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Ethics is a key factor to healthcare. With the pandemic there have been many situations where ethics has been questioned. The articles discusses the limitations placed on routine healthcare when the pandemic first began. This was done to immediately decrease the potential risk of exposure to the public although was not substantial long term.

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Updated May 1, 2020

In the midst of a pandemic, attention shifts toward containing the spread of disease and treating those who become seriously ill and away from the health care needs of other patients. In the short term, it may be essential to restrict the delivery of "routine" care in the interest of reducing immediate risks to public health, but that strategy is not medically or ethically sustainable in the longer term. The health of the community ultimately requires that a broad range of care be available beyond crisis-driven patient needs.

The AMA Code of Medical Ethics offers insight into thinking about what is ethically required to safely make this broader range of health care services available. The challenge is to prioritize among services and to identify conditions that should be in place to enable the delivery of care while minimizing the ongoing risk of exposure.

Principles VIII and IX of the AMA Principles of Medical Ethics set out physicians' commitments to individual patient welfare and to supporting access to care for all, respectively. Opinion 8.11, "Health promotion and preventive care," of the AMA Code of Medical Ethics defines the professional responsibility "to prevent disease and promote health and well-being for their patients and the community." Opinion 11.1.2, "Physician stewardship of health care resources," charges physicians to be "prudent stewards of the shared societal resources with which they are entrusted. Opinion 1.1.6, "Quality," meanwhile, delineates the "obligation to ensure that the care patients receive is safe, effective, patient centered, timely, efficient and equitable."

Finally, Opinion 11.1.3, "Allocating limited health care resources," suggests criteria for determining which services have the strongest claims to priority. Viz., services that address urgent medical needs, offer likelihood of benefit and promote change in quality of life, including services intended to prevent disease and promote health in keeping with Opinion 8.11, such as primary care or well-child visits or routine vaccinations.

Depending on the specific public health threat, some preventive services-for example, nonemergency dental care in the context of a severe respiratory infectious disease-may pose undue risk to both patients and health care professionals. Other services, such as those that reflect patient preferences rather than clinically defined need, have a lesser claim to priority. For all services, informed consent requires that patients be made aware of the "burdens, risks, and expected benefits of all options, including forgoing treatment," in keeping with Opinion 2.1.1, "Informed consent."

Taken as a whole, this guidance suggests further conditions under which it is ethically acceptable to offer noncritical health care services in the context of pandemic disease:

Ability to ensure a safe environment, including ability to quickly and reliably test all individuals who will be involved in patient care to ensure that they are free of disease or immune

Ability to provide care without making a claim on health care resources that are otherwise required to meet other patients' urgent medical needs, including medical equipment, personnel and laboratory or other services

Ability to provide appropriate follow up, including monitoring and testing to ensure the patient remains disease free, or to provide immediate access to appropriate care if infection is suspected

Additional ethics guidance in a pandemic

The AMA offers an overview of foundational guidance regarding medical ethics for health care professionals and institutions responding to the COVID-19 pandemic.

Essential Tools & Resources

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Prioritizing the rest of health care in a public health crisis

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Updated May 1, 2020 In the midst of a pandemic, attention shifts toward containing the spread of disease and treating those who become seriously ill and away from the health care needs of other patients.

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The vaccine mandate has been a popular topic throughout this pandemic. There are a lot of different opinions in regards to the ethics behind the mandate. COVID-19 has changed the world and changed our healthcare. As the pandemic persists I feel that ethical considerations will become even more challenging.

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Society of Critical Care Medicine (SCCM)

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Mandate to vaccinate or nudge if there is no budge? COVID-19 vaccines remain a key weapon in the fight against the deadliest modern-day pandemic the world has seen. In this article, we summarize key facts and ethical considerations for healthcare organizations when considering a COVID-19 vaccine mandate for U.S. healthcare workers (HCW).

Federal and State Regulations

Since the advent of vaccines, mandates have been used to achieve herd immunity both in the general population and amongst HCW. 1,2 Current federal regulations do not include any mandatory vaccination programs; rather, vaccine mandates are generally within the purview of state and local governments. State vaccine requirements for HCW vary widely. Some states have laws requiring HCW to be vaccinated against diseases such as measles, mumps, and rubella, with opt-out provisions for when the vaccine is medically contraindicated or against the person's religious or philosophical beliefs. ³ Mandatory requirements for influenza vaccination among HCW exist only in three states: Alabama, Colorado, and New Hampshire. ⁴ At present, no state has mandated COVID-19 vaccination for HCW.

Healthcare Organizations

Many professional organizations endorse the proposition that HCW have a professional and ethical responsibility to help prevent the spread of infectious pathogens. The Society for Healthcare Epidemiology of America recommends annual influenza vaccination as a condition of employment and professional privileges for HCW. ⁵ The American College of Physicians policy suggests that influenza vaccination be mandated for all HCW, unless there is a medical or religious objection. ⁶

In 2004, Virginia Mason Medical Center in Seattle, Washington, became the first healthcare system in the United States to make influenza vaccination a condition of employment. Within three years, the hospital reported 98% staff coverage. The remaining 2% of the staff who refused for medical or religious reasons were required to wear surgical masks when in the hospital during the flu season. ⁷ This influenza vaccine mandate is now followed by more than 400 healthcare organizations. ⁸

Currently, all COVID-19 vaccines are being used under Emergency Use Authorization (EUA) granted by the U.S. Food and Drug Administration (FDA). An EUA requires fewer safety and efficacy data than full Biologics License Application (BLA) approval, which may result in less trust in the vaccines because administration could be considered "ongoing medical research." ⁹ Under an EUA, vaccine recipients must be informed of "the option to accept or refuse administration of the product." 10,11 It is unclear whether COVID-19 vaccines can be legally mandated while operating under an EUA prior to official FDA approval, as courts have not yet ruled on this issue. Once COVID-19 vaccines receive BLA approval, healthcare facilities may implement mandates for COVID-19 vaccination more broadly (as with influenza vaccination).

The U.S. Equal Employment Opportunity Commission (EEOC) states that employers should follow COVID-19 vaccine guidelines specified by the Centers for Disease Control and Prevention (CDC) and state or local public health authorities. Employees may also be subject to certain legally protected exceptions for disabilities under the Americans with Disabilities Act and for sincerely held religious beliefs under Title VII of the Civil Rights Act. ¹²

According to CDC recommendations, COVID-19 vaccines are not mandated under EUAs; however, they allow for local mandates: "whether a state, local government or employer may require or mandate COVID-19 vaccination is a matter of state or other applicable law." ¹³

As of May 2021, Italy is the only country that has required HCW to be vaccinated against COVID-19. ¹⁴ Most healthcare organizations in the United States have not yet broadly mandated COVID-19 vaccination. Exceptions include Indiana University Health, the New York-Presbyterian healthcare system and Houston Methodist Hospital. The latter hospital was sued over this mandate, but the lawsuit was subsequently dismissed by a US District judge. ¹⁵

The risk for transmission of infections is high among vulnerable persons in healthcare settings: patients, HCW, and third parties with whom they may come in contact. The goal of a vaccine mandate would be community protection or 'herd immunity.'

The root of the ethical dilemma behind a vaccine mandate is the conflict between public health ethics and the right to individual liberty and autonomy. Utilitarian arguments for vaccine mandates claim that higher immunization rates result in greater good for all (lives saved, morbidity avoided). According to this view, mandating universal vaccination is morally justified because of the consequences: community protection and reduction in virus transmission, resulting in lower rates of infections, hospitalizations, and deaths. Critics of utilitarianism contend that it is limited to "value monism" or that utility is the only fundamental "super-value" and that other values (eg, individual liberty) do not have the same moral value. ¹⁶ Conversely, appealing to self-interest and individual liberty via lotteries and offering payment for vaccination ¹⁷ (inducement) could erode the sense of solidarity with public health and the willingness to take risks for the common good.

Advantages of a Mandate

Beneficence, nonmaleficence: Healthcare institutions have a legal and ethical obligation to ensure a safe environment for patients, HCW, and visitors. Vaccination would reduce viral transmission and thereby promote health, enhance patient safety, and provide a sense of security. HCW have an ethical/moral obligation to provide care for patients and to do no harm; vaccination would limit the spread of COVID-19 infection.

Justice: Vaccines prevent hospitalizations and may reduce HCW shortages and protect health system capacity. This would enhance distributive justice and enable healthcare organizations to fulfill their obligations to the sick and vulnerable.

Disadvantages of a Mandate

Administration of a vaccine requires verbal, informed consent. Mandates eliminate the right to informed consent or refusal of treatment. A mandate with no exemptions would infringe upon personal autonomy and likely has implications for privacy and confidentiality.

If basic requirements for HCW, such as personal protective equipment, are not met, a vaccine mandate may be counterproductive, negatively affecting staff morale and violating trust when stress levels among HCW are already high.

Issues to Consider

Availability of vaccines: A vaccine mandate requires unrestricted access to vaccines; therefore, considerations for a mandate would apply only to areas where COVID-19 vaccines are freely available and supply is unlimited. Of note, the World Health Organization (WHO) does not presently support mandates for COVID-19 vaccination, instead favoring a focus on educational campaigns and universal availability of vaccines.¹⁸

"Dynamic justification": The relative weightiness of reasons for mandating vaccines varies under different circumstances and epidemiological conditions (eg, background vaccination rate, infectivity rate, and rate of hospitalizations).¹⁶ Evidence for reduced viral transmission following COVID-19 vaccination is emerging. The incidence of new infections and hospitalizations in the United States has been steadily decreasing as more adults, adolescents, and now children have been vaccinated.¹⁹

Availability of other options: If the use of personal protective equipment, physical distancing, and physical barriers prevent the spread of COVID-19 and these options are available to HCW, mandating vaccinations may not be ethically warranted. Additionally, imposing a mandate may not be necessary for those who do not interact physically with patients or can work from home. When other alternatives exist, coercion²⁰ in the form of a mandate, specified threats, or significant negative consequences for refusal may not be justified.

"Mandatory" Vaccines and Exemptions

Contemporary forms of "mandatory" vaccination compel vaccination by direct or indirect threats of restrictions in cases of noncompliance. ²¹ Adverse action or termination of employment as a result of vaccine refusal could be considered coercion, ²⁰ and the employer could be subject to legal action.

According to the U.S. EEOC and CDC guidance documents, vaccine mandates are subject to medical and religious-based exemptions. 12,13 Providing exemptions would reduce HCW concerns and help them feel more empowered in their vaccination decision.

Facilities that opt for strict vaccine mandates should specify exemptions and offer alternatives to employment termination (ie, teleworking when feasible, staying home without pay).

Alternatives to Implementing a Mandate

Alternatives include education campaigns, inducement in the form of incentives and "nudge strategies." Some states have offered huge monetary incentives to encourage people to get vaccinated. ¹⁷ No EEOC guidance specifies which vaccination incentives can be offered by employers. Thus, these kinds of inducement may be legally problematic, given the potential for "undue inducement" and the different effects they have on persons who may see increased benefit from monetary compensation. ²²

Nudges change behavior by means of 'choice architecture' by organizing the context in which people make decisions. ²³ Tax breaks or enhanced benefits packages and health insurance premiums for HCW who get vaccinated are nudge strategies that can be considered by healthcare systems. ²³

Most people would agree that healthcare organizations are obligated to routinely offer COVID-19 vaccines to all HCW. Considerations for a mandate (even under EUA) would depend upon the local context, and the decision-making process should ideally involve discussions with state and local public health authorities. Deliberations and ethical analyses by institutional policy makers (including organizational ethics experts) should be transparent. Given existing information about COVID-19 vaccines and current EEOC/CDC guidelines, the question faced by healthcare organizations once the vaccines receive full FDA approval is not so much whether vaccination can be mandated legally, rather whether it is ethically justifiable to do so.

References

1. McClure CC, Cataldi JR, O'Leary ST. Vaccine hesitancy: where we are and where we are going. Clin Ther. 2017;39(8):1550-1562.

2. Haviari S, Bénet T, Saadatian-Elahi M, et al. Vaccination of healthcare workers: a review. Hum Vaccin Immunother. 2015;11(11):2522-2537.

3. Lindley MC, Horlick GA, Shefer AM, Shaw FE, Gorji M. Assessing state immunization requirements for healthcare workers and patients. Am J Prev Med. 2007;32(6):459-465.

4. Centers for Disease Control and Prevention. State immunization laws for healthcare workers and patients. Accessed 5/28/2021 https://www2a.cdc.gov/vaccines/statevaccsApp/AdministrationbyVaccine.asp?Vaccinetmp=Influenza#221

5. Talbot TR, Babcock H, Caplan AL, et al. Revised SHEA position paper: influenza vaccination of health care personnel. Infect Control Hosp Epidemiol. 2010;31(10):987-995.

6. American College of Physicians. Patient safety and health care provider immunization. Accessed 5/28/2021 https://www.acponline.org/acp_policy/policies/healthcare_provider_immunization_2013.pdf

7. Rakita RM, Hagar BA, Crome P, Lammert JK. Mandatory influenza vaccination of health care workers: a 5-year study. Infect Control Hosp Epidemiol. 2010;31(9):881-888.

8. Dubov A, Pjung C. Nudges or mandates? The ethics of mandatory flu vaccination. Vaccine. 2015;33:2530-2535

9. Gostin LO, Salmon DA, Larson HJ. Mandating COVID-19 vaccines. JAMA. 2021;325(6):532-539.

10. Centers for Disease Control and Prevention. COVID-19 vaccine Emergency Use Authorization fact sheets for recipients and caregivers. Accessed 5/28/2021 https://www.cdc.gov/vaccines/covid-19/eua/index.html#:~:text=For%20each%20COVID%2D19%20vaccine,an%20informed%20decision%20about%20vaccination.

