Other State Resources:

When practicing via telemedicine, the licensee is expected to establish a provider-patient relationship and is subject to all the laws and regulations in the state where they are providing care. The Oregon Medical Board does not require an in-person visit to establish or maintain the provider-patient relationship; however, not all medical care can be appropriately provided via telemedicine.

Oregon allows some limited exceptions for physicians and physician assistants to the requirement to hold an Oregon license.

An Active status Oregon license allows a physician (MD/DO/DPM), physician assistant (PA), or acupuncturist (LAc) to practice via telemedicine in Oregon. To qualify for Active status, the provider must maintain a physical practice address in Oregon. An Active status licensee does not need a second, separate Oregon license to practice telemedicine.

A Telemedicine status Oregon license allows an out-of-state physician (MD/DO/DPM) or physician assistant (PA) who practices entirely outside of Oregon to provide care to patients located in Oregon via telemedicine. Telemedicine status is not currently available for acupuncture providers. (seeORS 677.135-677.141 andOAR 847-025)

Consultation between two providers, see ORS 677.060(2) and 677.137(3)

Team physicians traveling with their out-of-state athletic team, see ORS 677.060(4)

Emergency care, see ORS 677.060(5) and 677.137(2)

Established provider-patient relationship with a person temporarily in Oregon for business, work, education, or vacation, see ORS 677.137(4)

Service through the U.S. Armed Forces, U.S. Public Health Service, or U.S. Department of Veterans Affairs, see ORS 677.060(1)

Temporary or intermittent follow-up visits for continuity of care if the patient regularly receives in-person care with that provider, see Telemedicine Statement of Philosophy

During the COVID-19 pandemic, the U.S. Department of Health and Human Services issued a 4th amendment to the Declaration under the Public Readiness and Emergency Preparedness Act (PREP Act) to increase access to critical countermeasures against COVID-19. The amendment authorizes health care personnel who are permitted to order and administer a Covered Countermeasure (defined in the PREP Act) through telehealth in a state to do so for patients in another state so long as the health care personnel comply with the legal requirements of the state in which the health care personnel are permitted to order and administer the Covered Countermeasure by means of telehealth.

The Oregon Medical Board is requesting an update to the Board's telemedicine regulations. SB 232 (2023) would formalize the Board's Telemedicine Statement of Philosophy, see the Board's SB 232 information sheet.

The Federation of State Medical Boards offers a Telemedicine Policies: Board by Board Overview. For each state, the overview includes the type of license required, regulations or policy guidelines, and pending legislation.

The American Medical Association's Telemedicine Policy supports state-based licensure and outlines the components for quality telemedicine.

The Centers for Medicare & Medicaid Services (CMS) expands telehealth coverage in the proposed 2022 Physician Fee Schedule. See the proposed rule for information.

References

What License Status do I Need to Practice Via Telemedicine?

For more information or for assistance with determining the right license status for you, contact the Board's Licensing Department at licensing@omb.oregon.gov or 971-673-2700.

Additional Resources

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The Oregon Medical Board supports a consistent standard of care and scope of practice for licensees, regardless of the delivery tool or business method enabling provider-patient communication. Telemedicine is an important tool in the delivery of health care. See the Oregon Medical Board's Statement of Philosophy on Telemedicine for additional guidance.

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Find out what types of care you can get with telehealth.

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Spotlight on State Exchange: CareAccord Oregon's Health Information Exchange

CareAccord, Oregon's Health Information Exchange, is administered by the Oregon Health Authority. CareAccord facilitates the secure exchange of health information between Oregon's health care organizations and providers, enabling the coordination of care for better health, better care and lower cost.

The Oregon Health Authority has contracted with Harris Healthcare Solutions to implement a statewide Health Information Exchange (HIE). The first HIE service to be offered is Direct Secure Messaging, a secure email system using national standards that allows participants to send (push) encrypted health information directly to known, trusted recipients over the Internet. The Direct program is available at no cost through September 2013 for providers, whether or not they have an electronic health record system.

Benefits of Health Information Exchange

Enhances communications between hospitals, laboratories, pharmacies, and referring physicians

Reduces orders for duplicate procedures, tests, and medications due to lack of information

Enables smoother transitions of care because providers, hospitals and long term care facilities can share information

Supports Meaningful Use

Organizational Documents
Latest News -In the just completed 2013 session, Oregon passed legislation that continues moving the state's health system transformation forward. Building on the reforms of the last several years, a key next step is to look for better ways to engage patients in their health. To that end, Governor Kitzhaber called for nominations for people to participate on a task force to help develop recommendations around individual responsibility and health engagement. Read the July issue of Health News, updates from the Oregon Health Policy Board.

Organization Profile:

State: Oregon
Name: CareAccord
Established: 2012
Provider Information: FAQs and Getting Started and Resources
Website: https://www.careaccord.org
Contact: Contact page
Social Media: Oregon Health Authority - Facebook.com/OregonHealthAuthority or Twitter.com/OHAOregon

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Oregon Health Information Exchange - Health IT Answers

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CareAccord, Oregon's Health Information Exchange, is administered by the Oregon Health Authority. CareAccord facilitates the secure exchange of health information between Oregon's health care organizations and providers, enabling the coordination of care for better health, better care and lower cost. The Oregon Health Authority has contracted with Harris Healthcare Solutions to implement a statewide Health Information Exchange (HIE).

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Oregon Health Authority : Promoting Interoperability (Meaningful Use) in Public Health : Promoting Interoperability / Meaningful Use : State of Oregon

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Information about Promoting Interoperability, formerly called Meaningful Use (MU), to meet public health objectives.

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Informatics: Evolution of the Nelson Data, Information, Knowledge and Wisdom Model: Part 2 | OJIN: The Online Journal of Issues in Nursing

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Nursing informatics is the nursing specialty that focuses on using the tools and processes inherent in automation to achieve the goals of nursing. Computers are excellent devices for managing the smallest of details, thereby supporting a safer, more efficient, and more effective quality healthcare s…

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

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

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

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

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

who lead the stock market

who was the market outsider

who had the highest growth ratio

and who stagnated.

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

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

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

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

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

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

DIKW pyramid in decision-making

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

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

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

some steps, willfully or not, might be skipped.

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

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

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

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

3. Evaluating past experience

4. Coming up with the actions plan

5. Taking actions

6. Analyzing first results and correcting the initial plan

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

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

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

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

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

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

resources - what professionals are required to do tasks

visuals - what the design should be

budgeting - how costs will be distributed

timing - what milestones shall you plan.

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

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

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

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

design insights

content ideas

web hosting inputs

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

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

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

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

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

In a nutshell

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

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

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

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Estimated read time: 5 minutes

When a patient arrives at a health care organization, they are greeted by a nurse who asks a series of questions and collects a number of documents as part of the admission process. While providing this information is crucial for receiving quality care, chances are the patient has been through this process already at least once during a previous care encounter.

This is just one example of where interoperability, the capacity of different information systems, devices or applications to access and exchange data within and across organizational boundaries, could play a role in eliminating the redundant, time-consuming and potentially life-threatening aspects of health care.

In this Q&A, Becky Fox, chief nursing informatics officer at Atrium Health, discusses why interoperability is key to decreasing documentation burden, clinician and provider burnout, and improving the overall health care experience.

The importance of interoperability for nurses and patients
Why is interoperability important to nursing staff, and how does it impact their day-to-day work?
Fox: We want to leverage all the data we have about patients to drive the best treatment and outcomes possible. Through mining the data with analytics, we can make the data more meaningful to clinicians, so they can focus on patient care rather than having to pull information together. In the health care industry, we are at a great place right now, where we can gather information from multiple sources, quickly synthesize to get to understanding and make data actionable.

Let me share an example. When a patient comes into a health care organization or an ambulatory setting, the nurse goes through a series of questions during the visit or admission process to gather information about the patient. Wouldn't it be great if the nurse already had detailed information from all the patient's previous health experiences and didn't have to go through the exercise of asking a lot of repetitive questions? Not only data from prior visits or encounters, but also information about the patient themselves and their own health management-from wearables, to health apps, and of course, demographic and social information. It would be great if all of that information could be taken in, digested, and presented to help the clinicians focus their questions and assessments. The end result would be quicker and better care.

That is what interoperability is all about: bringing multiple sources of information together and using health care technology, innovations and analytics to apply it more meaningfully to the care we provide to patients. It's also about being able to use the data to engage patients and consumers in changing behaviors in a positive way along their health care journey. Interoperability, coupled with usability, can help remove noise and mundane tasks from the workflow. It's an opportunity to collectively leverage information that has already been collected and contribute to the bigger picture of decreasing the burden of documentation, clinician burnout and improving the patient/consumer experience.
How does interoperability impact patient care?
There are still gaps that happen in health care. For example, when a patient goes into a care setting and fills out a series of questions, screening tools and has discussions with care providers, and then they go to a different health care setting, and they have to do it all again. Anytime you are repeating processes, things can be overlooked, missed or not understood. It's also annoying to the patient! They can end up with redundant testing, procedures and assessments, and all these examples have a financial cost to the patient, the institution and payors. There is also the cost of time and potential delay in care and decision-making when, for instance, a lab test has to be repeated.

We, as health care leaders, all collectively must address this by working to improve interoperability, the usability of data and how to better share all data across all systems in meaningful ways.

Overcoming roadblocks to health care interoperability
What are the biggest challenges that nurses face with regards to data access and exchange across systems?
While health care systems collect and document similar information about patients, it is not standardized. If all health care systems, regardless of their EHR vendor, collected and documented the same data, sharing information across hospitals and health systems would be significantly easier. There are some industry leaders working together to establish standards of such, and I think in the near future, you are going to see movement in this direction.

We are also at a place in the health IT journey in which technology and analytics are really making great strides, with even bigger strides to come. We realize we must collect data from multiple places, but we need data normalization to occur in the process, so that we are all speaking a common language. The implementation of standards that we can all agree upon is also extremely important.