11. U.S. Food and Drug Administration. Emergency Use Authorization for vaccines explained. Accessed 5/28/2021 https://www.fda.gov/vaccines-blood-biologics/vaccines/emergency-use-authorization-vaccines-explained

12. U.S. Equal Employment Opportunity Commission. Pandemic preparedness in the workplace and the Americans with Disabilities Act. Accessed 5/28/2021 https://www.eeoc.gov/laws/guidance/pandemic-preparedness-workplace-and-americans-disabilities-act

13. Centers for Disease Control and Prevention. Workplace vaccination program. Last updated March 25, 2021. Accessed 5/28/2021 https://www.cdc.gov/coronavirus/2019-ncov/vaccines/recommendations/essentialworker/workplace-vaccination-program.html#anchor_1615585395585

14. Paterlini M. Covid-19: Italy makes vaccination mandatory for healthcare workers. BMJ. 2021;373:n905. doi:10.1136/bmj.n905

15. 'Mandatory Vaccination Policy Lawsuit Update: Nurses Take a Shot Against Hospital, But Judge Jabs Back.' The National Law Review Vol XI, Number 167 . June 16, 2021. Accessed 6/16/2021. https://www.natlawreview.com/article/mandatory-vaccination-policy-lawsuit-update-nurses-take-shot-against-hospital-judge

16. Navin MC, Attwell K. Vaccine mandates, value pluralism, and policy diversity. Bioethics. 2019;33:1042-1049.

17. Groppe M. Federal government gives OK for states to offer lotteries, cash incentives for vaccinations. USA TODAY. May 25, 2021. Updated May 26, 2021. Accessed 5/28/2021 https://www.usatoday.com/story/news/politics/2021/05/25/covid-vaccine-feds-ok-lotteries-ca. sh-incentives-vaccinations/7436394002/

18. World Health Organization. COVID-19 and mandatory vaccination: ethical considerations and caveats. Published April 13, 2021. Accessed 5/28/2021 https://www.who.int/publications/i/item/WHO-2019-nCoV-Policy-brief-Mandatory-vaccination-2021.1

19. Moghadas SM, Vilches TN, Zhang K, et al. The impact of vaccination on COVID-19 outbreaks in the United States. Clin Infect Dis. 2021. Jan 30:ciab079. Published online ahead of print. doi: 10.1093/cid/ciab079.

20. Department of Health, Education, and Welfare; National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research. The Belmont Report. Ethical principles and guidelines for the protection of human subjects of research. J Am Coll Dent. 2014 Summer;81(3):4-13.

21. Gravagna K, Becker A, Valeris-Chacin R, et al. Global assessment of national mandatory vaccination policies and consequences of non-compliance. Vaccine. 2020;38:7865-7873.

22. Emanuel EJ. Undue inducements: nonsense on stilts? Am J Bioeth. 2005;5(5):9-13. Published correction appears in: Am J Bioeth. 2006;6(1):54.

23. Dubov A, Phung C. Nudges or mandates? The ethics of mandatory flu vaccination. Vaccine. 2015;33:2530-2535.

This educational activity was funded in part by a cooperative agreement with the Centers for Disease Control and Prevention (grant number 1 NU50CK000566-01-00). The Centers for Disease Control and Prevention is an agency within the Department of Health and Human Services (HHS). Its contents do not necessarily represent the policy of CDC or HHS, and should not be considered an endorsement by the Federal Government.

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SCCM | Ethical Considerations for a COVID-19 Vaccine Mandate

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Mandate to vaccinate or nudge if there is no budge? COVID-19 vaccines remain a key weapon in the fight against the deadliest modern-day pandemic the world has seen. In this article, we summarize key facts and ethical considerations for healthcare organizations when considering a COVID-19 vaccine mandate for U.S.

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This is a short and simple article that dates data visualization back to Florence Nightingale. She utilized a pie chart at the time to reflect the data she obtained. She utilized the pie chart in different sections to reflect deaths per month and cause of death. This was how she identified that more soldiers were dying from preventable causes than from actual wounds. This is amazing to see how it all began with her.

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Siobhan O'Connor PhD ,

Corresponding Author

Siobhan O'Connor PhD

Lecturer

School of Health in Social Science, The University of Edinburgh, Edinburgh, UK
Correspondence
Siobhan O'Connor, School of Health in Social Science, The University of Edinburgh, Doorway 6 Old Medical Quad, Teviot Place, Edinburgh EH8 9AG, UK.

Email: siobhan.oconnor@ed.ac.uk
Search for more papers by this author

First published:

Health care is becoming more data driven than ever before. The rise of evidence-based practice requires information to be used quickly and effectively to improve patient outcomes. This is a challenge for clinicians, health service managers and researchers who routinely obtain and process data from an array of sources. Insights gained can be highly informative in enhancing planning and policy in health care. Advances in computing have enabled vast amounts of digital health data to be collected and analysed in developed nations and emerging economies (Kuan et al., 2019). From electronic medical records that hold clinical and administrative datasets, to telehealth systems that monitor people with long-term conditions, and health apps and wearable devices that track diet and exercise among other behaviours, a wealth of digital health data is now available. These can also be combined with data from transport, agriculture, education and other sectors to improve human health.

One difficulty with understanding complex datasets is communicating the results of analysis in coherent ways to a range of stakeholders, necessary to aid individual and organizational decision-making. A 2012 Institute of Medicine report highlighted that: ' Designing information presentation to minimize safety risks with minimum effort is still an unsolved problem. Information visualization is not as advanced in parts of clinical medicine as compared with other scientific disciplines' (Institute of Medicine, 2012, pg. 172). A new science, that of data visualization, has evolved which examines how to create visual representations of complex data to help people quickly assimilate large amounts of information (Caban & Gotz, 2015). This can take numerous forms such as visualizing genomic and clinical data to track infections in hospitals (Smith et al., 2017) or displaying personal health data from self-monitoring technologies via graphs and other infographics (Almalki, Gray, & Sanchez, 2015).

The origins of data visualization in healthcare can be traced as far back as the 1800s, a primary example being Florence Nightingale's ground breaking work examining the causes of hospital mortality in the Crimean War (1853-1856). Based at the Barrack Hospital in Scutari, she witnessed first-hand the overcrowded and poor conditions wounded British soldiers were treated in. As a nurse, Florence worked tirelessly to improve hygiene, cleanliness, and the organization of equipment, staff and other hospital resources. A dramatic reduction in mortality after improvements were made by the Sanitary Commission, greatly influenced her professional perspective. She became committed to hospital and public health reform and after the war acquired a wealth of data on injured soldiers who contracted infections and died in field hospitals (Bostridge, 2015). Florence realized that bringing this information to life in a simple and clear way was essential for it to be quickly understood and action taken to improve practice.

She employed her statistical expertise, with help from William Farr a well-known epidemiologist and medical statistician (Farr, 1864), to create a form of a pie chart known as a polar area chart, a novel approach to presenting data at that time. This scientific collaboration enabled Florence to plot the counts of death by month, with the area of each sector representing the number of deaths which was further subdivided using colour based on the cause of mortality. In her seminal diagram, it is clear that more soldiers died from preventable diseases which are shaded in blue, than from wounds represented in red and other causes shown in black (Figure 1). This figure is commonly referred to as a coxcomb or 'rose' diagram and enables multiple comparisons of data to be seen in a single illustration, a useful visual aid for those unfamiliar with statistical data and reports (Magnello, 2012).

Florence used this and other statistical graphics when reporting to the British parliament on the health of the army. Rooted in the belief that improving sanitary conditions and having adequate ventilation would reduce deaths from infections such as typhus, cholera and dysentery, she continued to use data visualization to support the case for change. In 1858, Florence published a report on mortality of the British army that contained numerous forms of charts and tables explaining the health of soldiers at home and at military hospitals overseas (Royal Commission appointed to enquire into the Regulations affecting the Sanitary State of the Army, 1858). Her passion for statistics and representing the health of populations in a visual way helped persuade government officials to improve care for British armed forces active in India by introducing better drainage, clean water and ventilation, saving the lives of thousands of soldiers (Report of the Royal Commission, 1863). In recognition of her contribution to the field of statistics, Florence was elected to the Royal Statistical Society in 1859 as the first ever female member. Using her notoriety, political connections and statistical prowess, Florence was a powerful voice for change and continued to lobby ministers in the British government throughout her life to bring about improvements in public health.

Since then, Florence Nightingale's work and that of others has paved the way for modern day approaches to visualizing data in healthcare. As digital data becomes the cornerstone of contemporary evidence-based practice, her work and its relevance should not go unnoticed. The increasing popularity of data visualization techniques has resulted in a systematic review of the literature, calls for evaluation frameworks that can be used in health care (Wu et al., 2019) and subsequent efforts to consolidate best practice (Khasnabish et al., 2019). Nightingale's pioneering work helped to begin a new trend, the implications of which can still be felt today as governments, organizations and citizens use digital data and visual techniques to affect change in healthcare and across every area of society in both public and private spheres. Furthermore, interdisciplinary initiatives are generating ever more sophisticated data visualizations and incorporating machine learning to tackle some of the challenges faced by the volume, variety, quality and connectedness of health data (Gotz & Borland, 2016). This means the value and impact of data visualization in healthcare will no doubt continue. As the 200th anniversary of the birth of Florence Nightingale approaches on 12 th May 2020, which coincides with the World Health Organization designated Year of the Nurse and the Midwife, we should reflect on and celebrate the significant and wide-reaching impact she has had. Florence is an enduring role model not only for nurses working globally on the front lines of delivering patient care but also for researchers who collect, analyse and disseminate new knowledge via digital techniques such as data visualization to bring about improvements in human health.

ACKNOWLEDGEMENTS

Sincere thanks to the Florence Nightingale Foundation Emerging Leaders Leadership Scholarship programme that inspired this commentary.

CONFLICT OF INTEREST

None.

Health care is becoming more data driven than ever before. The rise of evidence-based practice requires information to be used quickly and effectively to improve patient outcomes. This is a challenge for clinicians, health service managers and researchers who routinely obtain and process data from an array of sources. Insights gained can be highly informative in enhancing planning and policy in health care. Advances in computing have enabled vast amounts of digital health data to be collected and analysed in developed nations and emerging economies (Kuan et al., 2019). From electronic medical records that hold clinical and administrative datasets, to telehealth systems that monitor people with long-term conditions, and health apps and wearable devices that track diet and exercise among other behaviours, a wealth of digital health data is now available. These can also be combined with data from transport, agriculture, education and other sectors to improve human health.

One difficulty with understanding complex datasets is communicating the results of analysis in coherent ways to a range of stakeholders, necessary to aid individual and organizational decision-making. A 2012 Institute of Medicine report highlighted that: ' Designing information presentation to minimize safety risks with minimum effort is still an unsolved problem. Information visualization is not as advanced in parts of clinical medicine as compared with other scientific disciplines' (Institute of Medicine, 2012, pg. 172). A new science, that of data visualization, has evolved which examines how to create visual representations of complex data to help people quickly assimilate large amounts of information (Caban & Gotz, 2015). This can take numerous forms such as visualizing genomic and clinical data to track infections in hospitals (Smith et al., 2017) or displaying personal health data from self-monitoring technologies via graphs and other infographics (Almalki, Gray, & Sanchez, 2015).

The origins of data visualization in healthcare can be traced as far back as the 1800s, a primary example being Florence Nightingale's ground breaking work examining the causes of hospital mortality in the Crimean War (1853-1856). Based at the Barrack Hospital in Scutari, she witnessed first-hand the overcrowded and poor conditions wounded British soldiers were treated in. As a nurse, Florence worked tirelessly to improve hygiene, cleanliness, and the organization of equipment, staff and other hospital resources. A dramatic reduction in mortality after improvements were made by the Sanitary Commission, greatly influenced her professional perspective. She became committed to hospital and public health reform and after the war acquired a wealth of data on injured soldiers who contracted infections and died in field hospitals (Bostridge, 2015). Florence realized that bringing this information to life in a simple and clear way was essential for it to be quickly understood and action taken to improve practice.

She employed her statistical expertise, with help from William Farr a well-known epidemiologist and medical statistician (Farr, 1864), to create a form of a pie chart known as a polar area chart, a novel approach to presenting data at that time. This scientific collaboration enabled Florence to plot the counts of death by month, with the area of each sector representing the number of deaths which was further subdivided using colour based on the cause of mortality. In her seminal diagram, it is clear that more soldiers died from preventable diseases which are shaded in blue, than from wounds represented in red and other causes shown in black (Figure 1). This figure is commonly referred to as a coxcomb or 'rose' diagram and enables multiple comparisons of data to be seen in a single illustration, a useful visual aid for those unfamiliar with statistical data and reports (Magnello, 2012).

Florence used this and other statistical graphics when reporting to the British parliament on the health of the army. Rooted in the belief that improving sanitary conditions and having adequate ventilation would reduce deaths from infections such as typhus, cholera and dysentery, she continued to use data visualization to support the case for change. In 1858, Florence published a report on mortality of the British army that contained numerous forms of charts and tables explaining the health of soldiers at home and at military hospitals overseas (Royal Commission appointed to enquire into the Regulations affecting the Sanitary State of the Army, 1858). Her passion for statistics and representing the health of populations in a visual way helped persuade government officials to improve care for British armed forces active in India by introducing better drainage, clean water and ventilation, saving the lives of thousands of soldiers (Report of the Royal Commission, 1863). In recognition of her contribution to the field of statistics, Florence was elected to the Royal Statistical Society in 1859 as the first ever female member. Using her notoriety, political connections and statistical prowess, Florence was a powerful voice for change and continued to lobby ministers in the British government throughout her life to bring about improvements in public health.

Since then, Florence Nightingale's work and that of others has paved the way for modern day approaches to visualizing data in healthcare. As digital data becomes the cornerstone of contemporary evidence-based practice, her work and its relevance should not go unnoticed. The increasing popularity of data visualization techniques has resulted in a systematic review of the literature, calls for evaluation frameworks that can be used in health care (Wu et al., 2019) and subsequent efforts to consolidate best practice (Khasnabish et al., 2019). Nightingale's pioneering work helped to begin a new trend, the implications of which can still be felt today as governments, organizations and citizens use digital data and visual techniques to affect change in healthcare and across every area of society in both public and private spheres. Furthermore, interdisciplinary initiatives are generating ever more sophisticated data visualizations and incorporating machine learning to tackle some of the challenges faced by the volume, variety, quality and connectedness of health data (Gotz & Borland, 2016). This means the value and impact of data visualization in healthcare will no doubt continue. As the 200th anniversary of the birth of Florence Nightingale approaches on 12 th May 2020, which coincides with the World Health Organization designated Year of the Nurse and the Midwife, we should reflect on and celebrate the significant and wide-reaching impact she has had. Florence is an enduring role model not only for nurses working globally on the front lines of delivering patient care but also for researchers who collect, analyse and disseminate new knowledge via digital techniques such as data visualization to bring about improvements in human health.

ACKNOWLEDGEMENTS

Sincere thanks to the Florence Nightingale Foundation Emerging Leaders Leadership Scholarship programme that inspired this commentary.

CONFLICT OF INTEREST

None.

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Data visualization in health care: The Florence effect

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Siobhan O'Connor PhD , Corresponding Author Siobhan O'Connor PhD Lecturer School of Health in Social Science, The University of Edinburgh, Edinburgh, UK Correspondence Siobhan O'Connor, School of Health in Social Science, The University of Edinburgh, Doorway 6 Old Medical Quad, Teviot Place, Edinburgh EH8 9AG, UK.

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I think its great that data visualization is utilized throughout healthcare. I am myself am a visual person. I am a visual learner, so when I see some for a visual representing the information that is being spoken about its easier for me to retain. Seeing healthcare information put into different graphs and visualizations make it easier for me to interpret the information and at the same time remain engaged.