The other thing that is shifting is our mindset in how we view and share our data.As we move in the direction of managing the health of large populations and look at data from more outside entities, we will need to be more accepting of data which may have errors in it.We can't be purists. We must look at data in terms of themes, directions and aggregation. We know there are going to be lessons learned. And, we can't let perfect get in the way of progress. We have to participate in this transformation and not be afraid of moving forward. As an industry we all need to work together to make sure we are sharing, streamlining and consolidating data. That is what will contribute to changing health care in the U.S. and around the world.
What are your recommendations to other health care systems on how to support their nurses through advancing interoperability?
At Atrium Health, we have a significant focus on our population health strategy. Looking at sources of data across different systems is another step in the journey of pulling information together and providing the best health care for our patients. If a health care system isn't yet on a population health journey and isn't sharing information with other health systems, then this would be a good place to start. We're all in this together, and our patients are going to expect that their health information is being shared, reviewed, analyzed and leveraged to give them the best care possible.

Health systems need to make sure they are participating in all avenues of interoperability and implementing standard tools, work and documentation. Nursing leaders must participate in the conversations of how-to bring interoperability to the beside and to ambulatory clinicians, so they can be their best for their patients.

Interoperability is not just for physicians, it's for all clinicians, and nursing needs to be at the table to make sure that all of the right data sources are being brought to the forefront in meaningful ways.
At Cerner, we're doing our part to innovate and transform health information technology for nurses. Learn more here.

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Why Interoperability Progress is Significant for Nursing

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Estimated read time: 5 minutes When a patient arrives at a health care organization, they are greeted by a nurse who asks a series of questions and collects a number of documents as part of the admission process.

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What is Interoperability?

It is the ability of different information systems, devices and applications (systems) to access, exchange, integrate and cooperatively use data in a coordinated manner, within and across organizational, regional and national boundaries, to provide timely and seamless portability of information and optimize the health of individuals and populations globally.

Health data exchange architectures, application interfaces and standards enable data to be accessed and shared appropriately and securely across the complete spectrum of care, within all applicable settings and with relevant stakeholders, including the individual.

Four Levels of Interoperability

Foundational (Level 1): Establishes the inter-connectivity requirements needed for one system or application to securely communicate data to and receive data from another

Structural (Level 2): Defines the format, syntax and organization of data exchange including at the data field level for interpretation

Semantic (Level 3): Provides for common underlying models and codification of the data including the use of data elements with standardized definitions from publicly available value sets and coding vocabularies, providing shared understanding and meaning to the user

Organizational (Level 4): Includes governance, policy, social, legal and organizational considerations to facilitate the secure, seamless and timely communication and use of data both within and between organizations, entities and individuals. These components enable shared consent, trust and integrated end-user processes and workflows

What is Health Information Exchange and Data Sharing?

Health information exchange, or HIE, provides the capability to electronically move clinical information among disparate healthcare information systems and maintain the meaning of the information being exchanged. The goal of health information exchange is to facilitate access to and retrieval of clinical data to provide safe, timely, efficient, effective and equitable patient-centered care. HIE can also be used by public health authorities to assist in the analysis of the health of populations.

The term HIE is generally used as either a verb or a noun.

Verb: The electronic sharing of health-related data between two or more organizations facilitated by applied standards for use by a variety of stakeholders to inform health and care.

Noun: Organizations within the United States that provide health information exchange technology and services at a state, regional or national level and often work directly with communities to promote secure sharing of health data.

In the European Union, the act of information exchange is often referred to as data sharing. The European Commission's Support Centre for Data Sharing uses the term to refer to the collection of practices, technologies, cultural elements and legal frameworks that are relevant to transactions in any kind of information digitally, between different kinds of organizations. This term is more broadly applied to all types of data sharing, but can also be specifically used for health-related data.

Interoperability Standards

Standards provide a common language and a common set of expectations that enable interoperability between systems and/or devices. In order to seamlessly digest information about an individual and improve the overall coordination and delivery of healthcare, standards permit clinicians, labs, hospitals, pharmacies and patients to share data regardless of application or market supplier.

Standards Development

A standards organization, standards body, standards development organization (SDO) or standards setting organization is focused on developing, coordinating, promulgating, revising, amending, reissuing, interpreting, or otherwise producing standards that are intended to address the needs of some relatively wide base of affected adopters.

In order to be recognized as an SDO, an organization may be accredited by the American National Standards Institute (ANSI) or the International Organization for Standardization (ISO). Standards are also developed by other groups such as trade unions or associations. The development and adoption of open, consensus-based standards can be a complex process involving many different stakeholders and subject matter experts.

There are over 40 different SDOs in the health IT arena. Some entities create standards, such as Health Level Seven (HL7), Systematized Nomenclature of Medicine (SNOMED) International, and the Clinical Data Interchange Standards Consortium (CDISC). Others, like Integrating the Healthcare Enterprise (IHE), do not develop new standards, but rather bundle complementary base standards into IHE profiles that are used to define a specific function or use case, and then are balloted. This creates a scenario that helps drive adoption of the base standards by providing implementation guidance that describes how multiple standards can be used together to support interoperable health information exchange.

The different SDOs and profiling organizations have varying compositions and processes, but generally follow shared principles based on developing standards through a multi-stakeholder, consensus-based process to respond to specific industry or market needs.

Types of Standards

In order to understand the types of health data standards available for use, informatics professionals organize these standards into the following specific categories: vocabulary/terminology, content, transport, privacy and security, and identifiers.

Vocabulary/Terminology Standards

Vocabulary/terminology standards address the ability to represent concepts in an unambiguous manner between a sender and receiver of information, a fundamental requirement for effective communication. Health information systems that communicate with each other rely on structured vocabularies, terminologies, code sets and classification systems to represent health concepts. Some common vocabulary standards currently used in the marketplace include:

Current Procedural Terminology (CPT®): A code set, maintained by the American Medical Association (AMA), used to bill outpatient and office procedures.

Healthcare Common Procedure Coding System: A set of healthcare procedure codes based on CPT that is used for Medicare reimbursement.

ICD-10 and ICD-11: The International Statistical Classification of Diseases and Related Health Problems (ICD) is a medical classification list by the World Health Organization (WHO). It contains codes for diseases, signs and symptoms, abnormal findings, complaints, social circumstances, and external causes of injury or diseases. The 11th revision will replace the ICD-10 in January 2022.

Logical Observation Identifiers Names and Codes (LOINC®): A universal code system for identifying health measurements, observations and documents. These codes represent the "question" for a test or measurement. LOINC codes can be grouped into laboratory and clinical tests, measurements and observations.

National Drug Code (NDC): Maintained by the U.S. Food and Drug Administration, NDC provides a list of all drugs manufactured, prepared, propagated, compounded or processed for commercial distribution.

RadLex: A unified language of radiology terms for standardized indexing and retrieval of radiology information resources, managed by the Radiological Society of North America. It unifies and supplements other lexicons and standards, such as SNOMED-Clinical Terms and DICOM.

RxNorm: A terminology used to normalize names for clinical drugs and links its names to many of the drug vocabularies commonly used in pharmacy management and drug interaction software. By providing links between these vocabularies, RxNorm can mediate messages between systems not using the same software and vocabulary.

Systematized Nomenclature of Medicine-Clinical Terms (SNOMED-CT): A comprehensive clinical health terminology product. It enables the consistent, processable representation of clinical content in electronic health records (EHRs). These codes often represent the "answer" for a test or measurement to the LOINC "question" code.

The Centers for Disease Control and Prevention (CDC) provide a number of code sets for vaccines (Vaccines Administered (CVX)) and manufacturers (Manufacturers of Vaccines (MVX)). These codes can be used in immunization messages.

The Unified Code for Units of Measure: A code system intended to include all units of measures used in international science, engineering and business to facilitate unambiguous electronic communication of quantities together with their units.

Content Standards

Content standards relate to the data content within exchanges of information. They define the structure and organization of the electronic message or document's content. This standard category also includes the definition of common sets of data for specific message types.

Consolidated CDA (C-CDA): A library of CDA templates, incorporating and harmonizing previous efforts from HL7, IHE, and Health Information Technology Standards Panel (HITSP). It represents harmonization of the HL7 Health Story guides, HITSP C32, related components of IHE Patient Care Coordination and Continuity of Care Documents, or CCD.

HL7's Version 2.x (V2): A widely implemented messaging standard that allows the exchange of clinical data between systems. It is designed to support a central patient care system as well as a more distributed environment where data resides in departmental systems.

HL7 Version 3 Clinical Document Architecture (CDA®): An XML-based document markup standard that specifies the structure and semantics of "clinical documents" for the purpose of exchange between healthcare providers and patients. It defines a clinical document as having the following six characteristics: persistence, stewardship, potential for authentication, context, wholeness and human readability.

Transport Standards

Transport standards address the format of messages exchanged between computer systems, document architecture, clinical templates, user interface and patient data linkage. Standards center on "push" and "pull" methods for exchanging health information.

Digital Imaging and Communications in Medicine (DICOM): The standard for the communication and management of medical imaging information and related data. DICOM enables the transfer of medical images across systems and facilitates the development and expansion of picture archiving and communication systems.

Direct StandardTM: Defines a set of standards and protocols to allow participants to send authenticated, encrypted health information directly to known, trusted recipients over the internet. Two primary specifications are the Applicability Statement for Secure Health Transport v1.2 and the XDR and XDM for Direct Messaging.

Fast Healthcare Interoperability Resources (FHIR®): An HL7 standard for exchanging healthcare information electronically. The basic building blocks of FHIR are "resources," which describe exchangeable health data formats and elements. FHIR also provides standardization for application programming interfaces (APIs). FHIR provides a number of benefits and improvements as a modern healthcare standard including facilitating interoperable exchange with legacy standards, lower overhead, shorter learning curve, an ability to transmit only the necessary pieces of information, potential for patient mediated data, and an energized community of supporters and implementers.

IHE provides a number of specifications that can be used in the exchange of health information.

PCHAlliance co-sponsors the Personal Health Device (PHD) efforts within the IHE Devices Domain, which are focused on developing IHE profiles that leverage and build upon the Continua Design Guidelines and are also updated to support FHIR. These profiles provide guidance to implement globally recognized, consensus-based approaches to connect and test both personal and clinical devices and integrate them into health information systems. The specifications being developed support both medical devices and mainstream consumer facing apps to enable scalable interoperability of the rapidly expanding connected health ecosystem.