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This monthly blog highlights and discuss emerging trends and challenges related to healthcare data and its ever changing life cycle.
By Melanie Meyer, PhD, MHA, RHIT, CCS, CPHQ

The scale of healthcare data has grown tremendously in recent years, increasing the need to present that data in ways that are more understandable and insightful. Data visualization, including storytelling, is an essential tool for doing this.

What is data visualization?

Data visualization places data in a visual context to help people better understand the data's significance.

The process of visualization synthesizes large volumes of data to get at the essence of that data and convey key insights.

Data storytelling goes further-linking data, visuals, and narrative, for example, in an infographic or storyboard. Adding the narrative to the data visualization helps highlight what is important in the visualization and how and why data changes over time. Data stories can help frame a discussion or provide a path to further analysis. Often, data stories are told through a series of linked visualizations.

Why is data visualization important?

Visuals or pictures convey meaning more easily than thousands or even millions of data points; storytelling focuses the message. Some examples of data visualization are here and here and here. Visualization also allows analysts and end users to recognize patterns and relationships in large volumes of data that may not be easily seen in the raw data or reports. This may help identify emerging trends, for example, to allow an organization to address quality or safety issues before they become bigger problems. The goal is to provide actionable insights that help drive change.

As access to data grows, data analysts and other users of healthcare data will require new skills and approaches for working with that data. A recent Forbes article found that the need for data storytellers will increase as analytic self-service capabilities grow and more people generate insights.

How is data visualization used?

Many organizations produce data visualizations in the areas of healthcare delivery, patient-facing applications, population health, public health, or global health. Some examples include:

The Institute for Health Metrics and Evaluation, a population health research center at UW Medicine, regularly features data visualization on its site regarding topics such as the social determinants of health and obesity.

Visualizing Health is a project of the Robert Wood Johnson Foundation and the University of Michigan Center for Health Communications Research that provides visualizations that communicate healthcare risk information.

The Center for Disease Control's National Center for Health Statistics offers a data visualization gallery based on the data the organization collects.

The Agency for Healthcare Research and Quality (AHRQ) offers a data visualization site that highlights findings from the Agency's Medical Expenditure Panel Survey, the Healthcare Cost and Utilization Project, and other AHRQ data sources.

Healthcare delivery organizations also create data visualizations via projects such as dashboards for quality initiatives. Some examples of dashboard best practices can be found here. A recent case study highlights the dashboard development process for facility-level data visualizations using electronic health record data. The visualizations focused on two areas: sepsis patient outcomes and 30-day readmissions. The goal was to provide meaningful summaries while highlighting critical performance metrics that were easy to understand by various end users across the organization. Utilization of the dashboards resulted in significant time savings and more readily available information.

Whether your role is data analyst or another area of health information management, understanding and using data visualization with storytelling will become more important in the future as more roles have access to and require use of a wider range of healthcare data.
Melanie Meyer is performance improvement leader at EVOSCALE Health.

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The Rise of Healthcare Data Visualization | Journal Of AHIMA

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This monthly blog highlights and discuss emerging trends and challenges related to healthcare data and its ever changing life cycle. By Melanie Meyer, PhD, MHA, RHIT, CCS, CPHQ The scale of healthcare data has grown tremendously in recent years, increasing the need to present that data in ways that are more understandable and insightful.

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Setup, conflict, resolution are the 3 major steps that guide storytelling with data. It was interesting to see much of an impact a presentation has on the data that is being reported. Both videos in our asynchronous material were interesting to watch. In their own both of the presenters caught my attention and kept me interested throughout the entire video. In using simplicity, imagery, and relatability both presenters portrayed their key points in a way that was easy to understand.

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Telling Stories with Data - method 1 (The "How we got here" method)

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Exploratory analysis is the process of searching of relationships within the collected data. This then allows for questioning the data and what it represents. Explanatory analysis is done after an insight has been found from the gathered the information. This answers the questions that arise from the exploratory analysis. A visualization is then formed to support the information, the question and the answer to the question regarding the data that has been analyzed.

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Exploratory vs  Explanatory Analysis wvgBSMks4p8 [Business Analytics Nanodegree]

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This short video and simple video that talks about the impact of COVID on the Cyber Security industry and healthcare. There has been an increase up to 86,000 of high risk malicious websites. This threat is leading to many challenges especially in healthcare. Healthcare with private and personal information are the highest forms of ransomware attacks because they have the greatest financial gain for the attacker. I have seen firsthand the increase in hacking of information.

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Healthcare information is a huge target for hackers. Regardless of how much is done to protect the information there has been even more of an increase now with more of our information being online. Some facilities are still using unencrypted emails such as Gmail or Yahoo which allows for easy access to breach of information. Appropriate actions need to be taken place to avoid this from happening. A risk assessment can be completed which makes an action plan to address issues that are placing a facility at a higher risk for breach of healthcare information.

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What You Need to Know about HIPAA Compliance and Violations

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Alarm fatigue is something I have personally experienced as nurse and know that many other nurses have as well. Boston adopted a alarm fatigue technique that entailed adjusting manufacturers default settings on alarms. In doing so, this reduced the noise from 90,000 alarms a week to over 9,000 a week. Alarm fatigue is the top technology hazard in hospitals. By reducing unnecessary alarms this will lead to greater patient safety, the ability to spend more time with patients, and overall limit the possibility of alarm fatigue.

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Boston hospital alarm fatigue technique held up as national model

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The Kaiser Family Foundation provided a generation of a data set related to the rate of individuals who received the COVID-19 booster as well as the the impact COVID-19 has had on individuals. According to the latest dataset information 77% related feeling tired and frustrated from the limitations of COVID-19. The rates of individuals to get the booster is slightly increasing. However, many individuals are reporting that whether vaccinated or not all individuals will end up getting COVID-19 at some point.

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The KFF COVID-19 Vaccine Monitor is an ongoing research project tracking the public's attitudes and experiences with COVID-19 vaccinations. Using a combination of surveys and qualitative research, this project tracks the dynamic nature of public opinion as vaccine development and distribution unfold, including vaccine confidence and acceptance, information needs, trusted messengers and messages, as well as the public's experiences with vaccination.
Key Findings

The latest KFF COVID-19 Monitor finds that fatigue and frustration dominate the public's mood as the U.S. nears the pandemic's two-year anniversary. While partisans have often been split in their pandemic attitudes, roughly three in four Democrats, independents, and Republicans say they feel "tired" and "frustrated," and similar shares say they think it is likely that most people in the U.S. will eventually get infected with COVID-19. Partisans do divide, however, on whether the pandemic is the most important issue facing the country, with about half of Democrats choosing COVID-19 as the most important among 6 different issues and a similar share of Republicans choosing inflation as the top issue.

The public overall says that compared to previous waves of the virus, they are now "more worried" about the impact of the omicron surge on the U.S. economy and on their local hospitals, but "less worried" about the impact in their own personal lives. Notably, however, Black and Hispanic adults and those with lower incomes report higher levels of worry than their counterparts when it comes to missing work due to COVID-19 or becoming seriously ill or hospitalized, reflecting the increased burden the pandemic has placed on people of color over the past two years.

While many vaccinated people have become infected with the omicron variant, a majority of the public (62%) continue to see the fact that most vaccinated people who get COVID-19 do not require hospitalization as a sign that the vaccines are working. However, the share who see breakthrough infections as a sign that the vaccines are not working has increased slightly since September (from 26% to 34%).

After holding relatively steady for several months, vaccine uptake inched up in January, with 77% now saying they have received at least one dose of a COVID-19 vaccine (up from 73% in November 2021). This movement seems to have come from a chipping away at the shares who say they will get vaccinated right away, will "wait and see" or will get vaccinated only if required. The share saying they will "definitely not" get vaccinated currently stands at 14% and has not moved in a statistically significant way since December 2020.

With the CDC now recommending that all eligible adults get a COVID-19 booster shot, we find that about four in ten (42%) of all adults say they have received a booster dose, a third (34%) have received at least one vaccine dose but are not boosted, and 22% remain unvaccinated. Among the population likely to be booster-eligible (those who either received a booster or completed a full initial course of vaccination 6 months ago or longer), seven in ten have gotten a booster.

Gaps in booster uptake are mirroring early gaps in initial vaccination uptake, with adults 65 and older (66%) and Democrats (62%) among the most likely to have received a booster and Black and Hispanic adults lagging behind White adults in booster uptake (31%, 37%, and 46%, respectively). Among those who are vaccinated but not boosted, four in ten (39%) say they want to get a booster as soon as they can, however a similar share say they will either definitely not get a booster (19%) or only do so if required (22%).

Nearly one-quarter of adults (23%) now say they've tested positive for COVID-19 at some point, including 8% who say they've tested positive in the past month. Among those who tested positive in the past month, 16% say their positive result was on an in-home test only, which is unlikely to be recorded in official COVID-19 case counts. Difficulty accessing COVID-19 tests is likely further contributing to an undercount of cases.

Amid the omicron surge, about six in ten of those who tried to purchase an at-home COVID-19 test say the tests were difficult to find, and one-third (35%) of those who tried to find an in-person test similarly report difficulty. Overall, nearly a quarter (23%) of all adults say they tried to find either type of test and faced difficulty. More broadly, about half of adults say there are not enough COVID-19 tests available in their local area for those who want them. In addition, while about eight in ten are confident the results of COVID-19 tests at a medical facility or testing site are usually accurate, just over half (54%) are similarly confident in the accuracy of home tests.

About half of adults say the FDA deserves at least a fair amount of blame for the limited availability of tests, while about four in ten assign blame to each President Joe Biden and the test manufacturers. President Biden announced on January 14th that Americans could order free at-home tests beginning on January 19 th, while the survey was still in the field.

Around one in five adults (19%) report they've personally had difficulty in the past three months figuring out whether they needed to isolate or limit their normal activities due to a COVID-19 exposure, positive test, or symptoms. This is even higher among those who say they tested positive for COVID-19 in the last month (38%) and those who reported difficulty finding a COVID-19 test (37%).

Mood Of The Country

Nearly two years into the COVID-19 pandemic, the public's mood is dominated by fatigue and frustration. About three in four adults including majorities across age, gender, race and ethnicity, and income groups say that "tired" and "frustrated" describe how they feel about the current state of the pandemic in the U.S., while about four in ten each say they are "optimistic" or "angry" and three in ten say they feel "confused."

While previous KFF research has shown that most attitudes toward COVID-19 tend to diverge along partisan lines, "tired" and "frustrated" are the dominant emotions expressed across partisans, with at least seven in ten Democrats, independents, and Republicans saying each word describes how they feel about the current status of the pandemic.

Despite feeling fatigued about the current state of the pandemic, about a third of adults (34%) continue to see the pandemic as the biggest problem facing the country right now, ranking slightly ahead of rising prices due to inflation (28%). About one in ten say climate change (9%) and racial inequality (8%) are the biggest problems facing the country, while 6% choose crime and 5% choose product shortages due to supply chain issues as the country's biggest problems. Partisans have differing views as a half of Democrats (51%) say the pandemic is the biggest problem, while a similar share of Republicans (44%) cite inflation. Among independents, about equal shares say the pandemic (28%) and inflation (30%) are the biggest problem facing the United States right now.

Along with feelings of fatigue and frustration, the public overall seems resigned to the idea that COVID-19 infection is inevitable for most people. Three in four adults (77%) think that most people in the U.S. will eventually get COVID-19, including majorities across gender, age, income, and racial and ethnic groups. Similar shares across partisanship and vaccine status also say this is inevitable.

In addition, six in ten (62%) of those who have never tested positive for COVID-19 think it is "very likely" or "somewhat likely" they will become infected in the next year, including two-thirds of those who are vaccinated and 45% of those who are unvaccinated and also have never tested positive.

Impact Of Omicron

Omicron's Impact On Worries

With the omicron variant of COVID-19 spreading across the country, about four in ten adults (42%) say they are "very" or "somewhat" worried that they will become infected with the new variant. The share who are worried about the virus' impact is somewhat lower, with about a third saying they are worried they will become seriously sick from coronavirus (34%) or will have to miss work due to a coronavirus infection (36%). About a quarter (27%) say they are worried they will be hospitalized for COVID-19. Each of these worries are more prevalent among adults with lower incomes and people of color. For example, Hispanic adults (57%) and Black adults (43%) are much more likely than White adults (27%) to say they are worried about having to miss work due to a coronavirus infection, as are those with incomes under $40,000 compared to those with higher incomes (46% vs. 31%). Hispanic and Black adults are also more likely than White adults to say they are worried about becoming infected with omicron, becoming seriously ill, or being hospitalized due to coronavirus, as are lower-income adults compared to their higher-income counterparts.

Similar to previous KFF Vaccine Monitor findings, vaccinated adults are also more likely than those who are unvaccinated to say they are worried about getting infected with, getting seriously sick from, or being hospitalized due to coronavirus. At least three in ten vaccinated and unvaccinated adults are worried about having to miss work due to a coronavirus infection.

While many adults, and particularly people of color, are worried about the personal impacts of omicron, the public overall says omicron has made them more worried about the pandemic's impact on the economy, their local hospitals, and people who are unvaccinated. Majorities say that compared to previous surges of the pandemic, they are now worried about the impact of omicron on the U.S. economy (56%) and on their local hospitals (54%). Half of adults also say they are worried about omicron's impact on people who are not vaccinated, however this largely reflects the vaccinated population's worry about the unvaccinated. Among unvaccinated adults themselves, just 15% say they are more worried about omicron's impact, compared to 61% of vaccinated adults who are worried about the impact on the unvaccinated.

When it comes to personal impact, the public reports being less worried about omicron than they were about previous waves of the pandemic. Majorities say that compared to previous surges, they are less worried about the impact of omicron on themselves personally (69%) and on their way of life (58%). An even larger share of unvaccinated adults say they are less worried about the impact of omicron on themselves personally (76% of unvaccinated adults, compared to 67% vaccinated) and their way of life (66% of unvaccinated adults, 55% vaccinated).

About seven in ten adults (71%) say they are "very" or "somewhat" worried that restrictions aimed at stopping the spread of omicron will hurt local businesses in their area and similar shares (68% each) say they are worried that their local hospitals will be overwhelmed with COVID-19 patients or that other new and more serious variants will spread in the United States. While majorities across partisans express worry about the impact of restrictions on local businesses, Democrats are much more likely than Republicans to worry that their local hospitals will be overwhelmed (86% vs. 50%) or that new variants will develop and spread in the U.S. (86% vs. 48%).

Despite widespread reports of vaccinated individuals becoming infected with the omicron variant, most adults (62%) say "the fact that most vaccinated people who become infected with COVID-19 do not require hospitalization means that the vaccines are working" while about a third (34%) say "the fact that some vaccinated people are becoming infected with COVID-19 means that the vaccines are not working." Notably, the share who see breakthrough infections as a sign that the vaccines are not working has increased slightly since September (from 26% to 34%), mainly driven by an increase among Republicans (from 39% to 50%) and unvaccinated adults (from 66% to 84%).