Privacy and Security Standards

Privacy standards aim to protect an individual's (or organization's) right to determine whether, what, when, by whom and for what purpose their personal health information is collected, accessed, used or disclosed. Security standards define a set of administrative, physical and technical actions to protect the confidentiality, availability and integrity of health information.

In the U.S., the Health Insurance Portability and Accountability Act (HIPAA) outlines standards that safeguard the privacy and security of protected health information.

HIPAA Privacy Rule: Establishes national standards to protect individuals' medical records and other personal health information. It applies to health plans, healthcare clearinghouses, and healthcare providers that conduct certain healthcare transactions electrically. The rule applies safeguards to protect the privacy of personal health information, and sets limits and conditions on the uses and disclosures of such information without patient authorization. The rule also gives patients rights over their own health information, including the right to examine and obtain a copy of their records, and to request corrections.

HIPAA Security Rule: Sets national standards for protecting the confidentiality, integrity, and availability of electronically protected health information. Compliance with the Security Rule was required as of April 20, 2005 (April 20, 2006 for small health plans). The rule addresses the technical and non-technical safeguards that "covered entities" must have in place to secure an individual's electronic protected health information. Prior to HIPAA, there were no generally accepted requirements or security standards for protecting health information.

In Europe, the General Data Protection Regulation (GDPR) outlines privacy and security regulations for all processing and storage of data relating to data subjects-or people-in the European Union (EU). This regulation extends to health information and any organization that may process or store data on these subjects, meaning it has extensive reach to many organizations worldwide and related to the sharing of data across organizations.

Identifier Standards

Entities use identifier standards to uniquely identify patients or providers.

Enterprise Master Patient Index (EMPI): A data registry used across a healthcare organization to maintain consistent and accurate data on the patients treated and managed within its departments.

Medical Record Number (MRN): An organization specific code used as a systematic documentation of a patient's history and care during a hospital stay.

National Council of State Boards of Nursing ID (NCSBN ID): A unique identifier automatically generated for each registered nurse and licensed practical/vocational nurse, freely available via the Nursys database and maintained by NCSBN.

National Provider ID (NPI): A unique 10-digit number for a healthcare provider to create a standard identification. These NPIs are included in the free NPI Registry.

Object ID (OID): A globally unique ISO identifier and a preferred scheme for unique identifiers in HL7.

Standards, Implementation Guides and Profiles

Standards on their own are helpful because they describe and constrain what data moves.

Implementation guides describe how to implement these standards for a specific use case. An implementation guide is a companion to a standard that describes how to use a standard to satisfy a specific healthcare use case. It should specify which way a standard is to be applied in a particular use case-how to structure the data consistently and what vocabulary to use. While you can implement a standard in a number of different ways, when using an implementation guide to integrate a standard into a health IT system, the guide directs one way to constrain the standard for a particular situation, removing ambiguity and achieving consistency.

A benefit to the use of implementation guides like IHE Profiles is that they provide a common language for purchasers and market suppliers to discuss the integration needs of healthcare sites and the integration capabilities of health IT products. They provide precise definitions of how to implement standards to meet specific clinical needs. They offer developers a clear implementation path for standards that has been carefully documented, reviewed and tested as well as supported by industry partners. They give purchasers a tool that reduces the complexity, cost and anxiety of implementing interoperable systems. IHE profiles, in particular, organize and leverage the integration capabilities that can be achieved by coordinated implementation of communication standards, such as DICOM, HL7 W3C and security standards.

Testing and Conformance Efforts

The adoption and implementation of standards involves a testing ecosystem that spans standards development, implementation and feedback from real-world testing into the development process to support continuous improvement. There are various different testing and compliance efforts that support advancing health IT interoperability, including:

Conformity Assessment: IHE International administers the IHE Conformity Assessment Scheme, which forms the basis for IHE Conformity Assessment Programs and any official certification of conformance to IHE Profiles associated with such testing programs. IHE International authorizes designated test laboratories accredited under this standard to assess the conformity of products with selected IHE profiles.

eHealth Exchange Testing Program: The program was developed to test compliance for HIE standards as required by the eHealth Exchange Coordinating Committee for onboarding to the eHealth Exchange network. Its purpose is to enable organizations that wish to participate in the exchange to validate the compliance of their health IT with the eHealth Exchange Performance and service specifications.

HL7 FHIR® Connectathons: These events provide hands-on FHIR development and testing opportunities held in conjunction with working group meetings. Participants engage in development and testing, working directly with other FHIR developers and senior members of the FHIR standards development team.

IHE Connectathons: A cross-vendor, supervised and structured testing event where the industry tests implementation of IHE Profiles and other standards to successful standards implementation and use. All tests are evaluated on interoperability and conformance to IHE Profiles found in IHE's technical frameworks. The test floor is overseen by IHE's technical project managers, providing a safe, neutral test environment and an opportunity for industry collaboration and problem resolution.

Office of the National Coordinator for Health IT Health IT Certification Program: A voluntary certification program established by the Office of the National Coordinator for Health IT (ONC) to provide for the certification of health IT. Requirements for certification are established by standards, implementation specifications and certification criteria adopted by the ONC secretary. The program supports the availability of certified health IT for its encouraged and required use under other federal, state and private programs. The program is run as a third-party product conformity assessment scheme for health IT based on the principles of the ISO and International Electrotechnical Commission framework.

Organizations like the Global Consortium for eHealth Interoperability, which was co-founded by HIMSS, IHE International and HL7 International, work to amplify and align the work of organizations like IHE and HL7 to increase adoption of emerging and mature health IT standards. The primary goal of the consortium is to coordinate work with governments and national ministries of health to further align existing and emerging standards and implementation guidance with strategic healthcare policy goals to achieve improved health outcomes for people everywhere.

The Interoperability Ecosystem

The health interoperability ecosystem comprises individuals, systems and processes that want to share, exchange and access all forms of health information, including discrete, narrative and multimedia. Individuals, patients, providers, hospitals/health systems, researchers, payers, suppliers and systems are potential stakeholders within this ecosystem. Each is involved in the creation, exchange and use of health information and/or data.

An efficient interoperability ecosystem provides an information infrastructure that uses technical standards, policies and protocols to enable seamless and secure capture, discovery, exchange and utilization of health information.

Exchange Partners and Data Sources

Historically, the focus was on the exchange of clinical information between providers and hospitals. As the technology has progressed, these exchange partners have broadened to include individuals, such as patients and caregivers, and organizations, such as long-term care, public health departments, community-based organizations and payers. With this expansion, along with the shift to value-based care, has also come a broader range of data types that are available to inform health and care, including device data, patient-generated data and data pertaining to the social determinants of health. Social determinants of health can include data points such as an individual's housing status, access to reliable transportation and level of food security.

Network Architectures

There are three primary types of exchange network architecture used to coordinate the exchange of health information across entities.

Centralized: Patient data are collected and stored in a centralized repository, data warehouse or other databases. The exchange organization has full control over the data, including the ability to authenticate, authorize and record transactions among participants.

Federated (Decentralized): Interconnected but independent databases allow for data sharing and exchange, and grant users access to the information only when needed.

Hybrid: Incorporates variations of federated and centralized architectures to harness the advantages of both. These are becoming common as various combinations of available services are implemented.

An important technical development that has expanded exchange partners and data sources is the emergence of healthcare APIs. APIs outline a set of clearly defined specifications to allow for one software application to build on the data and functionality of another application, without needing to understand its system design. APIs are already ubiquitous in today's web economy, and will play an essential role with respect to fueling healthcare interoperability for person centered care.

Types of HIE Organizations

An HIE organization oversees and governs the exchange of health-related information among organizations according to nationally recognized standards. The purpose of a health information exchange organization is to perform oversight and governance functions for information exchange. There are several different types of HIEs currently operating across the U.S. and its territories:

1. Hybrid HIEs are often collaborations between organizations, such as an ACO and a vendor network, within a state or region. The Kentucky Health Information Exchange is an example of a hybrid model.

2. Private/Proprietary HIEs concentrate on a single community or network, often based within a single organization, and include overall management, finance and governance. Examples may include hospital/integrated delivery system networks, payer-based HIEs and disease-specific HIEs. Some software vendors have also established an HIE network for their clients across the U.S. Additionally, the industry may see other evolving entities such as Accountable Care Organizations (ACOs) supporting information exchange.

3. Regional/Community HIEs are inter-organizational and depend on a variety of funding sources. Most are not-for-profit. Indiana Health Information Exchange and Chesapeake Regional Information System for Our Patients are examples of a regional HIEs.

4. State-wide HIEs are run by the governments of their respective states or maybe the state's designated entity. Some state-wide (and regional) HIEs use an umbrella approach and serve as the aggregator for disparate private health information exchanges. Statewide Health Information Network for New York and Arizona's Health Current are examples of a state-wide HIEs.

U.S. Exchange Initiatives: An Environmental Scan

Many organizations are actively working to achieve ubiquitous exchange. This environmental scan provides a curated review of the current landscape of the U.S.-based networks and frameworks which enable interoperable, nationwide health information exchange via a variety of methods and collaborations.

International Networks Enabling Exchange

Beyond the examples outlined above, various exchange networks and infrastructure have been established globally.

The eHealth Digital Service Infrastructure (EHDSI) facilitates the two primary building blocks of cross-border digital health services in Europe: ePrescription and Patient Summary. ePrescription (and eDispensation) allows EU citizens to obtain their medication in a pharmacy located in another EU country. Patient Summary provides information on important health related aspects such as allergies, current medication, previous illness, surgeries, etc. The digital Patient Summary is meant to provide doctors with essential information in their own language concerning the patient, when the patient comes from another EU country and there may be a linguistic barrier.

An important precursor of major exchange initiatives in Europe have been the European Reference Networks. These are virtual networks involving healthcare providers across Europe. They aim to facilitate discussion on complex diseases and conditions that require highly specialized treatment, providing concentrated knowledge and resources. Coordinators convene virtual advisory panels of medical specialists across different disciplines, using a dedicated IT platform and telemedicine tools.

Argentina's National Interoperability Network uses standards to enable communication between participating health information systems, providing a bridge for identity management across systems, sharing documents and patient summaries, managing ePrescriptions and supporting national registries.

Australia's My Health Record is an opt-out, CDA-based document repository used to store shared health summaries, eReferrals, specialist letters, discharge summaries, event summaries, prescription records, diagnostic imaging and pathology reports. Once patients have their record, they are able to manage access and permissions.

Canada's Health Infoway manages an ePrescribing service, PrescribeIT, electronically shares prescription information from clinicians with pharmacies for dispensing. Another Infoway initiative, ACCESS Health, aims to accelerate citizen access to personal health information and digital health services. The exchange initiative is expected to use a cloud-based infrastructure, a FHIR-based API service and a blockchain-enabled consent service.

Hong Kong's Electronic Health Record Sharing System is the country's equivalent to a regional HIE, managing patient data on care episodes, lab results, radiology studies and drug items. The Hong Kong Hospital Authority launched eHRSS to public and private health sectors using an opt-in patient consent model.

Uses of Information Exchange
Watch the HIMSS TV deep-dive on interoperability.
Ultimately, the goal is to be able to share and access information that informs an individual's full, longitudinal health story. By having and understanding the complete and accurate picture of an individual's health-including their preferences and other determinants of health-includes a number of benefits. Clinicians can better inform care and decision making, patients can become active participants in their care plans, and health IT developers and implementers can leverage evidence to create and adopt systems that support clinical processes and improve care delivery.

A number of benefits can be realized for exchange stakeholders, including:

1. Care coordination: Care coordination often involves a variety of stakeholders, including patients, caregivers and care teams across settings in the management of a patient's health. This is a dynamic process that requires data movement across platforms and among service providers in real time to successfully manage care.

2. Improving business and administrative processes: Having access to health information can eliminate time-consuming tasks within a health system, including processing of intake information, coordination across care teams and reporting needs associated with various regulatory requirements.

3. Increased patient safety and satisfaction: Having a full picture of a patient's health through access to disparate data can better inform clinicians on the patient's medical history, preferences and past encounters. This can help avoid duplicate testing, reduce adverse events, inform care decisions and conduct appropriate follow-up to ensure adherence with care management.

4. Value-based care: Population-level data analysis is an essential component of health systems managing high-risk patient populations, public health systems and other stakeholders working to coordinate care needs for a community. Understanding population data can enable better risk analysis and more cost-effective care. At an individual level, having access to longitudinal health data on a patient can reduce duplicative testing and close gaps in information. A key consideration in the shift to value-based care is the incorporation of non-traditional health data, such as an individual's social determinants of health data (e.g. food security status, housing stability and access to reliable transportation) into patient records. This data can be leveraged by health systems to proactively identify a health risk within a target population and intervene to work toward positive outcomes.

With these benefits in mind, taking a use-case driven approach can frame how information sharing may inform care or business practices and can help frame the potential benefits and drive interoperability forward. The following examples provide a snapshot at how interoperable exchange may occur.

Care delivery data: A physician can query a network or external health system for the appropriate patient data from other sources, such as other clinicians and hospitals. The physician can then select certain patient documents or resources (such as medication lists and reconciliations, allergy lists or lab reports) that they want to access during a patient visit.

Consent management: Where consent is not governed by other laws or regulations, a centralized consent management process can enable consensus among the community's stakeholders for a patient consent model, and provide awareness of a patient's willingness to participate in data sharing activities.

Lab results: HIE services provide physicians with the ability to get results directly via their exchange networks.

Medication reconciliation: Having a longitudinal view of all of a patient's encounters can raise awareness of the potential medications a patient was previously or is currently using. This can allow for appropriate follow up with the patient to reconcile differences in records to produce an accurate picture of their medication lists for future encounters.

Patient intake: Using a query and response process, the patient's health record can be acquired ahead of an appointment for clinician review.

Patient preferences: Exchange networks can enable access to information about a patient's care preferences, such as advance directives, empowering patients to make decisions in their care.

Provider alerts: HIE organizations are able to provide alerts to a clinician on their patients when one presents to the emergency department or is admitted to or discharged from a hospital, to ensure appropriate follow-up can be conducted.

Query services: Physicians may want to obtain more information about a patient that is referred to their office. They can query an exchange network which can in turn query other networks or health systems to assemble a complete, longitudinal health record.

Record locator services/master patient indexes: Many exchange networks enable creation of one continuous community record or search capabilities for patient records, facilitating patient identification across multiple provider settings.

Referrals: Organizations have the ability to send referrals directly through HIE services, along with all appropriate documentation.

Secure messaging: Many use cases require the exchange of patient records or related health information between clinicians in different healthcare settings with different EHRs or IT systems. For instance, providers needing to send referrals can electronically share data, eliminating paper documents and expediting the patient's treatment.

Transitions of care: As patients make the transition from one care setting to another (such as from hospital to a primary care provider or skilled nursing facility), interoperable exchange of relevant documentation from one care setting can be easily accessed at the next.

Workflow Considerations and Guidance

As the HIMSS definition suggests, interoperability goes beyond its technical requirements. Often there are policy changes influencing the value proposition and therefore willingness to exchange. Organizational considerations must align with external organizations and work toward achieving trust across stakeholders to enable meaningful data sharing. The following outlines some of these considerations and the approaches that may be leveraged.

Obtaining Patient Consent

Ensuring and communicating patient consent for data sharing activities is critical to abide by regulatory requirements and maintain the trust of the patient. There are several consent models used in information exchange, dependent on regulatory requirements within a jurisdiction.

No consent: Patient health information at a participating healthcare organization is automatically included in and available for exchange.

Opt-in: No patient data sets are made available for electronic exchange until patients actively express whether they would like to make all, or a pre-defined set, of their information available.

Opt-in with restrictions: No patient data sets are made available for electronic exchange until patients actively give their consent to participate. Patients have the option to:

Allow information to flow only to specific providers/li>

Include only specific categories of data or data elements

Make all of their information available for exchange

Opt-out: All or some predefined data sets are qualified to be included and available for exchange, after patients are given the opportunity to opt out in full.

Opt-out with exceptions: All or some predefined data sets are qualified to be included and available for exchange after patients are given the opportunity to:

Allow the exchange of their information only for specific purposes

Limit exchange of their information only for specific purposes

Limit exchange of their information to specific providers or provider organizations

Opt out in full

Selectively exclude categories of data or specific data elements from the exchange

Regardless of the model leveraged, a patient has the right to revoke their consent at any time, and processes to ensure that preference is communicated and enforced is important for data sharing processes.

Ensuring Privacy and Security

Regulations such as GDPR in the EU, HIPAA in the U.S., and various other national and state laws and regulations set forth privacy and security requirements to safeguard health information when it is created, received, transmitted, or maintained by health systems and other organizations. Privacy and security considerations may include the following:

Executing and implementing data use and reciprocal support agreements (DURSA)-also known as trust agreements-with the health systems and other stakeholders. These agreements set forth who may access and exchange the data. Additionally, such agreements set forth standard privacy and security policies and procedures that govern data protection and use. Further, data is typically encrypted when data is exchanged from one organization to another.

While specific security requirements may differ depending upon the DURSA, a typical DURSA may call for each participant to maintain appropriate administrative, physical and technical safeguards to ensure the confidentiality, integrity and availability of the data, aligned with the HIPAA requirements and any other applicable laws and/or regulations.

Producing High-Quality Data

In order to ensure that the information received from disparate sources can be trusted that it is accurate and complete, there needs to be efforts by organizations to maintain high-quality data. This can be achieved by ensuring that:

1. Data are structured and coded with standardized terminology and styles where possible to be leveraged in a machine-readable format for analysis and interpretation

2. Documentation is robust and complete to communicate the full patient story in a way that provides meaning to the human end user of the information.

Public Policy and Government Efforts

Achieving interoperability to date has required government involvement, guidance and regulation. Public policy efforts have the potential to drive forward frameworks for trusted exchange, align and educate stakeholders on existing and emerging standards and broaden stakeholder participation to ensure an inclusive exchange ecosystem for care coordination and continuity. The following outlines examples of how governments have worked to facilitate exchange both within the U.S. and in countries around the world.

United States Policies and Initiatives

With the goal of achieving ubiquitous, interoperable, nationwide exchange, the U.S. Department of Health and Human Services (HHS) has enacted and implemented a variety of legislation, regulations and guidance to further the adoption of standards-based approaches to interoperability.

21st Century Cures: Key Provisions

Among the health IT provisions outlined in 21st Century Cures, many sections provide directives to the ONC, Centers for Medicare and Medicaid Services (CMS) and other agencies related to improving interoperability.

The draft Trusted Exchange Framework and Common Agreement includes the policies, procedures and technical standards that build from existing health information network capabilities and enables them to work together to provide that single on-ramp to electronic health information regardless of the developer, health information exchange, or where a patient's records are located.

The U.S. Core Data for Interoperability is a "standardized set of health data classes and constituent data elements for nationwide, interoperable health information exchange." A "Data Class" is an "aggregation of various Data Elements by a common theme or use case."

The ONC Interoperability and Information Blocking Final Regulation implements key provisions of the 21st Century Cures Act focused on advancing interoperable exchange; supporting the access, exchange and use of electronic health information; and addressing occurrences of information blocking. The rule calls for accelerated use of healthcare APIs. ONC Certified Health IT must have standardized APIs for patient and population services, and meet specific requirements called out in the API Conditions and Maintenance of Certification. Most importantly, the regulation requires that United States Core Data for Interoperability is available for healthcare providers and patients "without special effort."

The Cures Act tasks ONC with the creation of an EHR Reporting Program as a condition of their certification and maintenance of certification. ONC has a process to solicit feedback from the industry on what criteria and methods of collection should be explored for this program. The program's reporting criteria aims to address the following five categories: security, interoperability, usability and user-centered design, conformance to certification testing, and other categories as appropriate to measure the performance of certified EHR technology.