Omicron's Impact On Behaviors

When asked about the impact of omicron on their own behaviors, 43% of adults say the spread of the new variant has made them more likely to wear a mask and nearly as many (38%) say it has made them more likely to avoid large gatherings. Reflecting their higher levels of worry about becoming infected with omicron, Black and Hispanic adults are more likely than White adults to say the new variant has prompted them to change their behavior, as are vaccinated adults compared to unvaccinated adults and Democrats compared to Republicans.

Trend data also reveals that self-reported mask usage has increased among the public since last summer. A majority of adults (57%) now say they wear a mask every time when they are in a grocery store, a 20-percentage point increase since July 2021. About half of the public say they wear masks every time at work (47%) or on public transportation (49%), up from about a third in July. While a majority of adults (54%) say they were a mask every time they are in an indoor crowded place, fewer (33%) report consistent mask usage in crowded outdoor spaces. Overall, Black and Hispanic adults are more likely than White adults to report consistent mask usage, as are vaccinated adults compared to those who are not vaccinated. Across partisans, Democrats are roughly two to three times as likely as Republicans to say they wear a mask every time in each of the situations presented.

Trends In COVID-19 Vaccination Intentions And Uptake

After holding relatively steady for several months, COVID-19 vaccine uptake inched up between November 2021 and January 2022. More than three-quarters of adults (77%) now report that they have received at least one dose of a COVID-19 vaccine, up from 73% in November. One percent say they plan to get vaccinated as soon as possible, 4% say they want to "wait and see" before getting a vaccine, and 3% say they will get vaccinated only if they are required for work, school, or other activities. One in eight adults (14%) say they will "definitely not" get vaccinated for COVID-19, a share that has held generally constant since December 2020.

The spread of omicron in the U.S. does not appear likely to motivate more unvaccinated adults to get the COVID-19 vaccine. Three in four unvaccinated adults (76%) say that news of the omicron variant spreading has not made much of a difference as to whether they will get the vaccine while just 8% said it has made the more likely to get vaccinated and 15% say it has made them less likely to do so.

Looking at demographic patterns of COVID-19 vaccine uptake, we continue to find that Democrats, college graduates, older adults, and those living in urban areas report being vaccinated at higher rates than Republicans, those without college degrees, younger adults, and rural residents.

The age gap in vaccine uptake appears to be shrinking somewhat with 73% of 18-29 year-olds and 72% of 30-49 year-olds now saying they are vaccinated, compared to about two-thirds in November. In addition, the shares of Republicans (63%) and independents (76%) who report being vaccinated reached new highs this month, though still lagging significantly behind the vaccination rate among Democrats (91%). Notably, a quarter of Republicans (26%) continue to say they will "definitely not" get vaccinated, a share that has held relatively steady since December 2020.

Vaccine Booster Uptake And Intentions

A recent study released by the Centers for Disease Control and Prevention (CDC) found that a booster dose was effective in preventing hospitalization due to COVID-19 and the CDC announced earlier this month that a booster dose will be required for adults to be considered "up-to-date" on their COVID-19 vaccinations. We find that among adults overall, about four in ten (42%) have received a booster dose, while 34% have received at least one vaccine dose but are not boosted and 22% remain unvaccinated.

Mirroring their earlier uptake of the initial vaccine doses, Democrats (62%) are more likely than independents (37%) and Republicans (32%) to have received a booster. Similarly, two-thirds of adults 65 and older and half of adults ages 50-64 have received a booster, compared to three in ten adults ages 30-49 (31%) and a quarter of those ages 18-29 (24%) who say the same.

Notably, across racial and ethnic groups, White adults (46%) are more likely than Black (31%) and Hispanic (37%) adults to say they have received a booster dose of the vaccine, while larger shares of Black and Hispanic adults are vaccinated but not boosted (49% and 39% respectively, compared to 29% of White adults). This may at least partially reflect some Black and Hispanic adults not yet being eligible for a booster if their initial course of vaccination was completed more recently, since access issues and other concerns delayed initial vaccine uptake for some people.

Among vaccinated adults who are likely eligible for a booster dose (those who either got a booster or completed an initial full course of vaccine at least six months ago), seven in ten have gotten a booster. This rises to 86% among those ages 65 and over and 77% among those ages 50-64 who are likely booster-eligible.

However, there is evidence that booster uptake and access may be lagging among the booster-eligible population of Black adults. Among the likely booster-eligible population, 57% of Black adults compared with 69% of Hispanic adults and 72% of White adults have gotten a booster.

The racial and ethnic gap in booster uptake does not appear to be driven by a lack of desire among Black and Hispanic adults to get boosted. Among those who are vaccinated but not yet boosted, four in ten (39%) say they want to get a booster dose as soon as they can, including 41% of Black adults and nearly half of Hispanic adults (47%).

On the other hand, resistance to getting a booster does appear to be a factor in the partisan gap in booster uptake. A majority (58%) of Democrats who are vaccinated but not yet boosted say they want to get a booster dose as soon as they can, compared to just 18% of Republicans who have not yet gotten a booster. Indeed, about half (53%) of vaccinated but not yet boosted Republicans say they will either "definitely not" get a booster (23%) or will only do so if they are required (30%).

Most vaccinated but not boosted adults (60%) say news of the omicron variant spreading has not made much of a difference as to whether they will get a booster. However, about three in ten (29%) say the spread of omicron has made them likely to get a booster shot, a share that rises to 41% among Hispanics who are vaccinated but not boosted.

When asked to say in their own words the main reason they have not gotten a booster dose, about one in eight vaccinated but not boosted adults offer that they feel that their initial vaccination doses are enough or that they don't need it (13%) and a similar share say they are currently not eligible for a booster (12%). Other common reasons provided include doubts about the efficacy of boosters, including those who cite the fact that vaccinated people are still getting sick (9%), being too busy or not having time to get a booster (8%), having already gotten COVID-19 (7%), work-related concerns such as having to miss work to get the shot or recover from side effects (5%), medical issues (5%), procrastination (5%) and wanting to wait and see how boosters are working (5%).

Among those who are more open to getting a booster (those who say they want to get a booster dose as soon as possible or wait and see), the most common reasons given for not getting a booster are that they aren't eligible (17%), are too busy (12%), already had COVID-19 (8%), or haven't gotten around to it or have been putting it off (8%). Those who are more reluctant (saying they will get a booster only if required or will definitely not get one) cite different reasons for not getting boosted, with the top answers being that they feel they don't need a booster or feel the initial vaccination is enough (22%), or that they question the boosters' effectiveness (19%).

Across racial and ethnic groups, one in five Hispanic adults who are vaccinated but not boosted (21%) cite lack of eligibility as the main reason they have not gotten a booster dose, as do 13% of Black adults and 10% of White adults. Notably, about one in ten vaccinated but not boosted Black and Hispanic adults said they have been too busy or haven't had time to get a booster dose of the vaccine.

The spread of the omicron variant has led some disease experts to call for COVID-19 vaccine mandates for air travel. Overall, most of the public think that COVID-19 vaccination should be required for international air travel (62%) and for domestic air travel (55%) and about half think there should be a vaccination requirement for train travel within the U.S. (51%). Unsurprisingly, there are large partisan differences on whether to institute vaccination requirements for travel. More than three in four Democrats think there should be a vaccine requirement for air travel and train travel, whereas about four in ten Republicans (39%) think a requirement should be in place for international travel, and about one in four think a requirement should be in place for domestic travel by air (28%) or train (25%).

COVID-19 Testing

How Many Adults Have Personally Tested Positive For COVID-19?

Almost a quarter of adults now say they have personally tested positive for COVID-19 at some point (23%), while another 12% say someone in their household has tested positive and 49% say someone else they know has. Around a third of unvaccinated adults say they have tested positive at some point (34%), significantly higher than the share among vaccinated adults (21%).

Notably, while unvaccinated adults are more likely than those who are vaccinated to say they tested positive for COVID-19 at some point, similar shares of vaccinated and unvaccinated adults say they tested positive within the past month amid the omicron surge (7% and 10%, respectively).

Amid concerns that official COVID-19 case counts may be an underestimate if many people are taking at-home tests that are not recorded by government agencies, about eight in ten of those who say they tested positive in the past month say they received a positive result on an in-person test (66%) or on both an in-person and at-home test (17%).

One in six (16%) adults who received a positive test result in the past month (representing about 1% of all U.S. adults) say that result came on an at-home test only. With few states or localities offering residents a way to report their at-home tests, these results are unlikely to be included in official case counts.

Experiences With Testing Access

With COVID-19 cases surging to unprecedented levels over the past two months, many U.S. adults report difficulty accessing both at-home and in-person COVID-19 tests.

While many made an effort to find testing for COVID-19, some - depending on what type of test they sought - struggled with the task. A majority (65%) of adults who tried to get an in-person COVID-19 test in the past month say it was easy, though a substantial share, 35%, say it was difficult. At-home tests presented even more of a challenge, with 62% of those who tried to find an at-home test reporting it was difficult and 38% saying it was easy.

Overall, 23% of all adults say that they tried to get either an in-person or at-home test in the past month and had difficulty finding one, 24% tried and didn't have any difficulty, and 52% didn't try to access either type of test. Suburban (27%) and urban (24%) residents are more likely than rural residents (12%) to report difficulties finding tests in the past month. As well, higher shares of young adults (33% of those ages 18-29) and adults with high incomes (32% of those with a household income of $90K or more a year) reported difficulties finding tests. These differences are largely a reflection of the higher shares of these groups who said they attempted to purchase at-home tests, which were often in short supply.

The vast majority (89%) of those who attempted to get an in-person COVID-19 test in the past month say they were able to get one, including 58% who say they got a test on the first day they wanted one. However, 11% of those who tried to get such a test (4% of all adults) say they were unable to get an in-person test, and 23% (7% of all adults) say they had to wait 2 days or more to be tested. Combined with the longer wait time for the results of in-person tests, this could result in delays in returning to work or other activities for people who were exposed or experiencing symptoms of COVID-19.

Among those who tried to get an at-home test, one-third (33%, or 9% of all adults) say they were unable to get one, mainly because tests were not available rather than being too expensive (91% vs. 2% among those who tried but could not get an at-home test).

Perceptions Of Test Access In Local Areas

Beyond their personal experiences, about half of adults (48%) feel that there are not enough COVID-19 tests available in their local area for people who want them, while 45% believe there are enough tests. Residents of rural areas are more likely to think there are enough tests in their area (53%) compared to suburban residents (42%). President Biden announced on January 14th that Americans could order at-free home tests beginning on January 19 th, while the survey was still in the field.

Adults with higher incomes and those in urban and suburban areas are more likely to feel that the testing supply isn't adequate, which may reflect the fact that they were more likely to say they have sought out at-home tests, which many people reported difficulty finding.

Despite reported difficulty accessing tests, most adults are confident they would be able to access COVID-19 testing if needed. About eight in ten adults overall say they are confident that they would have access to COVID-19 testing if they were exposed to the virus (81%), with about half saying they are very confident (51%).

Who Does The Public Blame For Issues With Testing?

When asked who is responsible for the limited availability of COVID-19 tests in the U.S., around half of adults (49%) say that the FDA deserves a lot or a fair amount of blame for the limited availability of COVID-19 tests in the United States. Slightly fewer think President Joe Biden deserves a fair amount or a lot of blame (44%) with a similar number who say the same about the COVID-19 test manufacturers (41%). However, more adults say that Biden deserves a lot of blame (26%), compared to the FDA (19%) and test manufacturers (16%).

Republicans and unvaccinated adults are more likely than Democrats and vaccinated adults to place at least a fair amount of blame for test shortages on President Biden and on the FDA. Three in four Republicans (75%) and more than four in ten independents (44%) say President Biden deserves a lot or a fair amount of blame for the limited availability of COVID-19 tests, compared to one in five Democrats (21%) who say the same. Similarly, Republicans are more likely than Democrats to say the FDA deserves a lot or a fair amount of blame (62% vs. 39%).

Confidence In Accuracy Of Testing

In addition to difficulty accessing at-home tests, many adults lack confidence in their accuracy. While a large majority are very (43%) or somewhat (39%) confident that the results of in-person COVID-19 tests at a medical facility or testing site are usually accurate, far fewer are very (11%) or somewhat (44%) confident in the accuracy of at-home tests.

Views Of CDC Guidance Following Testing Or Exposure

Even after testing positive or being exposed to COVID-19, many still are not sure what they are supposed to do. Around one in five adults (19%) report they have personally had difficulty in the past three months figuring out whether they needed to isolate or limit their normal activities due to a COVID-19 exposure, positive test, or symptoms they were experiencing. Adults under 65 (22%) are more likely than those 65 and older (8%) to say they have had difficulty figuring out whether they needed to isolate or limit their normal activities; among 18-29 year-olds more than one in four (27%) report difficulty making personal decisions around COVID. Notably, 38% of those who say they tested positive for COVID-19 in the last month say they had difficulty deciding whether they needed to isolate, as did 37% of those who reported difficulty finding a COVID-19 test.

In late December, the CDC shortened the recommended isolation time for people who test positive for COVID-19 from 10 days to 5 days, followed by another five days of wearing a mask. Some individuals and organizations, including the American Medical Association, questioned the wisdom of the updated guidelines and whether they would be confusing for people to follow. In addition, some states and localities, including Washington, DC, have continued to advise residents to observe a 10-day isolation period. Our findings suggest that the change has indeed been confusing for the public, with around six in ten adults (58%) saying the CDC guidelines for testing and isolation for those exposed or infected with COVID-19 are confusing, while 39% think the guidelines are clear.

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KFF COVID-19 Vaccine Monitor: January 2022

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This report shows that 42% of all adults have received a COVID-19 booster, including about 70% of those who are likely booster-eligible. More than three quarters (77%) have received at least one dose of a COVID-19 vaccine. The poll also explores the public's experiences getting at-home and in-person COVID-19 tests and other issues.

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The patients perspective on patient portals 

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The patients perspective on patient portals 

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I have enjoyed using the patient portal throughout my healthcare. It has made things so much easier and more convenient. There are many issues I have been able to address via my patient portal. Especially during the COVID-19 pandemic it has brought me great relief. I know it has for others as well, especially those who are caring for individuals that are immunocompromised.

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It is interesting to learn about how quickly telehealth has taken over in healthcare. I was not aware of the flexibility allotted with telehealth visits. For example, physicians who are licensed in Washington can see a patient who lives in California or how patients do not need to be established with a provider to have a telehealth visit with that specific provider. It definitely provides more convenience for patients. I'm curious to see how telehealth will influence my career in the future.