In parallel with the release of the ONC Interoperability and Information Blocking Final Regulation, CMS also outlined an Interoperability and Patient Access Final Regulation, as part of their MyHealthEData Initiative, focused on driving exchange of and patient access to health information by liberating patient data using CMS authority to regulate Medicare Advantage, Medicaid, Children's Health Insurance Plan, and Qualified Health Plan issuers on the federally facilitated exchanges.

Interoperability Standards Advisory

The Interoperability Standards Advisory (ISA) process represents a single, public list of standards and implementation specifications published by ONC. ONC coordinates the identification, assessment and determination of these recommended standards and implementation specifications for industry use to fulfill specific clinical health IT interoperability needs. Stakeholders are encouraged to implement and use these recommended standards as applicable to their needs.

The ISA is organized into sections with related interoperability needs, each including one or more standards or implementation specifications to accomplish the listed need. Additional characteristics on these standards and implementation specifications are listed to inform stakeholders on maturity and adoption. These include:

1. Adoption Level: Approximate, average adoption level for that specific use case in healthcare within the United States.

2. Cost: Conveys whether a fee is involved to purchase, license, or obtain membership for access or use of the recommended standard or implementation specification.

3. Federally Required Status: Conveys whether a standard or implementation specification has been adopted in regulation, referenced as a federal program requirement, or referenced in a federal procurement (i.e., contract or grant).

4. Implementation Maturity: Conveys maturity based upon its implementation state.

5. Standards Process Maturity: Conveys maturity in terms of its stage within a particular organization's approval/voting process.

6. Test Tool Availability: Conveys whether a test tool is available to evaluate health IT's conformance to the standard or implementation specification.

Advancing Standards for Precision Medicine

This ONC project aims to standardize data found outside of traditional care delivery settings. As part of the precision medicine initiative, it focuses on standards in two areas:

1. Mobile health, sensor and wearable data

2. Social determinants of health data

Interoperability Proving Ground

ONC hosts an open, community platform where individuals and organizations can share and learn from projects occurring in the United States and internationally.

Data: Elemental to Health Campaign

HIMSS and PCHAlliance strongly urge prioritization of necessary funding and technical assistance to implement modern health information and technology strategies to ensure timely interoperability and sharing of public health surveillance data. An important component is the work in partnership with the Association of Public Health Laboratories, Council of State and Territorial Epidemiologists and the National Association for Public Health Statistics and Information Systems that support this multi-year effort to modernize public health data systems, surveillance and analytics at the CDC, and state, local and tribal health departments. Funding included in the Fiscal Year 2020 Appropriations Package and the Coronavirus Aid, Relief and Economic Security (CARES) Act, will help address some of the challenges, but in the long term, we need a predictable and sustained funding source to fully tackle this issue.

State-Level Interoperability Initiatives

In the U.S., state public health agencies manage and track information for immunizations, infectious diseases and vital statistics, however, they often lack adequate funding to ensure their information systems are updated and conform to national standards and profiles. Health information exchanges -which are public or private entities, depending on the state and region-aim to ensure there is robust and standardized data exchange between and among public health entities and state and local health departments.

Global Governmental Policies and Initiatives

At an international level, there are a variety of initiatives being led by health ministries and governments to further country-level and cross-border interoperability efforts.

The Global Digital Health Partnership (GDHP) is a collaboration of over 40 governments and territories, government agencies and the World Health Organization, formed to support the effective implementation of digital health services. Interoperability is one of the various work streams explored by GDHP. The proposed work stream activities focus on the evolving challenges of sharing patient data between care providers, organizations, caregivers and patients. These challenges are partly a technical problem for health systems, clinicians and patients, however they also pose significant risks for patient safety, and detract from high-quality coordinated care and the efficient delivery of services. A GDHP report outlines a variety of government efforts from 15 participating countries.

In Europe, the EU EHR Exchange Format developed the EU's eHealth Network to support the digital transformation of health and care in the EU by seeking to unlock the flow of health data across borders, and support implementation of the Cross-Border Health Care Directive. The recommendation seeks to facilitate the cross-border exchange of EHRs in the EU by supporting member states in their efforts to ensure that citizens can securely access and exchange their health data wherever they are in the EU. It will help citizens to quickly access and share their health data with healthcare professionals, for example, when consulting a specialist or receiving emergency treatment in another EU country.

The backbone infrastructure for the EHR exchange is the EHDSI. The EHDSI consists of services and infrastructure that use information and communication technologies to enable cross-border healthcare services. Initial focus is on enabling the exchange of baseline health information, including:

ePatient Summary to provide access to verified key health data of a patient during an unplanned care encounter while abroad

ePrescriptions to enables patients to receive equivalent medication treatment while abroad to what they would receive in their home country

A number of EU countries are already exchanging the ePatient summaries and ePrescriptions. As part of the EHDSI, Finland and Estonia were among the first to implement infrastructures to support the exchange of patient summaries, leveraging the X-Road platform, a free and open-source data exchange layer solution that enables organizations to exchange information securely over the internet. Future phases will include lab results, medical imaging and reports, and hospital discharge letters.

The European Commission also outlined priorities for 2019-2024 EU Digital Strategy, which included the creation of a European Health Data Space (EHDS) to foster the exchange and sharing of different kinds of health data (electronic health records, genomics, registries, etc.) in Europe. It aims to not only support exchange for the delivery of primary care, as well as, the development of new treatments, medicines, medical devices and services. Beyond the citizens, this would help meet the needs of different users and actors in the health system, while simultaneously protecting citizens' data. The commission is currently working with the member states and stakeholders to define the best governance structure and set up the appropriate infrastructure for the EHDS. Additionally, some existing regulatory gaps regarding member states implementation of the GDPR for the secondary use of data are being addressed.

The European Interoperability Framework (EIF) gives specific guidance on how to set up interoperable digital public services. It offers public administrations 47 concrete recommendations on how to improve governance of their interoperability activities, establish cross-organizational relationships, streamline processes supporting end-to-end digital services, and ensure that both existing and new legislation do not compromise efforts.

In an effort to ensure secure and federated access to genomic data, the European 1+ Million Genomes Initiative aims to create a technical infrastructure to improve disease prevention, allow for more personalized treatments and provide a sufficient scale for new clinically impactful research. In addition to the technical foundation for this effort, the signatory countries will also assess ethical and legal implications of genomics, and data storage, security and ethical uses.

Beyond these broader initiatives occurring across Europe, many countries are also leading specific regional and national efforts to expand exchange abilities./p>

The Nordic Interoperability Project is a consolidated grassroots effort to demonstrate the value for the patient from a seamless cross-border exchange of health data. The consortium includes organizations from all five Nordic countries-Denmark, Finland, Iceland, Norway, Sweden-and works to enable patients to live and act in an open, seamless, cross-border healthcare ecosystem, by showcasing and implementing solutions and innovations from the Nordics. The goal is to enable the Nordics by 2030 to be the most sustainable and integrated health region in the world, providing personalized healthcare for all its citizens.

In Portugal, the Shared Services of the Ministry of Health (SPMS) is a state-owned enterprise that manages the Portuguese Health Ministry Central Purchasing and serves as the national IT authority and the Portuguese representative for eHealth cross-border affairs. SPMS provides shared services in the areas of purchasing and logistics, financial services, human resources, and information and communication technologies to health and care entities to centralize and optimize the procurement of goods and services within the Portuguese National Health System.

In Asia and the South Pacific, many governments are also adopting digital health strategies to encourage interoperable exchange. For example, India's The Ministry of Health and Family Welfare (MoHFW) publishes EHR standards and created a Centre for Health Informatics under the eHealth Division of MoHFW as part of their Digital Health Strategy to initiate several digital health initiatives in the country, and started the National Health Portal as a citizen portal with the objective of improving the health literacy of the masses in India. New Zealand's health agencies, organizations and individuals signed a Commitment to New Zealand Health Interoperability to outline principles to drive the adoption of exchange infrastructures and processes in their health sector.

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Discover key insights about interoperability and better understand how data sharing can make a difference in healthcare.

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Mary A. Dolansky, PhD, RN

Mary A. Dolansky, PhD, RN

Mary A. Dolansky is an Associate Professor at the Frances Payne Bolton School of Nursing, Case Western Reserve University in Cleveland, OH. Dr. Dolansky is Director of the QSEN Institute (Quality and Safety Education for Nurses) and Senior Fellow in the VA Quality Scholars program, mentoring pre- and post-doctoral students in quality and safety science. She has co-published two books on quality improvement, co-authored several book chapters and articles, and was guest editor on a special quality improvement education issue in the Journal of Quality Management in Health Care. She has taught the interdisciplinary course, &ldquo;Continual Improvement in Health Care,&rdquo; at CWRU for the past 8 years and was chair of the quality and safety task force at the School of Nursing that integrated quality and safety into the undergraduate and graduate curriculum.

Shirley M. Moore, PhD, RN, FAAN

Shirley M. Moore, PhD, RN, FAAN

Shirley M. Moore is the Edward J. and Louise Mellon Professor of Nursing and Associate Dean for Research, Case Western Reserve University in Cleveland, OH. She is a past President of the Academy for Healthcare Improvement and is on the leadership team of the national Quality and Safety Education for Nurses (QSEN) project. She is currently leading the integration of nurse scholars in the VA Quality Scholars Program. She also is conducting NIH-funded studies testing a process improvement approach to health behavior change with patients.

Over a decade has passed since the Institute of Medicine's reports on the need to improve the American healthcare system, and yet only slight improvement in quality and safety has been reported. The Quality and Safety Education for Nurses (QSEN) initiative was developed to integrate quality and safety competencies into nursing education. The current challenge is for nurses to move beyond the application of QSEN competencies to individual patients and families and incorporate systems thinking in quality and safety education and healthcare delivery. This article provides a history of QSEN and proposes a framework in which systems thinking is a critical aspect in the application of the QSEN competencies. We provide examples of how using this framework expands nursing focus from individual care to care of the system and propose ways to teach and measure systems thinking. The conclusion calls for movement from personal effort and individual care to a focus on care of the system that will accelerate improvement of healthcare quality and safety.