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With the arrival of COVID-19 and stay-at-home orders instituted across the country, physicians turned to telemedicine as a way to remain engaged with their patients. This form of real-time, audio-video communication allows physicians and patients to connect from different locations existed prior to the pandemic, but certain restrictions limited widespread usage.

Because of the coronavirus and temporary waivers on Medicare limitations, though, the usage numbers skyrocketed. A McKinsey & Co. report estimated that physicians saw between 50 and 175 times more patients via telehealth than they did prior to the pandemic.

As practices continue to focus on providing telehealth opportunities to their patients, the AMA has updated its telemedicine STEPS Forward™ module to reflect changes implemented during the pandemic. STEPS Forward™ is a series of interactive and engaging educational modules created by physicians to help address and potentially resolve common practice challenges.

Learn how the AMA is advancing telemedicine during the COVID-19 pandemic.

The changes that made telemedicine's rise possible

The module highlights five key changes that were made by the Centers for Medicare & Medicaid Services (CMS) that made telehealth opportunities so accessible during the current pandemic. Those changes were:

Medicare will pay physicians the same rate for telehealth services as they do for in-person visits for all diagnoses, not just those related to COVID-19, throughout the national public health emergency.

Patients can be in their home, or in any other setting, to receive telehealth services.

Patients do not need to have an existing relationship with the physician who is providing telehealth assistance.

Physicians are allowed to waive or reduce cost-sharing for telehealth visits.

Physicians who are licensed in one state are allowed to see a patient in a different state.

Discover why so many patients still can't connect to doctors via telehealth.

Steps to adopt telemedicine in your practice

The updates build upon the original module, which highlights key steps to successfully implementing telemedicine in your practice.

Once you have an understanding about the laws and regulations related to telehealth, the next step is to determine a service model that works best for you and your practice. There are a host of service models available through telehealth, including, but not limited to:

Direct care for your existing patients with face-to-face videoconferencing.

Acting as a connector with other physicians who offer store-and-forward consultation applications, meaning that medical data or images are captured and then forwarded to a separate specialist who at a later time reviews it and provides feedback.

Leveraging remote monitoring tools to help manage chronic illnesses

Participating as a consultant to physicians or other practitioners.

From there, you need to identify which technical applications and assistance you will need in order to successfully implement telehealth. Privacy should be the first word that comes to mind when thinking about this.

It is important to carefully select technologies to comply with HIPAA, the module notes. Privacy relates to more obvious aspects like appropriate informed consent and data encryption, but it also includes issues like finding a secure room that prevents any potential unauthorized access.

Lastly, in order to implement telehealth in your practice, you need to understand appropriate guidelines to initiate a telemedicine service model. This includes following appropriate specialty clinical practice guidelines as set out by medical specialty societies or state medical associations, following technical guidelines and ensuring appropriate sustainability models to support the types of telehealth services being offered.

The AMA has developed a Telehealth Implementation Playbook that is designed to support efficient, successful implementation of telemedicine so that physicians, care teams, patients and the broader community can experience the benefits of telehealth in practice. Learn more about protecting cybersecurity while using telehealth in this document, " Working from home during COVID-19 pandemic," produced by the AMA and the American Hospital Association.

Essential Tools & Resources

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5 huge ways the pandemic has changed telemedicine

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With the arrival of COVID-19 and stay-at-home orders instituted across the country, physicians turned to telemedicine as a way to remain engaged with their patients. This form of real-time, audio-video communication allows physicians and patients to connect from different locations existed prior to the pandemic, but certain restrictions limited widespread usage.

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This is a great ted talk that discusses the effect of COVID19 on telehealth. COVID19 brought forth a rise in the use of telehealth. Telehealth visits have drastically increased over the past two years. It has been a great way for individuals especially those immunocompromised to continue receiving healthcare. I believe it has been a benefit to both patients and providers during this challenging time.

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The COVID-19 Telehealth Accelerator | Jack Miner | TEDxTraverseCity

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This class was the first time I really looked into gamification for different health issues. I think its important to provide individuals with different options especially when it comes to learning something about health. A "hand-out" is not always the best option. Allowing people to play a game and be hands on helps increase the changes of retaining information that will benefit them the most.

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From the Ancient Egyptian board game Senet (3500 BCE) to current video games like Call of Duty, people have played games throughout history. In some ways, we are wired to enjoy games.

While games are old, the term "gamification" is new. Gamification is the activity of applying gameplay methods to non-games to make them more engaging. According to experts , the term gamification didn't become popular until 2010; however, people have been "gamifying" since the late 1800s.

The history of gamification

Here's a brief timeline of the history of gamification:

1896: Sperry & Hutchinson Co. created a reward program using "Green Stamps." Shoppers collected stamps and later redeemed them for rewards (like the modern Monopoly game from McDonald's).

1908: Boy Scouts started using badges to incentivize members to learn skills, attend events, and do other activities to earn badges and rewards.

1981: American Airlines launched the first frequent flier program that encouraged customer loyalty by offering monetary rewards for customers.

2002: The term "gamification" first appeared when Nick Pelling coined the word.

2020-2025: The gamification industry is expected to have a compound annual growth rate of 30.31% over the next five years. As of 2019, gamification was already a 7.17 billion dollar industry .

Gamification history shows that gamification has been utilized as a marketing strategy and a tactic to engage learners. By using gamification, people are more likely to take specific actions (like purchasing or learning) because they enjoy playing games.

Why is gamification effective in learning?

Unfortunately, we've all had the experience of nodding off in class, at a training, or while studying. Despite our best efforts, sometimes learning just isn't "fun."

This is where gamification comes in.

On a basic level, we are wired to learn from play. From the time we are little, we learn through "playing pretend:" acting out imaginative scenarios that are instructive for development and socialization .

As adults, games still have the power to transform tedious tasks into engaging, pleasurable activities. Gamification is so effective in elearning (for children and adults) because the learner they are "working" and instead feels they are "playing."

Additionally, scientific studies show that students who learn with gamified content experience increased long-term retention. According to the International Journal of Educational Technology in Higher Education , gamification increased retention by 12.23% and increased overall performance by 7.03%.

A systematic review of gamification's effectiveness also found that:

"Gamification promises to increase engagement with online programs...Results of this systematic review indicate that gamification positively impacts engagement and downstream behaviours (e.g. health behaviours and academic performance), especially in the short term. Preliminary evidence also indicates that leader boards may be a particularly effective gamification feature, however more research is required to confirm this" ( ).

As far back as 1997, the U.S. Department of Defense used gamification to train soldiers safely. They saw astounding results: "an 11% increase in knowledge recall, a 14% increase in procedural knowledge, and a 9% greater retention of knowledge" ( ).

Overall, gamification apps help learners engage authentically with content and forget that they are "working". Gamification is one of the most effective ways to increase learner engagement, retention, and recall by reducing barriers to elearning.

Effective Gamification with EdApp

, we infuse gamification strategies into the core of our microlearning platform. Why? Gamification triggers real emotions in learners (like the desire to win or earn prizes) that motivate learners to engage with content authentically.

Mobile microlearning, combined with gamification, puts modern workers in the best position to learn. That's why companies who use EdApp's platform have experienced increased completion rates , increased engagement in training, and increased retention.

Here's how it works:
Prizing and real rewards
The EdApp platform allows you to provide real rewards to employees. Example rewards include gift cards, free coffee, or food vouchers from Uber Eats.

As employees progress in their gamified training, they can redeem rewards based on criteria set by you, the trainer. Motivate employees to learn by giving them rewards based on course completion, frequency-of-use, and performance.

Custom Achievements is a reward system to help level up your engagement game by recognizing and incentivizing superb learning habits. It allows learners to earn achievements that can be customized based on your criteria of good learning behavior.

With Custom Achievements, you can personalize a wide range of accomplishments and rewards. Some can be as simple as opening the app for the first time or completing an entire course with flying colors. It's all up to you to determine the ingredients of each achievement and set up a "recipe" for success based on your intended learning process.

To match the branding and tone of your organization, you can name your achievement, upload your own image or design one on Canva, and choose your preferred color scheme. You can also include a personalized message to display whenever a learner earns a specific achievement to further enhance the learner experience.

If you're not sure where to start, EdApp has a list of default achievements to serve as a guide to creating your own.
Leaderboards
EdApp's leaderboard function allows employees to compare their learning progress with others. People are more motivated to learn when there are real stakes, like winning and losing. Leaderboards promote healthy competition and push employees to get excited about the learning environment and learning experience.
Rapid authoring gaming templates
We offer diverse, premade gaming templates for you to customize. Templates range from True/False to a Jeopardy simulation. These easy-to-use templates allow you to transform your current online training system into gamified content within a few hours (or minutes, depending on how fast you work!).

Case study: "The Bar World of Tomorrow" course

In collaboration with Pernod Ricard, Trash Tiki, and the Sustainable Restaurant Association, EdApp recently released a course called The Bar World of Tomorrow . During this challenging time of COVID-19, the course aims to prepare professionals for the future of the service industry. You can take the course for free here .

The course contains 14 microlessons and focuses on the best sustainability and responsibility practices in the bar world. All content is aligned with the SDGs (Sustainable Development Goals). Learn how to use responsibly-sourced ingredients, reduce food waste, and address common bar problems with actionable solutions.

Bonus content includes: transforming your bar checklist, mocktail and cocktail recipes, a seasonality chart, and more!

The course uses gamification in two ways: badges to reward learners when they reach goals and gamified course content. Here's what it looks like:
#1 - Categorize and match game
In this game, the learner matches the word in the middle with 1 of 2 categories. As they play, they gain points for correct answers. This game tests the learner's knowledge through a fun, interactive game within the flow of digital learning.

At the end of the game, the learner can review their progress and even try the game again to reinforcement and get a higher score.
#2 - Letter Jumble
Letter Jumble is an excellent game to review newly-learned vocabulary words and visualize the words. How it works: gain points for placing the correct letter in the missing box to form the word.

At the end of the game, the learner can review their score and retry the game again to earn a higher score. Points are an excellent way to motivate learners to "win" and keep learning. This activity encourages repetition in mlearning through gamified content.

Key takeaways from The Bar World of Tomorrow course:

1. Gamified elements keep a learner engaged throughout a microlesson. While games work well as a review tool, they also keep the learner engaged when they are embedded throughout the lesson.

2. Gamified lessons can take multiple shapes and forms, depending on your needs as a trainer or curriculum designer.

3. Gamified content doesn't feel like a "test," but learners testing their knowledge while playing. Gamification increases learner retention and motivation by making work fun.

EdApp and gamification

At EdApp learning management system (LMS), we know that gamification is not an "add-on" to corporate learning-it is an integral part of the learning process. Microlearning and gamification work hand-in-hand to perfect the mobile learning experience for millennials and modern learners. Get better training outcomes for your team today by creating your own course or checking out the instructional designs from the learning platform's fully editable course library.

You may also be interested in the Personal Productivity course from EdApp's free content library! Click here to check it out.

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From the Ancient Egyptian board game Senet (3500 BCE) to current video games like Call of Duty, people have played games throughout history. In some ways, we are wired to enjoy games. While games are old, the term "gamification" is new. Gamification is the activity of applying gameplay methods to non-games to make them more engaging.

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The challenges of data quality and data quality assessment in the big data era. 

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The challenges of data quality and data quality assessment in the big data era. 

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The rate at which data collected and the way it is collected can truly have an impact on the quality of the assessment. This article goes through are pros and cons to the rate at which data is collected. The timing of the data collection is not always the issue. However, as long as it is relevant to the issue at hand quality results can still be obtained.

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1 Introduction

Many significant technological changes have occurred in the information technology industry since the beginning of the 21 st century, such as cloud computing, the Internet of Things, and social networking. The development of these technologies has made the amount of data increase continuously and accumulate at an unprecedented speed. All the above mentioned technologies announce the coming of big data (Meng & Ci, 2013). Currently, the amount of global data is growing exponentially. The data unit is no longer the GB and TB, but the PB (1PB = 2 ¹⁰TB), EB (1EB = 2 ¹⁰PB), and ZB (1ZB = 2 ¹⁰EB). According to IDC's "Digital Universe" forecasts (Gantz & Reinsel, 2012), 40 ZB of data will be generated by 2020.

The emergence of an era of big data attracts the attention of industry, academics, and government. For example, in 2012, the US government invested $200 million to start the "Big Data Research and Development Initiative" (Li & Chen, 2012). Nature launched a special issue on big data (Nature, 2008). Science also published a special issue "Dealing with Data" (Science, 2011), which illustrated the importance of big data for scientific research. In addition, the development and utilization of big data have been spread widely in the medical field, retail, finance, manufacturing, logistics, telecommunications, and other industries and have generated great social value and industrial potential (Feng, Z. Y., Guo, X. H., Zeng, D. J., et al., 2013).

By rapidly acquiring and analyzing big data from various sources and with various uses, researchers and decision-makers have gradually realized that this massive amount of information has benefits for understanding customer needs, improving service quality, and predicting and preventing risks. However, the use and analysis of big data must be based on accurate and high-quality data, which is a necessary condition for generating value from big data. Therefore, we analyzed the challenges faced by big data and proposed a quality assessment framework and assessment process for it.

2 Literature Review on Data Quality

In the 1950s, researchers began to study quality issues, especially for the quality of products, and a series of definitions, for example, quality is "the degree to which a set of inherent characteristics fulfill the requirements" (General Administration of Quality Supervision, 2008); "fitness for use" (Wang & Strong, 1996); "conformance to requirements" (Crosby, 1988) were published. Later, with the rapid development of information technology, research turned to the study of the data quality.

Some literature regarded web data as research objects and proposed individual data quality standards and quality measures. Alexander and Tate (1999) described six evaluation criteria - authority, accuracy, objectivity, currency, coverage/intended audience, and interaction/transaction features for web data. Katerattanakul and Siau (1999) developed four categories for the information quality of an individual website and a questionnaire to test the importance of each of these newly developed information quality categories and how web users determine the information quality of individual sites. For information retrieval, Gauch (2000) proposed six quality metrics, including currency, availability, information-to-noise ratio, authority, popularity, and cohesiveness, to investigate.

From the perspective of society and culture, Shanks and Corbitt (1999) studied data quality and set up an emiotic-based framework for data quality with 4 levels and a total of 11 quality dimensions. Knight and Burn (2005) summarized the most common dimensions and the frequency with which they are included in the different data quality/information quality frameworks. Then they presented the IQIP (Identify, Quantify, Implement, and Perfect) model as an approach to managing the choice and implementation of quality related algorithms of an internet crawling search engine.

Research in China on data quality began later than research abroad. The 63rd Research Institute of the PLA General Staff Headquarters created a data quality research group in 2008. They discussed basic problems with data quality such as definition, error sources, improving approaches, etc. (Cao, Diao, Wang, et al., 2010). In 2011, Xi'an Jiaotong University set up a research group of information quality that analyzed the challenges and importance of assuring the quality of big data and response measures in the aspects of process, technology, and management (Zong & Wu, 2013). The Computer Network Information Center of the Chinese Academy of Sciences proposed a data quality assessment method and index system (Data Application Environment Construction and Service of the Chinese Academy of Sciences, 2009) in which data quality is divided into three categories including external form quality, content quality, and the utility of quality. Each category is subdivided into quality characteristics and an evaluation index.