Key words: QSEN, quality, safety, systems, QSEN competencies, education, measurement

...national healthcare quality organizations, such as the Leapfrog Group, report that the majority of hospitals have demonstrated little progress in improving quality and safety. Over a decade has passed since the Institute of Medicine (IOM) report, To Err Is Human: Building a Safer Health System, and the follow-up report, Crossing the Quality Chasm, which turned healthcare professionals' attention to the importance of improving healthcare outcomes ( IOM, 2000; Committee on the Quality, 2001). These reports highlighted the need to redesign systems of care to better serve patients in the complex healthcare environment. During the last decade, national initiatives to improve quality and safety have been implemented, such as the Institute for Healthcare Improvement's (IHI) Transforming Care at the Bedside, 5 Million Lives Campaign, and the Triple Aim ( IHI, 2013a; IHI, 2013b; IHI, 2013c). To accelerate change, regulatory agencies have implemented National Patient Safety Goals, Core Measures, ( Joint Commission, 2013a; 2013b; 2013c), and Hospital Acquired Conditions (HAC) Never Events ( Kuhn, 2008). Yet national healthcare quality organizations, such as the Leapfrog Group, report that the majority of hospitals have demonstrated little progress in improving quality and safety. For example, although we know that zero central line infections should be a reality in hospitals, thousands of infections are still reported each year ( Clark, 2013).

QSEN is a national movement that guides nurses to redesign the 'what and how' they deliver nursing care so that they can ensure high-quality, safe care. In 2005, nursing leaders responded to the IOM call to improve the quality of healthcare by forming the Quality and Safety Education for Nurses (QSEN) initiative funded by the Robert Wood Johnson Foundation. The QSEN initiative consisted of the development of quality and safety competencies that serve as a resource for nursing faculty to integrate contemporary quality and safety content into nursing education ( QSEN Institute, 2013). The focus of QSEN, now the QSEN Institute, has expanded from undergraduate nursing students' education to include quality and safety education for all nurses. The mission of QSEN is to address the challenge of assuring that nurses have the knowledge, skills, and attitudes (KSA) necessary to continuously improve the quality and safety of the healthcare systems in which they work. QSEN is a national movement that guides nurses to redesign the 'what and how' they deliver nursing care so that they can ensure high-quality, safe care. Linda Cronenwett, PhD, RN, FAAN, the founder of QSEN, often states that QSEN helps nurses to identify and bridge the gaps between what is and what should be and helps nurses focus their work from the lens of quality and safety (Personal Communication, 2013).

Viewing nurses' work through the lens of quality and safety requires a contemporary approach that incorporates systems thinking. A crucial skill, systems thinking helps nurses to meet the challenge of improving healthcare as they move beyond the application of the QSEN competencies from individual patients and families to accelerate the overall improvement of healthcare quality and safety. In this article, we review the history of QSEN and propose a framework that expands nursing focus from individual care based on personal effort and care of the individual to systems thinking and care of the system. Examples are provided to demonstrate how to integrate systems thinking in the application of QSEN competencies and how systems thinking can be taught and measured.

Although QSEN competencies have spurred quality and safety in nursing education, it is now time to accelerate their use and impact. In response to calls for improved quality and safety, leaders from schools of nursing across the country joined forces to create the Quality and Safety Education for Nurses (QSEN) initiative. The Robert Wood Johnson Foundation in 2005 funded QSEN Phase 1 and three subsequent phases followed ( Table 1). The major QSEN contribution to healthcare education was the creation of six QSEN competencies (modeled after the IOM reports) and the pre-licensure and graduate-level knowledge, skills, and attitude (KSA) statements for each competency ( Cronenwett et al., 2007). The competency statements provide a tool for faculty and staff development educators to identify gaps in curriculum so that changes to incorporate quality and safety education can be made ( Barnsteiner et al., 2013). The QSEN website serves as a national educational resource and a repository for nurses to publish contemporary teaching strategies focused on the six competencies: patient-centered care, teamwork and collaboration, evidenced-based practice, quality improvement, and informatics. Currently, there are over 100 teaching strategies posted.

Table 1. History of QSEN

Phase

Details

Websites and References

Phase 1 a

October 2005-March 2007

QSEN competencies and their requisite KSAs

QSEN.org website

qsen.org/competencies/pre-licensure-ksas/
Cronenwett et al., 2007

Phase 2 a

April 2007-October 2008

Funded 15 pilot schools to use the IHI Learning Collaborative method to develop, test, and disseminate teaching strategies

Peer reviewed teaching strategies on the website

qsen.org/about-qsen/pilot-schools-june-2007-november-2008/

http://qsen.org/teaching-strategies/

Phase 3 a

November 2008-February 2012

National forums to educate nursing faculty

Incorporation of nurses into the Veterans Affairs (VA)

Quality Scholars program (VAQS- 2 year pre or post-doctoral fellowships in quality and safety)

Faculty modules to the QSEN website

8 regional Faculty Development workshops (train the trainer) were coordinated by the AACN

http://qsen.org/conferences/
VAQS.org http://qsen.org/faculty-resources/learning-modules/
Barnsteiner et al., 2013
http://qsen.org/faculty-resources/aacn-workshop-modules/

Phase 4 a

March 2012-March 2014

American Association of Colleges of Nursing (AACN) funded to further develop graduate competencies and coordinate 5 graduate level faculty development conferences

www.aacn.nche.edu/faculty/qsen/competencies.pdf

www.aacn.nche.edu/qsen/faculty-development

San Francisco Bay Area (SFBA) QSEN Faculty

Development Institute b

2009-2013

AACN implementation and evaluation of impact of incorporating the QSEN content into 22 schools of nursing in the San Francisco Bay area. Funding for a series of workshops for faculty and clinical leaders

Disch et al., 2013

apps.aacn.nche.edu/qsenec/GBMFoverview.html

Academic/Clinical Partnership and collaboration in QSEN

Lourdes University and ProMedica c

Innovative educational model for undergraduate education that includes a clinical integration partner to assist with the QSEN-based clinical education model

Didion, Kozy, Koffel, Oneail, 2013 www.rwjf.org/en/research-publications/find-rwjf-research/2013/04/academic-clinical-partnership-and-collaboration-in-quality-and-s.html

QSEN Institute

July 2012 to present

The Frances Payne Bolton School of Nursing at Case Western Reserve University continues to host the website and the National QSEN forum

QSEN.org

aRobert Wood Johnson Foundation funding
bGordon and Betty Moore Foundation funding
c Bureau of Health Professions, Health Resource and Services Administration, Department of Health and Human Services Nurse Education Practice, Quality and Retention

QSEN competencies have been used by national nursing organizations and are the central focus of the National Council of State Boards of Nursing (n.d.) Nurse Residency program, the foundational concepts in the Massachusetts Future of Nursing Framework ( Massachusetts Department of Higher Education, 2010), and the Ohio Hospital Association ( Ohio Organization of Nurse Executives, 2013). The QSEN competencies also have been incorporated into nursing textbooks such as the medical-surgical text by Ignatavicious and Workman ( 2013), and other books, such as Quality and Safety in Nursing: A Competency Approach to Improving Outcomes ( Sherwood & Barnsteiner, 2012), Second Generation QSEN, a special issue of the Nursing Clinics of North America ( Barnsteiner & Disch, 2012) and Quality and Safety for Transformational Leadership ( Amer, 2012).

Although QSEN competencies have spurred quality and safety in nursing education, it is now time to accelerate their use and impact. The full effect of the QSEN competencies to improve the quality and safety of care can only be realized when nurses apply them at both the individual and system levels of care. Many nurse educators report that the QSEN competencies are already integrated into their curriculum, but in our practice, we have noted that often this integration is at the individual level of care, rather than at the level of the system of care. The full effect of the QSEN competencies to improve the quality and safety of care can only be realized when nurses apply them at both the individual and system levels of care. Figure 1 provides a display of how the six QSEN domains are linked to optimal patient care through both vigilant individual care and vigilant systems of care. Traditionally, nurses have focused primarily on vigilant individual care; less attention has been given to assisting nurses to provide vigilant systems of care. We propose that in addition to the emphasis on teaching critical thinking skills ( Simpson & Courtney, 2002), nurses also need to be taught the knowledge and skills associated with systems thinking. In their day-to-day work, nurses' abilities to engage in better problem-solving, priority setting, delegation, interactions and collaborations, decision making, and action-taking are greatly influenced by their ability to view how any one component of their work system is related to other components and to the whole.

Figure 1 Source: Authors

Systems thinking is the ability to recognize, understand, and synthesize the interactions and interdependencies in a set of components designed for a specific purpose. This strategy includes the ability to recognize patterns and repetitions in interactions and an understanding of how actions and components can reinforce or counteract each other. These relationships and patterns occur at different dimensions: temporal, spatial, social, technical or cultural ( Oshry, 2007). Systems thinking links a person's environment to his/her behavior. In the delivery of nursing care, this involves the nurse's understanding and valuing how components of a complex healthcare system influence care of an individual patient. Systems thinking can be viewed as a continuum, ranging from the individual to the larger internal and external environmental components. Figure 2 shows examples of care approaches that represent increasing levels of systems thinking.

Figure 2 Source: Authors

Systems thinking links a person's environment to his/her behavior. How nurses view both themselves as nurses, and their work, is shaped by the structures and processes of the systems in which they work. Most nurses provide care in healthcare organizations that are characterized as complex, multilevel, and multifunctional. Greater knowledge and application of systems thinking skills by nurses have the potential to mitigate errors in practice, improve nurse priority setting and delegation, enhance problem solving and decision-making, improve timing and quality of interactions with other professionals and patients, and enhance workplace quality improvement initiatives. The ability to engage in systems thinking has been viewed as a key component in the successful delivery of safe and high quality care ( Bataldan & Mohr, 1997; Bataldan & Leach, 2009; Batalden & Stoltz, 1993; Senge, 2006). Systems thinking is required to redesign healthcare to improve the quality and safety of care.