3 The Challenges of Data Quality in the Big Data Era

3.1 Features of big data

Because big data presents new features, its data quality also faces many challenges. The characteristics of big data come down to the 4Vs: Volume, Velocity, Variety, and Value (Katal, Wazid, & Goudar, 2013). Volume refers to the tremendous volume of the data. We usually use TB or above magnitudes to measure this data volume. Velocity means that data are being formed at an unprecedented speed and must be dealt with in a timely manner. Variety indicates that big data has all kinds of data types, and this diversity divides the data into structured data and unstructured data. These multityped data need higher data processing capabilities. Finally, Value represents low-value density. Value density is inversely proportional to total data size, the greater the big data scale, the less relatively valuable the data.

3.2 The challenges of data quality

Because big data has the 4V characteristics, when enterprises use and process big data, extracting high-quality and real data from the massive, variable, and complicated data sets becomes an urgent issue. At present, big data quality faces the following challenges:

The diversity of data sources brings abundant data types and complex data structures and increases the difficulty of data integration.
In the past, enterprises only used the data generated from their own business systems, such as sales and inventory data. But now, data collected and analyzed by enterprises have surpassed this scope. Big data sources are very wide, including: 1) data sets from the internet and mobile internet (Li & Liu, 2013); 2) data from the Internet of Things; 3) data collected by various industries; 4) scientific experimental and observational data (Demchenko, Grosso & Laat, 2013), such as high-energy physics experimental data, biological data, and space observation data. These sources produce rich data types. One data type is unstructured data, for example, documents, video, audio, etc. The second type is semi-structured data, including: software packages/modules, spreadsheets, and financial reports. The third is structured data. The quantity of unstructured data occupies more than 80% of the total amount of data in existence.
As for enterprises, obtaining big data with complex structure from different sources and effectively integrating them are a daunting task (McGilvray, 2008). There are conflicts and inconsistent or contradictory phenomena among data from different sources. In the case of small data volume, the data can be checked by a manual search or programming, even by ETL (Extract, Transform, Load) or ELT (Extract, Load, Transform). However, these methods are useless when processing PB-level even EB-level data volume.

Data volume is tremendous, and it is difficult to judge data quality within a reasonable amount of time.
After the industrial revolution, the amount of information dominated by characters doubled every ten years. After 1970, the amount of information doubled every three years. Today, the global amount of information can be doubled every two years. In 2011, the amount of global data created and copied reached 1.8 ZB. It is difficult to collect, clean, integrate, and finally obtain the necessary high-quality data within a reasonable time frame. Because the proportion of unstructured data in big data is very high, it will take a lot of time to transform unstructured types into structured types and further process the data. This is a great challenge to the existing techniques of data processing quality.

Data change very fast and the "timeliness" of data is very short, which necessitates higher requirements for processing technology.
Due to the rapid changes in big data, the "timeliness" of some data is very short. If companies can't collect the required data in real time or deal with the data needs over a very long time, then they may obtain outdated and invalid information. Processing and analysis based on these data will produce useless or misleading conclusions, eventually leading to decision-making mistakes by governments or enterprises. At present, real-time processing and analysis software for big data is still in development or improvement phases; really effective commercial products are few.

No unified and approved data quality standards have been formed in China and abroad, and research on the data quality of big data has just begun.
In order to guarantee the product quality and improve benefits to enterprises, in 1987 the International Organization for Standardization (ISO) published ISO 9000 standards. Nowadays, there are more than 100 countries and regions all over the world actively carrying out these standards. This implementation promotes mutual understanding among enterprises in domestic and international trade and brings the benefit of eliminating trade barriers. By contrast, the study of data quality standards began in the 1990s, but not until 2011 did ISO published ISO 8000 data quality standards (Wang, Li, & Wang, 2010). At present, more than 20 countries have participated in this standard, but there are many disputes about it. The standards need to be mature and perfect. At the same time, research on big data quality in China and abroad has just begun and there are, as yet, few results.

4 Quality Criteria of Big Data

Big data is a new concept, and academia hasn't made a uniform definition of its data quality and quality criteria. The literature differs on a definition of data quality, but one thing is certain: data quality depends not only on its own features but also on the business environment using the data, including business processes and business users. Only the data that conform to the relevant uses and meet requirements can be considered qualified (or good quality) data. Usually, data quality standards are developed from the perspective of data producers. In the past, data consumers were either direct or indirect data producers, which ensured the data quality. However, in the age of big data, with the diversity of data sources, data users are not necessarily data producers. Thus, it is very difficult to measure data quality. Therefore, we propose a hierarchical data quality standard from the perspective of the users, as shown in Figure .

We chose data quality dimensions commonly accepted and widely used as big data quality standards and redefined their basic concepts based on actual business needs. At the same time, each dimension was divided into many typical elements associated with it, and each element has its own corresponding quality indicators. In this way, hierarchical quality standards for big data were used for evaluation. Figure shows a universal two-layer data quality standard. Some detailed data quality indicators are given in Table .

In Figure , the data quality standard is composed of five dimensions of data quality - availability, usability, reliability, relevance, and presentation quality. For each dimension, we identified 1-5 elements with good practices. The first four quality dimensions are regarded as indispensible, inherent features of data quality, and the final dimension is additional properties that improve customer satisfaction. Availability is defined as the degree of convenience for users to obtain data and related information, which is divided into the three elements of accessibility, authorization, and timeliness. The concept of usability means whether the data are useful and meet users' needs, including data definition/documentation, reliability, and metadata. Reliability refers to whether we can trust the data; this consists of accuracy, consistency, completeness, adequacy, and auditability elements. Relevance is used to describe the degree of correlation between data content and users' expectations or demands; adaptability is its quality element (Cappiello, Francalanci, & Pernici, 2004). Presentation quality refers to a valid description method for the data, which allows users to fully understand the data. Its dimensions are readability and structure. Descriptions of the data quality elements are given below.

We present a big data quality assessment framework in Table , which lists the common quality elements and their associated indicators. Generally, a quality element has its own multi-indicators.

5 QUALITY ASSESSMENT PROCESS FOR BIG DATA

An appropriate quality assessment method for big data is necessary to draw valid conclusions. In this paper, we propose an effective data quality assessment process with a dynamic feedback mechanism based on big data's own characteristics, shown in Figure .

Determining the goals of data collection is the first step of the whole assessment process. Big data users rationally choose the data to be used according to their strategic objectives or business requirements, such as operations, decision making, and planning. The data sources, types, volume, quality requirements, assessment criteria, and specifications as well as the expected goals need to be determined in advance.

In order to further quality assessment, we need to choose specific assessment indicators for every dimension. These require the data to comply with specific conditions or features. The formulation of assessment indicators also depends on the actual business environment.

Each quality dimension needs different measurement tools, techniques, and processes, which leads to differences in assessment times, costs, and human resources. In a clear understanding of the work required to assess each dimension, choosing those dimensions that meet the needs can well define a project's scope. The preliminary assessment results of data quality dimensions determine the baseline while the remaining assessment as a part of the business process is used for continuous detection and information improvement.

Then, the process enters the data quality assessment and monitoring phases. The core of data quality assessment is how to evaluate each dimension. The current method has two categories: qualitative and quantitative methods. The qualitative evaluation method is based on certain evaluation criteria and requirements, according to assessment purposes and user demands, from the perspective of qualitative analysis to describe and assess data resources. Qualitative analysis should be performed by subject experts or professionals. The quantitative method is a formal, objective, and systematic process in which numerical data are utilized to obtain information. Therefore, objectivity, generalizability, and numbers are features often associated with this method, whose evaluation results are more intuitive and concrete.

After assessment, the data can be compared with the baseline for the data quality assessment established above. If the data quality accords with the baseline standard, a follow-up data analysis phase can be entered, and a data quality report will be generated. Otherwise, if the data quality fails to satisfy the baseline standard, it is necessary to acquire new data.

Strictly speaking, data analysis and data mining do not belong to the scope of big data quality assessment, but they play an important role in the dynamic adjustment and feedback of data quality assessment. We can use these two methods to discover whether valuable information or knowledge exists in big data and whether the knowledge can be helpful for policy proposals, business decisions, scientific discoveries, disease treatments, etc. If the analysis results meet the goal, then the results are outputted and fed back to the quality assessment system so as to provide better support for the next round of assessment. If results do not reach the goal, the data quality assessment baseline may not be reasonable, and we need to adjust it in a timely fashion in order to obtain results in line with our goals.

6 Conclusion

The arrival of the big data era makes data in various industries and fields present explosive growth. How to ensure big data quality and how to analyze and mine information and knowledge hidden behind the data become major issues for industry and academia. Poor data quality will lead to low data utilization efficiency and even bring serious decision-making mistakes. We analyzed the challenges faced by big data quality and proposed the establishment and hierarchical structure of a data quality framework. Then, we formulated a dynamic big data quality assessment process with a feedback mechanism, which has laid a good foundation for further study of the assessment model. The next stage of research will involve the construction of a big data quality assessment model and formation of a weight coefficient for each assessment indicator. At the same time, the research team will develop an algorithm used to make a practical assessment of the big data quality in a specific field.

7 Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant No. 61170096, the Major Program of National Natural Science Foundation of China under Grant No. 71331005, the Shanghai Science and Technology Development Funds under Grant No. 13dz2260200, 13511504300, and the Department of Science and Technology of Yunnan Province under Grant No. 2012FD004.

References

1. Alan, F. K., Sanil, A. P., Sacks, J., et al. (2001) Workshop Report: Affiliates Workshop on Data Quality, North Carolina: NISS.

2. Data Application Environment Construction and Service of Chinese Academy of Sciences (2009) Data Quality Evaluation Method and Index System. Retrieved October 30, 2013 from the World Wide Web: http://www.csdb.cn/upload/101205/1012052021536150.pdf

3. Gantz, J., & Reinsel, D. (2012) THE DIGITAL UNIVERSE IN 2020: Big Data, Bigger Digital Shadows, and Biggest Growth in the Far East. Retrieved February, 2013 from the World Wide Web: http://www.emc.com/collateral/analyst-reports/idc-digital-universe-western-europe.pdf

4. Nature (2008) Big Data. Retrieved November 5, 2013 from the World Wide Web: http://www.nature.com/news/specials/bigdata/index.html

5. Science (2011) Special online collection: Dealing with data. Retrieved November 5, 2013 from the World Wide Web: http://www.sciencemag.org/site/special/data/

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Many significant technological changes have occurred in the information technology industry since the beginning of the 21 st century, such as cloud computing, the Internet of Things, and social networking. The development of these technologies has made the amount of data increase continuously and accumulate at an unprecedented speed.

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HIE has allowed for great benefits in reducing medication errors and preventing the possibility of repeat testing. It has made it easier for medical staff to access necessary patient data that is pertinent to their current health conditions. Having access to this information at all times helps reduce potential medication errors, duplicate testing, and limits readmissions. It also allows for improved diagnoses and treatment plans.

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I do believe that our electronic medical record systems have improved healthcare. It has become easier to access current medical records including, recent visit summaries and lab/imaging results. Patients are able to communicate with their physicians via patient portals through secure messaging. Electronic medical systems has helped improved the ability for patients to effectively manage their own care.

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Do electronic medical records improve quality of care?: Yes

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The intent of the HITECH Act is to increase patient quality care and outcomes. This has allowed for the implementation of meaningful use within EHR systems. This does present physicians with many challenges. One of the biggest issues in my opinion is the increase in pressure to meet ALL objectives/requirements to ensure receiving appropriate reimbursement. Although there has been an increase in patient outcomes, having that kind of pressure does take away from patient care and the physicians ability to be fully present with the patient.

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What is the HITECH Act An overview by athenahealth

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It is overwhelming to see how much of our lives are impacted by the "IoT". In almost every aspect of our lives smart devices have taken over. We are able to track our sleep, our activity, our heart rate, and even run an EKG if needed. Technology is slowly but surely taken over healthcare. I'm curious to see what more will be become of technology and how it will impact us in the future.

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Internet of Things (IoT) | What is IoT | How it Works | IoT Explained | Edureka

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A simple and straightforward video that simplifies the DIKW hierarchy and the importance each levels contains. Very interesting concepts related to the DIKW hierarchy. Going up the pyramid allows for synthesis of information and a platform to further build on. While going down the pyramid allows for the breakdown of information into individual components in order to be properly analyzed.

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A short and informative video - discussing the impact on big data in healthcare. Big data has individualized healthcare to gather information from patients of different ethnic minorities, elderly, children, and patients with multiple diseases. Data is generated from patient care studies to form successful treatment plans. The more the treatment plan is geared specifically to the patient and their condition, the greater likelihood that the patient will obtain better health outcomes.

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Embracing big data - the future of healthcare | Willem Herter & Wouter Kroese | TEDxSaxionUniversity

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Interoperability has allowed for easier access to patient records reducing the risk of repeat testing and imaging. The data collected helps guide what areas need to be improved based on the review of health trends. This could be anywhere from increasing patient safety to creating care models. Once identified, necessary changes and improvements can be implemented.

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What is interoperability in healthcare?

Interoperability in healthcare refers to timely and secure access, integration and use of electronic health data so that it can be used to optimize health outcomes for individuals and populations.

Health data has always been challenging to access and share in a secure manner. The nature of health data creates a paradox: It's difficult to share because it's sensitive and requires a high level of privacy and security, yet the inability to access it when it's needed has potential to cause significant harm. A lack of interoperability can result in an incomplete understanding of an individual's or population's health needs, which can lead to poorer outcomes and higher costs.

As populations around the world age and people live longer, interoperability and data sharing are going to become increasingly critical for delivering effective healthcare. In the United States, the Agency for Healthcare Research and Quality has estimated that two out of three older Americans have at least two chronic behavioral or physical conditions. Treatment for people with multiple chronic conditions currently accounts for an estimated 66% of US healthcare costs.

In their nationwide roadmap (PDF, 3.5 MB), the Office of the National Coordinator for Health Information Technology (ONC) says the use of electronic health records (EHRs) has dramatically increased in the United States. Many hospitals now have routine access to medical records and patient data from outside providers, yet less than half of hospitals are integrating the data they receive into individual patient records. So although access to vital clinical data has improved, there's still a lot of work that needs to be done to bring stakeholders together to create an integrated data ecosystem.

In addition to helping physicians and other healthcare providers see a more complete view of their patients, health data interoperability helps organizations across the healthcare industry. If health information systems were more integrated, then health plans would be able to develop a better understanding of their utilization rates and demand for services. Government service providers would be able to access population data to see trends and meet their citizens' needs. Also, life science organizations would be able to leverage robust datasets to drive faster, more informed research.