The importance of systems thinking in quality improvement (QI) initiatives was identified in early literature on application of QI techniques to healthcare ( Batalden & Stoltz, 1993; Deeming & Appleby, 2000) and, more recently, was highlighted in reports from the Institute of Medicine ( IOM, 2003), the Accreditation Council for Graduate Medical Education ( Varkey, Karlapudi, Rose, Nelson, & Warner, 2009), and the article, "Quality and Safety Education for Nurses" ( Cronenwett, Sherwood & Barnsteiner, 2007). Given the hypothesized importance of systems thinking in the success of quality and safety in healthcare, it is probable that if nurses engage in better systems thinking, greater improvements in outcomes will be achieved. Knowledge and skills associated with systems thinking, however, are seldom addressed in basic or continuing nursing education. The next sections describe strategies for teaching and learning systems thinking, especially as related to QSEN competencies, and a newly developed tool for measurement of systems thinking.
Teaching and Learning Systems Thinking
Systems thinking is an essential skill for nurses. Yet, there has been little knowledge disseminated about how to assist nurses to better engage in this type of thought process, despite their key roles in planning, delivering, and improving patient care in complex organizations. To teach systems thinking it is important to enhance the learner's awareness of the interdependencies in people, processes, and services and to view problems as occurring as part of a chain of events of a larger system, rather than as independent events.

The clinical environment is an ideal place to teach systems thinking in undergraduate, graduate, and staff development education. During the clinical experience, the faculty preceptor can broaden the learner's problem identification from a focus on personal effort in a single situation to a focus on sequences of events with possible multiple causes for both individuals and populations. Table 2 provides examples of this continuum of systems thinking using the QSEN competencies. An example of a teaching technique for systems thinking is to have learners create grids such as those presented in Table 2 to expand their scope of thinking from the individual to the system level of care. Students might obtain outcome data from their unit and identify reasons for variation across time. Enhancing systems thinking skills also can be done by having learners complete an assessment of their unit or microsystem.

Assessment tools are available from the Clinical Microsystem ( 2013) Green Books for inpatient, emergency room, long-term care, and outpatient groups. These free workbooks from the Dartmouth Institute have been developed to help individuals assess the complexity of the system in which they work. Another approach to expand learners' scope of thinking to a systems level is to have them connect nursing skills and clinical issues to national quality and safety initiatives ( Armstrong & Barton, 2013). For example, urinary care is connected to the National Quality Forum ( 2012) Catheter Associated Urinary Tract Infection (CAUTI) prevention and the Joint Commission's ( 2013c) National Patient Safety Goal Number 7.

Nurses can also learn systems thinking by creating flowcharts or process diagrams that elicit the steps of a care process and the multitude of healthcare workers involved in that process. This mapping technique is one of the first steps of a quality improvement project. For example, to improve the care coordination of preparing hospitalized patients for discharge, teams of healthcare professionals could map steps in the course of a patient's stay leading to discharge. This exercise has been shown to increase knowledge about system factors and enhance awareness of the importance of interprofessional collaboration ( Brennen, Olds, Dolansky, Estrada, & Patrician, in press).

Another approach to teach systems thinking is to have learners conduct a root cause analysis ( Lambton & Mahlmeister, 2010; Tschannen & Aebersold, 2010). Root cause analysis (RCA) is a widely used technique to assist people to move beyond blame of an individual for errors made in the workplace to understanding the system factors that may have contributed to errors. Healthcare organizations routinely perform RCA after an event so that appropriate changes can be made in the system to prevent future errors. This technique could be used to understand system factors even when events "almost happen." Having nursing students participate in RCAs during their undergraduate education has been shown to be beneficial ( Dolansky, Druschel, Helba, & Courtney, 2013). For example, having students conduct an RCA for addressing a medication error may lend a new perspective to how system level factors interact with individual level factors in the creation of that error.

In the classroom setting, systems thinking also can be enhanced by using case studies. The book Set Phasers to Stun ( Casey, 1998) includes stories of design, technology, and human error that can be discussed in class. These stories identify the close connection between technology and humans. Another book, Systems Concepts in Action ( Williams & Hummelbrunner, 2011), is a practitioner's toolkit to teach the principles of systems thinking, such as system dynamics, outcome mapping, and social network analysis. Highly effective and very interactive, the game Friday Night in the ER ( 2009) guarantees learning and fun. The game is played by four people and simulates the challenge of managing a hospital during a 24-hour period. Each player is in charge of a unit. The demands of the game demonstrate that systems thinking is the key to success.

Lastly, teaching systems thinking requires guided reflection. Faculty need to assist learners to look for and recognize patterns in systems of care by standing back, reflecting on data, and considering the system as a whole. Too often in healthcare we make quick judgments that are based on limited information and preconceived ideas. Teaching nurses to step back and consider the dependencies and interconnectedness of system components will lead to a broader understanding of the healthcare system and the quality of care that results from that system.
Measurement of Systems Thinking
To improve systems thinking, we need to be able to measure it. A valid and reliable measure of systems thinking is now available. The Systems Thinking Scale (STS) is an instrument that measures healthcare professionals' systems thinking specifically related to system interdependencies. The 20-item STS has good reliability as demonstrated by a test-retest reliability assessment (N=36; correlation of .74) and internal consistency testing (N=342) using Cronbach's alpha (.89) ( Case Western Reserve University, 2013b).

...systems thinking can be taught and learned and an individual's level of systems thinking can be changed. Data from recent studies indicated that systems thinking can be taught and learned and an individual's level of systems thinking can be changed ( Abourmatar et al., 2012; Moore, Dolansky, Palmieri, Singh, & Alemi, 2010). Moore and colleagues tested three groups of healthcare professions students (n= 102) who received high, low, or no dose levels of systems thinking education. There were no differences in STS mean scores at pretest. At posttest, the high-dose systems thinking education group scored significantly higher on the STS than both the low and no-dose groups (p=.05 and .01, respectively). The STS is now publicly available for use and a website has been established to provide information on its use ( Case Western Reserve University, 2013a).

Almost 10 years have passed since the QSEN competencies were developed, and the field of quality and safety is rapidly advancing. The time has come to consider what new competencies should be added. We propose that the current QSEN competencies and knowledge, skills, and attitudes (KSAs) be reviewed and evaluated. Do the KSAs need to be updated, reclassified, or expanded? Should a systems perspective be made more prominent in the QSEN model? The QSEN competencies were developed to be a tool to promote better education for nurses in healthcare quality and safety. We need to update the QSEN competencies to be as useful as possible to prepare all nurses to ensure the highest level of care possible.

... a safe and high quality system of care requires that all healthcare professionals take responsibility to learn and apply skills associated with improving the wider system of care. Throughout QSEN history, reports from nurses and nurse faculty are that they already integrate the QSEN competencies into education and practice. However, we have observed that, despite the fact that contemporary approaches to quality and safety emphasize a systems view, much of the nursing education approach to teaching quality and safety (including application of the QSEN competencies) emphasizes personal effort at the individual level of care. Although we believe that personal expertise of the nurse with individual patients is necessary, a safe and high quality system of care requires that all healthcare professionals take responsibility to learn and apply skills associated with improving the wider system of care. We argue, therefore, that the QSEN competencies should be integrated into nursing curriculum and practice with a strong systems-perspective emphasis. Nurse faculty and staff development educators must critically evaluate the extent to which they apply QSEN competencies and at what levels.

Mary A. Dolansky, PhD, RN
Email: mad15@case.edu

Mary A. Dolansky is an Associate Professor at the Frances Payne Bolton School of Nursing, Case Western Reserve University in Cleveland, OH. Dr. Dolansky is Director of the QSEN Institute (Quality and Safety Education for Nurses) and Senior Fellow in the VA Quality Scholars program, mentoring pre- and post-doctoral students in quality and safety science. She has co-published two books on quality improvement, co-authored several book chapters and articles, and was guest editor on a special quality improvement education issue in the Journal of Quality Management in Health Care. She has taught the interdisciplinary course, "Continual Improvement in Health Care," at CWRU for the past 8 years and was chair of the quality and safety task force at the School of Nursing that integrated quality and safety into the undergraduate and graduate curriculum.

Shirley M. Moore, PhD, RN, FAAN
Email: smm8@case.edu

Shirley M. Moore is the Edward J. and Louise Mellon Professor of Nursing and Associate Dean for Research, Case Western Reserve University in Cleveland, OH. She is a past President of the Academy for Healthcare Improvement and is on the leadership team of the national Quality and Safety Education for Nurses (QSEN) project. She is currently leading the integration of nurse scholars in the VA Quality Scholars Program. She also is conducting NIH-funded studies testing a process improvement approach to health behavior change with patients.

Institute of Medicine. (2003). Health professions education: A bridge to quality. Washington, DC: National Academies Press.

© 2013 OJIN: The Online Journal of Issues in Nursing
Article published September 30, 2013

References

Abourmatoar, H.J., Thompson, D., Wu, A., Dawson, P., Colbert, J., Marsteller, J., & Pronovost, P. (2012). Development and evaluation of a 3-day patient safety curriculum to advance knowledge, self-efficacy and system thinking among medical students. BMJ Quality and Safety. 21(5). Doi.org/10.1136/bmj qs-2011-000463.

Amer, K. (2012). Quality and safety for transformational nursing. Upper Saddle River, NJ: Prentice Hall, Inc. Pearson Publishing.

Armstrong, G. & Barton, A. (2013). Fundamentally updating fundamentals. Journal of Professional Nursing, 29(2), 82-87. doi: 10.1016/j.profnurs.2012.12.006

Barnsteiner, J., Disch, J., Johnson, J., McGuinn, K., Chappell, K., & Swartwout, E. (2013). Diffusing QSEN competencies across schools of nursing: The AACN/RWJF Faculty Development Institutes. Journal of Professional Nursing, 29(2) 68-74. doi: 10.1016/j.profnurs.2012.12.003

Batalden, P.B., & Leach, D.C. (2009). Sharpening the focus on systems-based practice. Journal of Graduate Medical Education, 1, 1-3. doi: 10.4300/01.01.0001

Batalden, P.B., & Mohr, J.J. (1997). Building knowledge of health care as a system. Quality Management in Health Care, 5, 1-12.

Batalden, P.B. & Stoltz, P.K. (1993). A framework for the continual improvement of health care: Building and applying professional and improvement knowledge to test changes in daily work. Joint Commission Journal on Quality Improvement,19, 424-447.

Brennan, C., Olds, D., Patrician, P. A., Dolansky, M. A., & Estrada, C. (in press). Learning by doing: Observing an interprofessional process as an interprofessional team. Journal of Interprofessional Care.

Casey, S. M. (1998). Set phasers on stun: And other true tales of design, technology, and human errors (2nd ed.). Santa Barbara, CA: Aegean.

Clinical Microsystems.(2013). Materials overview. Retrieved from www.clinicalmicrosystem.org/materials/materials_overview/

Committee on the Quality of Health Care in America. (2001). Crossing the quality chasm: A new health system for the 21st century. Washington, DC: The National Academies Press.

Cronenwett, L., Sherwood, G., Barnsteiner, J., Disch, J., Johnson, J., Mitchell, P., & Warren, J. (2007). Quality and safety education for nurses. Nursing Outlook, 55, 122-131.

Deeming, C. & Appleby, J. (2000). Measuring performance. Green with envy?. Health Service Journal, 110, 22-25.

Didion, J., Kozy, M. A., Koffel, C., & Oneail, K. (2013). Academic/Clinical partnership and collaboration in Quality and Safety Education for Nurses education. Journal of Professional Nursing, 29(2) 88-94. doi: 10.1016/j.profjurs.2012.12.004

Disch, J., Barnsteiner, J., & McGuinn, K. (2013). Taking a "deep dive" on integrating QSEN content in San Francisco Bay Area schools of nursing. Journal of Professional Nursing, 29(2), 75-81. doi: 10.1016/j.profnurs.2012.12.007

Dolansky, M.A., Helba, M., Druschel, K., & Courtney, K. (2012). Nursing student medication errors: A root cause analysis to develop a fair and just culture. Journal of Professional Nursing, 29(2), 102-108. doi: 10.1016/j.profnurs.2012.12.010

Estrada, C.A., Dolansky, M.A., Singh, M.K., Oliver, B.J., Callaway-Lane, C., Splaine, M., & Patrician, P.A. (2012). Mastering improvement science skills in the new era of quality and safety: The Veterans Affairs National Quality Scholars Program. Journal of Evaluation in Clinical Practice, 18, 508-514. doi: 10.1111/j.1365-2753.2011.1816.x.Epub 2012 Feb 5

Friday night at the ER. (2009). Retrieved from: www.fridaynightattheer.com/

Ignatavicious, D. D., & Workman, L. M. (2013). Medical- Surgical nursing patient-centered collaborative care. (7 th Edition). Philadelphia: PA: Elsevier.

Institute of Medicine (2000). To err is human: Building a safer health system. Washington, DC: The National Academies Press.

Institute of Medicine. (2003). Health professions education: A bridge to quality. Washington, DC: National Academies Press.

Lambton, J., & Mahlmeister, L. (2010). Conducting root cause analysis with nursing students: Best practice in nursing education. Journal of Nursing Education, 49, 444-448. doi: 10.3928/01484834-20100430-03

Massachusetts Department of Higher Education. (2010). Creativity and connections: Building the framework for the future of nursing education and practice. Retrieved from www.mass.edu/currentinit/documents/NursingCoreCompetencies.pdf

Moore, S.M., Dolansky, M.A., Palmieri, P., Singh, M., & Alemi, F. (2010). Developing a measure of system thinking: A key component in the advancement of the science of QI. International Health Forum on Quality and Safety in Healthcare, Nice, France: Acropolis.

Oshry, B. (2007). Seeing systems: Unlocking the mysteries of organizational life. San Fransico: Barett-Koehler.

Patrician, P. A., Dolansky, M. A., Pair, V., Bates, M., Moore, S.M., Splaine, M., & Gilman, S. C. (2012). The Veterans Affairs National Quality Scholars (VAQS) Program: A model for interprofessional education in quality and safety. Journal of Nursing Care Quality,28(1), 24-32. doi:

Patrician, P. A., Dolansky, M. A., Estrada, C., Brennan, C., Miltner, R., Newsom, J., ... Moore, S. M. (2012). Interprofessional education in action: The VA Quality Scholars Fellowship program. Nursing Clinics of North America, 47, 347-354.

Senge, P.M. (2006). The fifth discipline: The art and practice of the learning organizations. New York: Doubleday.

Sherwood, G. & Barnsteiner, J. (2012). Quality and safety in nursing: A competency approach to improving outcomes. Hoboken, NJ: John Wiley & Sons.

Simpson, E. & Courtney, M. (2002). Critical thinking in nursing education: Literature review. International Journal of Nursing Practice, 8, 89-98.

Tschannen, D., & Aebersold, M. (2010). Improving student critical thinking skills through a root cause analysis pilot project. Journal of Nursing Education, 49, 478-485. doi: 10.3928-01484834-20100524-02

Varkey, P., Karlapudi, S., Rose, S., Nelson, R., & Warner, M. (2009). A systems approach for implementing practice-based learning and improvement and systems-based practice in graduate medical education. Academic Medicine, 84, 335-339. doi: 10.1097/ACM.0b013e31819731fb

Williams, B. & Hummelbrunner, R. (2011). Systems concepts in action: A practitioner's toolkit. Stanford, CA: Stanford University Press.

September 30, 2013

DOI: 10.3912/OJIN.Vol18No03Man01

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key words: qsen

# data[].attributes.attachment_link.title
Quality and Safety Education for Nurses (QSEN): The Key is Systems Thinking

# data[].attributes.attachment_link.description
Over a decade has passed since the Institute of Medicine’s reports on the need to improve the American healthcare system, and yet only slight improvement in quality and safety has been reported. The Quality and Safety Education for Nurses (QSEN) initiative was developed to integrate quality and safety competencies into nursing education.

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A year ago, more than one hundred nursing leaders representing seventy organizations met to define a unified, collective vision for the future, one that bridges the quality chasm using information technology. On November 1, 2006, the Technology Informatics Guiding Education Reform (TIGER) Summit participants defined action steps that the nursing profession can take to better prepare our nursing workforce to use technology and informatics with the goal of improving the delivery of patient care. Today a practicing nurse's portfolio of necessary skills includes computer literacy, information literacy, and informatics skills. The TIGER Initiative is focused on helping the nursing profession to adopt informatics tools, principles, theories, and practices that make healthcare safer and more effective, efficient, patient-centered, and equitable for all stakeholders. The executive summary report and recommendations from the TIGER Summit are available online at www.tigersummit.com.

While the TIGER Initiative started out as a grass-roots effort to engage all stakeholders who are committed to a common "vision" of an ideal electronic health record (EHR) for nursing practice, today more than 120 diverse organizations are joining this effort. While the original 70 organizations that attended the Summit work towards completing their strategic action plans, additional nursing professionals have joined the TIGER Initiative to collaborate on nine important topics. These nine topics include:

1. Standards and Interoperability

2. Healthcare Information Technology National Agenda/Policy

3. Informatics Competencies

4. Education and Faculty Development

5. Staff Development/Continuing Education

6. Usability/Clinical Application Design

7. Virtual Demonstration Center

8. Leadership Development

9. Consumer Empowerment/Personal Health Record

As of this writing, 280 nurses have volunteered their expertise to work on the nine collaborative teams.

Each collaborative team is lead by an industry expert on the specific topic. The goal of each collaborative team is to share their findings and recommendations with all practicing nurses and nursing students through targeted outreach activities. Examples of the materials that the collaborative teams are developing include presentations, white papers, webinars, and articles for publication in specialty journals and textbooks.

The diverse background of the nurses participating in this initiative is unparalleled. Participants represent the interests of nursing professional organizations, nursing informatics organizations, government agencies, information technology vendors, industry partners, non-profit organizations, healthcare provider organizations, and academic institutions. Together these teams are reaching out to gather case studies, examples of innovative practice and education models, publications, and ongoing research programs that represent a diversity of nursing professionals both in educational preparation and practice experiences. These teams are concurrently developing mechanisms to enhance collaboration between professional organizations, government and policy-making agencies, and industry partners to specifically address the educational needs of the nursing workforce so they can better use technology to improve the delivery of patient care.

Why are so many nursing leaders on board with the TIGER Initiative? There has never been a better opportunity for nursing to lead the effort to reform our healthcare delivery system using technology as the enabler and the bridge to do so. Technology is also the enabler facilitating the collaborative effort of these teams. These teams are meeting via web-enabled meetings, sharing documents via wikis and SharePoint®-enabled websites, and conducting audio conference calls that are recorded if not all team members can attend the meetings. The outcomes of each of the collaborative teams will be available for distribution through a variety of electronic and multi-media delivery mechanisms. The collaborative teams have already established their topic-specific goals and are organizing smaller work groups to accelerate the achievement of their outcomes. It is not too late to roll up your sleeves and joint this effort! Instructions are provided after each collaborative team description. If you have feedback or questions related to the TIGER Initiative and/or the ways in which you can become an active proponent of this program, please contact the Program Director, Donna DuLong, at the email address below.

Author

Donna DuLong, RN, BSN
Email: donna@tigersummit.com

Donna DuLong is the Program Director of the Technology Informatics Guiding Education Reform (TIGER) Initiative. Donna has worked in the healthcare information technology industry for 17 years, focusing primarily on nursing-based, information-technology solutions related to patient safety, medication management, and evidence-based knowledge. At the TIGER Initiative, Donna coordinates the efforts of the nine collaborative teams and the participating organizations towards achieving the three-year action plan that was defined at the TIGER Summit. Donna has held several executive management positions in the healthcare information technology market with responsibilities for business development, product development and management, marketing, compliance, implementation, and customer support. Donna has worked with numerous Health Information Systems vendors including Thomson Healthcare, HBOC/McKesson, Pyxis Corporation, Multum Information Services, Cerner, Bridge Medical, and Medvantx. Donna holds a baccalaureate degree in nursing from Michigan State University. She has completed graduate work in nursing and business at the University of San Diego, and has worked for seven years in critical care nursing.

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Informatics: The Tiger Project | OJIN: The Online Journal of Issues in Nursing

# data[].attributes.attachment_link.description
A year ago, more than one hundred nursing leaders representing seventy organizations met to define a unified, collective vision for the future, one that bridges the quality chasm using information technology. On November 1, 2006, the Technology Informatics Guiding Education Reform (TIGER) Summit par…