With better interoperability, organizations would be able to stop regarding individuals as a patient one day, a health plan member the next and a consumer of health apps the next. Instead, decision-makers across the industry would be able start looking at how people access and use health information, regardless of its source, to drive better models of care, pursue better patient safety and improve experiences for the people they serve.

Healthcare interoperability benefits

Better care coordination

With access to data, clinicians have an easier time accessing a patient's most important health information, which can lead to fewer repeat tests, prevent inadvertent treatment interactions and reduce miscommunications.

Higher performance

When data can be combined more easily, it can also be analyzed more easily. Interoperability makes it possible for organizations to study data trends, past performance and make data-driven improvements in patient care and other areas.

Better experiences

Data interoperability can reduce the amount of redundant administrative work both within and outside organizations, creating a more satisfying experiences both for employees and for those they serve.

Fast Healthcare Interoperability Resources, also known as FHIR, is an open source standards framework for healthcare data that builds off of a previous standards framework called HL7. FHIR was created to make it easier for healthcare data to move from one system to another.

FHIR organizes data into resources like patient, conditions, medications and provides a standardized structure for how that data is organized and interpreted by different computer systems or applications. FHIR can also be used to structure financial data and workflow data, such as claims information, appointments and more.

Many major healthcare systems in United States have already adopted FHIR in their health IT practices. Medicare's Blue Button 2.0 initiative is based on FHIR and the Veteran's Affairs Administration has developed a FHIR platform called Lighthouse. Both provide platforms for patient access to healthcare information through FHIR.

The National Coordinator for Health IT in the United States has made FHIR a key part of the nationwide interoperability roadmap (PDF, 3.5 MB). New government regulations and interoperability rules are requiring wider adoption of FHIR, so it's vital for healthcare organizations, especially for ones that receive payments for Medicare or Medicaid services, to understand FHIR and incorporate it into their interoperability strategies.
Learn more about IBM FHIR Server

Modern consumers have high expectations when it comes to accessing information, and many now expect to have quick and continuous access to records about their health and care. That's why many healthcare organizations are building health information exchanges (HIE), which are specialized networks that rely on interoperable systems to share electronic health information seamlessly and securely.

Even though the adoption of EHRs was a good first step towards building HIEs, there are still many challenges that need to be overcome to achieve the level of interoperability needed to obtain the full benefits of HIEs. These challenges include:

Lack of standardization: Although standard record formats like FHIR and HL7 are becoming more common and new regulations are pushing EHR vendors to provide APIs that support interoperability, many providers and healthcare systems use customized EHR systems that can be hard to convert to a standard format and shared with others.

Security: Healthcare organizations can find it hard to balance the need for health information to be accessible with the need to secure sensitive information and maintain patient privacy, especially with the increasing number of cybersecurity attacks on healthcare systems (PDF, 1.3 MB).

Consent: By building digital health systems in which health information flows freely from provider to provider, it's not always clear when patient consent is needed and what level of consent is needed. Healthcare organizations are understandably cautious about this and tend to error on the side of not sharing information.

Professional burdens: When new tools for recordkeeping are introduced, people need to learn how to use them. Healthcare professionals are often wary about new systems since EHR systems often do a better job supporting administrative and billing workflows than clinicians' needs.

These challenges are not insurmountable. With advances in cloud computing, especially hybrid cloud, it's become easier for organizations to move and secure data in a consistent way. Cloud environments provide opportunities for organizations to build data pipelines that standardize data to an industry-standard format like FHIR and provide secure access to people who need it - whether they're payers, providers or patients themselves.

Learn about enabling health data interoperability
Read the article

In 2016, President Obama took a major step to solve information blocking by signing the 21st Century Cures Act, which requires EHR systems to provide a patient-facing API to maintain their federal certification. In 2020, the Centers for Medicaid & Medicare Services (CMS) issued a rule requiring health plans and providers who receive federal funds from their programs must take steps to make health information easier to access.

Most of the policies focus on encouraging payers to implement application programming interfaces (APIs) and data exchanges that provide secure access to their provider directories, patient claims data and other resources that would make it easier for data to be accessed in a timely manner. This access would give patients, as well as providers and payers, a more holistic view of the care people receive and support broader public health efforts.

See how mandatory interoperability standards will affect your organization.
Learn more about the CMS regulations

While many healthcare experts and leaders agree that better interoperability would improve healthcare overall, there are common challenges that healthcare organizations tend to face as they work on making their data and systems more interoperable. Let's explore those challenges and how organizations can overcome them:

Disjointed coordination
Improving interoperability requires strong coordination between different organizations, regulators and leaders as well as coordination within organizations. Regulators provide standards and rules for healthcare organizations to follow but organizations that want to be proactive about interoperability should consider creating a dedicated interoperability strategy and make interoperability planning a priority.

Limited budgets
Not all organizations have the financial or technical resources they need to invest in the technical resources needed to build a truly interoperable system. There may be some government grants available to update health records systems, so organizations should check to see if they're eligible. Many cloud vendors also offer pay-as-you-go payment models that could make technical expenses more affordable and predictable.

Diverse technology needs
Organizations need to follow different rules and regulations depending on what type of care they provide and where they're located, so many organizations have highly customized data. Organizations can help connect different internal and external systems through a hybrid cloud platform that gives them options to combine and integrate their data without sacrificing the customizations they need.

Legacy systems
Healthcare organizations with older legacy systems face the dual challenges of modernizing their systems while also meeting interoperability requirements. Organizations can meet both goals using a hybrid cloud approach to extract data from legacy systems and make it more accessible for modern applications and programs. This approach gives organizations the option to keep data moving while they work on updating their systems.

Interoperability for healthcare has four different levels that have been defined by informatics experts and the Healthcare Information and Management Systems Society (HIMSS). Some of these levels can be achieved today with existing healthcare IT architecture and IT systems, while others will require innovation and additional developments in patient-centered technology.

These levels include:
Foundational
This level of interoperability, also known as simple transport, is the most basic. Data is securely transferred from one system or device to another without interpreting the data or transforming it into a particular format. For example, a nurse downloads a PDF file of a patient's latest lab results from the lab's results portal, then manually enters the data into the patient's health record.
Structural
When structural interoperability, or structured transport, is achieved, all the data is standardized to a particular format so it can be interpreted by multiple systems or devices. This data is organized in a particular order so the receiving system can automatically detect specific data fields. Data standards like FHIR and HL7 provide structural interoperability so records can be consistent, centralized and easy to move between systems.
Semantic
The semantic level of interoperability, or semantic transport, involves exchanging data between systems with completely different data structures. Imaging systems provide a simple example - there are many specialized DICOM and non-DICOM formats for images. With semantic interoperability, images could be transferred from one system to another, interpreted and incorporated into the new system regardless of the image's original format or source. Yet determining what data to collect and transfer can be difficult, since systems have different ways of presenting the same information. For that reason, some experts argue artificial intelligence will be needed to achieve full semantic interoperability.
Organizational
Organizational interoperability involves the seamless exchange of data between various organizations with different requirements, regulations and goals. To achieve this level of interoperability, there must be policy and governance innovations as well as technological innovations to ensure consent, security and integrated workflows move smoothly between different groups. Though some experts say semantic is the highest level of interoperability, others say it is organizational interoperability.

Discover the stages of maturity along the path to interoperability
Explore the stages

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Interoperability in healthcare refers to the secure access and integration of electronic health data so it can be used to optimize health outcomes.

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Meaningful use and interoperability work hand in hand. Each aspect has allowed for improving clinical decisions. Meaningful use has allowed for the integration of patient centered healthcare. It brings together the processes and workflow that involves healthcare practitioners to utilize pertinent electronic information. In doing so, they are then able to improve delivery of care and allow for better overall patient outcomes.

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Health informatics has become to play a crucial role in identifying the impacts of COVID-19 on healthcare. A lot of misinformation is constantly available to the public whether its through the news or social media. Utilizing the T.I.G.E.R initiative can help individuals make informed health care decisions based on true/accurate information. The more accurate information is released the better overall healthcare decisions can be made.

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TIGER scholar will examine COVID-19 misinformation - News Center - The University of Texas at Arlington

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The Healthcare Information and Management Systems Society (HIMSS) selected a graduate researcher at The University of Texas at Arlington for its competitive Technology Informatics Guiding Education Reform (TIGER) Scholars Informatics Internship.

Ana Aleksandric, a doctoral student in the Department of Computer Science and Engineering, received a one-year internship to enhance her knowledge of health informatics and related skill sets. She is one of only two global internship recipients for the 2021-22 academic year.

As a graduate research assistant under the supervision of co-advisors Shirin Nilizadeh, assistant professor of computer science and engineering, and Gabriela Wilson, professor of kinesiology and co-director of the Multi-Interprofessional Center for Health Informatics (MICHI), Aleksandric investigates where COVID-19 vaccination misinformation and disinformation are likely to occur.

"Health informatics is becoming even more important now because of the pandemic," Aleksandric said. "Collecting data only from hospitals is not enough to understand what is happening at the community level."

Analyzing Twitter activity, Aleksandric gathers information about how social media enhances misinformation and the impact it has on public health. Through this internship, she hopes to provide information that will promote an increase of U.S. COVID-19 vaccination rates and make people aware of resources where they can find accurate health-related information.

"In my research with Dr. Wilson, we cross-reference where misinformation occurs with geographic and demographic areas that have a low health-literacy rate and high social vulnerability index," Aleksandric said. "When we put these data together, it allows public health advocates to gain the knowledge needed to intervene against misinformation and assist the public in making fully informed health care decisions."

As a TIGER Scholars Informatics intern, Aleksandric will be paired with mentors based on shared research interests, develop a case study focused on TIGER's scope of work as it relates to her doctoral research and create resource content related to emerging trends in health informatics.

"This is an opportunity for Ana to really go beyond what is offered in the classroom," said Wilson, who is also the U.S. co-chair of the TIGER international taskforce. "Students learn by doing. As an intern with TIGER, she will connect with professionals in health care, receive expert mentorship and expand her knowledge of health informatics."

The TIGER initiative from HIMSS provides the global health workforce with innovative informatics/eHealth tools and resources to transform health for all. The global TIGER network collaborates to integrate informatics/eHealth into health care education, certification, practice and research through an inclusive, interdisciplinary, intergenerational approach.

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TIGER scholar will examine COVID-19 misinformation

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The Healthcare Information and Management Systems Society (HIMSS) selected a graduate researcher at The University of Texas at Arlington for its competitive Technology Informatics Guiding Education Reform (TIGER) Scholars Informatics Internship. Ana Aleksandric, a doctoral student in the Department of Computer Science and Engineering, received a one-year internship to enhance her knowledge of health informatics and related skill sets.

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COVID has been a stressful experience for everyone. Our healthcare has taken a great hit and so have have healthcare personnel. It has changed healthcare in many different ways and led to burnout. Implementing QSEN competencies may be beneficial in increasing both staff and patient safety. Now that employee shortages have increased greater interventions need to take place to assure that all patients and staff are protected.

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The past year of global COVID-19 pandemic renewed the attention on the inherent quality and safety risks in healthcare. Fragmentation in delivery services, poor coordination across transitions in care, and ineffective communication among providers and patients were magnified as healthcare delivery underwent sudden pivots to address unprecedented healthcare demands. The scope of unknown vulnerabilities to system safety and quality, the demands of processing the explosion of new information, and the complexity of managing safety risks to patients and workers were only some of the sudden threats as the virus spread across boundaries.

COVID-19 illuminated both progress and lingering gaps in patient safety and quality over the past twenty years since the deep gaps in patient care quality and safety became public knowledge. In the United States (US), the publication of the Institute of Medicine's initial report of the startling statistics of preventable patient harm, To Err is Human, was met with alarm by both clinicians and consumers [ 1]. Still, two decades later, we do not know the full extent of the burden of preventable patient harm because preventable harm remains a difficult concept to categorize definitively, healthcare staff are often reluctant to report concerns and incidents for fear of retribution, and there is no aggregate reporting of preventable patient harm.

Even before COVID-19, the WHO called preventable harm a global crisis [ 2]. Few countries have a national plan to transform healthcare to accurately identify, report, redesign, and mitigate preventable patient harm, briefly defined as harm to the patient unrelated to the patient's reason for seeking medical care. Data from the most comprehensive global quality report, Crossing the Global Quality Chasm [ 3] confirm patient safety is far from reaching zero, the goal for many safety organizations. Primary recommendations for transforming healthcare education and delivery systems include implementing new educational strategies to focus on quality and safety competencies, fostering systems thinking, reimaging leadership development, and creating learning organizations. A brief review of recent literature traces progress, but questions remain. Where are we globally in assuring all nurses have the opportunity to achieve the competencies essential for nurses in developing a mindset for quality and safety in their practice? What educational approaches best prepare nurses for the transition to safe quality practice environments? How do healthcare delivery organizations hardwire quality and safety into organizational culture? What have we learned from the global pandemic to improve safe quality patient care?

1. Quality and Safety Education for Nurses (QSEN): an evidenced based Competency Approach

Transforming healthcare to eliminate preventable harm begins with a competent workforce. The Quality and Safety Education for Nurses (QSEN, www.QSEN.org) project developed a robust framework used in many countries. It launched at the University of North Carolina at Chapel Hill and became the US national plan for nurses to improve quality and safety in patient care [ 4]. QSEN defined six competencies nurses must achieve to be able to lead and transform practice to improve patient care quality and safety.

Patient-Centered Care: Treats patients and their family with respect, engaging them in decisions about their care, and honoring their culture, values and beliefs.

Teamwork and Collaboration: Skillfully connecting, coordinating, and communicating across all disciplines and care team members including the patient and family to assure accurate, timely and effective information sharing, informed decision-making and smooth transitions between providers.

Evidence Based Practice: Assuring latest evidence guides practice interventions that also support patient values and preferences.

Quality Improvement: Applying a spirit of inquiry to question processes for best practices, measuring actual practice to compare with desired benchmarks, and implementing appropriate system improvements.

Safety: Identifying and alleviating conditions and processes in the healthcare environment that contribute to preventable harm.

Informatics: Participating in design and application of informatics and technology to support decision-making, data management, and information sharing and retrieval.

Consistent with competency development, evidence based objectives for knowledge, skills, and attitudes (KSAs) were first developed to guide pre-licensure development of each competency for integrating into nursing curricula [4]. In 2009, higher-level objectives for the KSAs were adopted to guide graduate nursing academic programs and nursing professional practice standards [ 5]. This work is translated into Swedish, Korean, Italian, and Chinese through the Quality and Safety in Nursing: A Competency Approach to Improving Outcomes [ 6].

To assure nurses are prepared for the transition to practice, these six competencies are embedded in the standards for nursing schools in the US. Globally, several studies demonstrate patient safety and quality are unevenly applied in nursing education and practice. Kirwan et al. [ 7] reported patient safety is incorporated in nursing education in 27 countries with less integration in European Union countries. Furthermore, unlike the US, most countries lack regulatory guidelines on how patient safety and quality are integrated. Steven et al. [ 8] demonstrated cross-country collaboration could speed change through sharing patient safety student learning events.

HOW we teach is as critical as WHAT we teach for learners to develop a practice based safety mindset. Educator development for effective educational redesign is an imperative [ 9, 10], and is compounded by the emphasis in online learning due to COVID-19. Interactive classrooms [ 11] built around unfolding case studies [ 12, 13] encourage learners to apply safety competencies in the security of the learning environment [ 14]. Faculty serve as facilitator and coach with students working in small groups or in online virtual breakout rooms.

Learning from case studies and other forms of narrative pedagogy derives from reflecting on the unfolding case to apply knowledge, recall previous experience, and examine the impact of decisions on the patient [ 15]. Educators foster clinical judgment by using instructional strategies that help develop reflection among nurses to examine, reconsider, and learn from critical situations [ 16]. Reflective Practice ( Fig. 1 ), systematically examining experiences to learn better ways to respond, guides how we learn from experience. Self-awareness developed through reflective practice develops the mental model to ask questions about safety of high-risk procedures, monitor processes that don't work reliably, and become aware that all actions have consequences. A basic process for reflection in Fig. 1 begins with mindfulness to recognize critical experiences with potential for improvement:

2. Learning organizations: a systems approach to safety and quality

The first US national patient safety plan [ 17] cites areas of organizational progress. These include working interprofessionally for improved collaboration in patient care [ 18], more transparent reporting systems to capture near misses and adverse events [ 19], more integration of quality and safety competencies across health professions education, and strategic efforts to actively enlist patients in their care [ 20]. Yet, preventable harm remains pervasive. The report, like the 2018 global report [ 3], reveals system change is the most critical area of improvement to promote learning organizations focused on inquiry as the basis for improvement. Leaders at all levels of the system hardwire safety as a basic value in creating safety culture and supporting their workers. While individual competency development is essential, safety outcomes ultimately depend on how each worker lives patient safety and quality with every patient, every interaction and procedure, every day.

New concepts form the science guiding safety cultures in healthcare organizations [ 21]. Because nurses are continuously present with patients and their families, they are key to establishing an organizational safety culture. Developing a mindset for safety begins with inquiry, the presence of mind with a willingness to ask questions about practice, that is, whether practice is based on best evidence with openness to constructive feedback.

Systems thinking is the framework for implementing quality and safety competencies. Systems are a set of interdependent components that interact to achieve a common goal [ 22]. Creating a safety culture that embraces transparency enables the organization, whether, in-patient or out-patient primary care, to learn from, not hide, near misses and preventable harm. Just Culture encourages learning from near misses and adverse events through a reporting system with systematic analysis, emphasizing accountability not individual blame [ 19, 20].

Safety cannot rely on a piecemeal set of activities. Improving safety requires a system culture and mindset that guides a proactive system wide plan to provide care that is safe, reliable and free from harm. Total system safety is interdependent, collaborative and coordinated. High reliability organizations simplify and standardize processes to achieve reliable results. For example, following an evidence-based protocol exactly when inserting and managing central lines has resulted in sharp reductions in infection.

Healthcare workers take pride in working in organizations focused on quality and safety. Workers experience satisfaction in doing work well and are supported or restrained by their work environment. New evidence from research on human factors and the value of psychological safety for workers-feeling free from bullying and incivility-is clarifying the need to feel supported in speaking up about unsafe care [ 23]. New studies examine the impact of human factors on how workers complete their work, including fatigue, interruptions, distractions, broken processes, unreliable equipment, and other human workplace interfaces [ 24].

3. 2020 and beyond: safety lessons from the global pandemic

COVID-19 highlighted the critical inclusion of the QSEN competencies in daily practice and challenged the overall safety for workers and patients. The cessation of family visiting hours put nurses and other healthcare providers as the caring connection between isolated patients with COVID-19 and family members relegated to virtual healthcare visits. Nurses were recognized for their innovative approaches to patient-centered care, a core QSEN competency. Nurses applied competency in evidence-based practice for quick assessment and implementation of rapidly emerging new evidence on treatment and prevention, often relying on skills in informatics. Quality improvement projects evaluated various treatments and monitored infection management. Teamwork and collaboration of all healthcare workers in all settings established a pattern for sharing knowledge, resources, and skills to assure safe quality care delivery [ 25]. Daily safety briefings assessed bed availability, coordinated emergency responses, allocated equipment shortages and monitored worker safety. Research continues to examine changes in healthcare that will extend beyond the pandemic, such as improvements in telehealth, increasing healthcare access for all [ 26]. Still, safety ultimately relies on shared accountability between the system and each worker in the healthcare delivery organization to engage in and build a culture of safety with high reliability, human factors and transparent reporting.

Ricciardi [ 27] issues a call to action for all healthcare workers to commit to the goals of patient safety and quality across the continuum of care, from ambulatory primary care and health promotion to the highest acuity level. Patient safety is a team activity requiring all of us to work together, locally and globally, to build safer systems, eliminate patient harm, and ensure worker safety. Let's reflect on the experiences of the pandemic for lessons learned to achieve equitable safe quality healthcare every patient, every time.

Footnotes

Peer review under responsibility of Chinese Nursing Association.

References

1. Institute of Medicine . National Academies Press; Washington, DC: 2000. To err is human: building a safer health system. [ Google Scholar]

3. National Academies of Sciences . National Academies Press; Washington,D.C.: 2018. Engineering, and medicine. Crossing the global quality chasm: improving health care worldwide. [[ CrossRef] Google Scholar]

6. Sherwood G, Barnsteiner J. editors, second ed.. Quality and safety in nursing: a competency approach to improving outcomes. Hoboken, NJ: Wiley.

8. Steven A., Tella S., Turunen H., Flores Vizcaya-Moreno M., Pérez-Cañaveras R.M., Porras J. Shared learning from national to international contexts:a research and innovation collaboration to enhance education for patient safety. J Res Nurs. 2019;24(3-4):149-164. doi: 10.1177/1744987118824628. [[ CrossRef] Google Scholar]

16. Armstrong G., Horton-Deutsch S., Sherwood G. Reflection in clinical contexts: learning, collaboration, and evaluation. In: Horton-Deutsch S., Sherwood G., editors. Reflective Practice: transforming education and improving outcomes. second ed. Sigma Theta Tau International Press; Indianapolis: 2017. [ Google Scholar]

17. National Steering Committee for Patient Safety . Institute for Healthcare Improvement; Boston, Massachusetts: 2020. Safer together: a national action plan to advance patient safety.[ www.ihi.org/SafetyActionPlan Google Scholar]

18. Interprofessional Education Collaborative Expert Panel (IPEC) Interprofessional Education Collaborative; Washington, DC: 2016. Update: core competencies for interprofessional collaborative practice: report of an expert panel. [ Google Scholar]

19. Yang Y., Liu H.P. The effect of patient safety culture on nurses' near-miss reporting intention: the moderating role of perceived severity of near misses. J Res Nurs. 2021;26(1-2):6-16. doi: 10.1177/1744987120979344. [[ CrossRef] Google Scholar]

27. Ricciardi R. Perspectives: envisioning healthcare quality and safety in 2030. J Res Nurs. 2021;26(1-2):168-175. doi: 10.1177/1744987121992911. [[ CrossRef] Google Scholar]

Articles from International Journal of Nursing Sciences are provided here courtesy of Chinese Nursing Association

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Quality and Safety Education for Nurses: Making progress in patient safety, learning from COVID-19

# data[].attributes.attachment_link.description
The past year of global COVID-19 pandemic renewed the attention on the inherent quality and safety risks in healthcare. Fragmentation in delivery services, poor coordination across transitions in care, and ineffective communication among providers and patients were magnified as healthcare delivery underwent sudden pivots to address unprecedented healthcare demands.

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It is interesting to see how much of an impact digital health has had on nursing and on healthcare. Even through the pandemic there have been many changes to our healthcare system today. Our healthcare relies on this digital transformation. I truly believe that even when this pandemic is over a lot of what has already been implemented will continue to be used in future healthcare.

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Digital innovation is becoming increasingly critical to clinical practice, not just as a workforce management tool, but as a foundation to change the way care is delivered. Healthcare IT News spoke to senior nursing informatics officer for Abu Dhabi Health Services, Hana Abu Sharib and professor in health and human services informatics at the University of Eastern Finland, Dr Kaija Saranto, to find out exactly how nurses and midwives are leading the digital transition.

Nurses and midwives have played a key role in leading the digital and informatics arena to improve patient care, particularly in the COVID environment where the quality of digital care delivery has come to the forefront of healthcare and been under more scrutiny than ever. In September, they will be speaking at the HIMSS & Health 2.0 European Digital Event, in the 'Nurses 2.0' session, where they will expand on some of these key issues in greater detail.

"During the pandemic, nurse informatics teams were involved in training for different EMR applications and devices for nurse shifting cross settings (ED-OPD-Inpatient) based on operational needs. They were also very actively involved in building the input forms for nursing and addressing the patient surge during the crisis. From a data perspective, they were validating the reports and dashboard," explains Abu Sharib.

Saranto adds: "Recording has been more crucial, and a variety of mobile applications have been tested and partly implemented to control the outbreak of the virus. I assume that both nurses and midwives have worked under a heavy pressure to guarantee safety and continuity of care."

With the recent growth of digitalisation, nurses and midwives are continuing to lead the digital transition and using informatics to improve patient care.

Saranto said: "The most important or effective key to success is education. I believe that there still is a lot of nurses who do not have proper knowledge and skills to use health information technology tools. Nursing and midwifery programmes have integrated health informatics into their curricula, but it concerns newcomers in the field and the 40 plus age groups are left behind. Thus, in-service training is badly needed."

Nurses and midwives recognise the importance of this transition and the value of health informatics education and training, as Abu Sharib commented: "RN level and the nursing informatics speciality roles are adopted by many nurses and they are addressed for many clinical documentation requirements or technology adoption as they have advanced knowledge in that field. Nurse informatics is now classified under nursing as one of the specialities acknowledged by senior management."

In Finland, technology is helping to bring healthcare to the homes of the ageing population, and nursing has subsequently been pushed to the forefront of this community-based care. Finland recently adopted a national information system called Kanta, which includes electronic prescriptions, a patient data repository and an online system which allows patients to view their health information.

Saranto explains: "The situation in Finland is partly easy, but also complicated when we already have 100% EHR coverage in the country. Thus, moving from electronic to electronic systems creates a lot of challenges around how to convince staff that the new system is better. So, the old way was a step-by-step or phased implementation, and now we have moved to big-bang implementations."

The importance of nurse involvement was also highlighted by Saranto, as a way to optimise cooperation between health professionals: "Whatever the model, I think nurses and midwives should be involved right from the beginning and I would like to stress the importance of cooperation between all expert groups.

"Too often nurses and midwives are invited too late to participate in projects. This often leads to misunderstanding and neglects the relation of information flow and work processes. I believe that participation in the HIT projects will also facilitate health informatics (HI) skills, as the need for education becomes real and is attached to your daily practice."

When discussing the future of innovation and informatics, Saranto said: "Multidisciplinary education is not an innovation, but it is far too seldom used as a model for basic or advanced HI education, although we have encouraging outcomes.

"I have always found IT as a tool to support practice. I hope that those coming to the healthcare arena also have at least minimal knowledge and skills from the context.

"Often in multidisciplinary groups, concepts and terms can cause severe misunderstanding. For example, when planning interfaces. I believe that this could lead to more efficient implementations and satisfied users."

Abu Sharib concludes: "My hopes are to build the capacity on nurse informatics designated roles in the nursing workforce, and the improvement of nursing informatics use in the innovation of nursing practice based on process enhancement and improving patient outcome."

Learn more at the HIMSS & Health 2.0 European Digital Event taking place on 7-11 September 2020.

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Nurses 2.0 - The digital transformation of nursing

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Digital innovation is becoming increasingly critical to clinical practice, not just as a workforce management tool, but as a foundation to change the way care is delivered.

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The expectation of nurses and technology has grown over the years. Now more than ever nurses are expected to obtain a skill set that allows for appropriate usage of technology. It is important for nurses to be able to navigate through EHR systems to aid in the coordination of care. This helps improve patient outcomes and quality of patient care.

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Nursing is increasingly becoming as "high tech" as it is a "high touch" profession.

Today's nurses have more technology at their disposal than any nurses ever before, and as one might expect, it's considerably improving patient care. #Nursing explains all the necessities as well as the advantages and benefits of selecting nursing as a career.

Today's nurses have more technology at their disposal than any nurses ever before, and as one might expect, it's considerably improving patient care. #Nursing explains all the necessities as well as the advantages and benefits of selecting nursing as a career.

One area where nurses are putting technology to use is in informatics. Officially known as the study of information, in the world of health care, health informatics is the management of health information. Using electronic medical records, devices that collect health information electronically, and other electronic information standards, health informatics nurses are responsible for managing, interpreting, and communicating the data that comes in and out of health care facilities, all with one primary purpose: Improving the quality of patient care.

But how does that happen, specifically? How are nurses using informatics as a way to improve the care they - and their colleagues - provide to patients? As it turns out, there are several key ways that informatics is part of that effort.
Improved Documentation
Documentation has long been considered an important part of the nursing profession, but it's more vital than ever to the delivery of quality care. While the theory and practice of nursing, the standards of nursing practice, legal and ethical considerations, and other points that are taught in advanced nursing programs all influence the practice of nursing, it's information, and specifically, electronic documentation, that is having the greatest influence on modern nursing.

Modern nursing care is driven by individual patient needs and history - information that is collected and organized in electronic patient records. By documenting a patient's condition, and sharing that information electronically, nurses are able to more effectively manage care, and by extension, improve the quality of that care.

A great deal of documentation takes place automatically thanks to connected devices, which collect specific information in real time and transmit it to patient records. By looking at the documentation of a patient's condition over time, nurses can make better decisions about how to provide care and when changes or adjustments need to be made.
Reduced Medical Errors
Patient safety is a primary concern of any health care provider, and nurses are often on the front lines of ensuring that their patients are kept safe and preventing medication errors, misdiagnoses, falls, and other problems. Health informatics provides important data that can prevent these errors; for example, an electronic record can provide information about a possible dangerous medication interaction or allergy that might not otherwise be immediately apparent. Armed with data, nurses can make quick decisions that keep their patients safe.

Nurse practice error allegations and patient complaints are one of the leading causes of nursing board license investigations, disciplinary actions and malpractice lawsuits. Complaints have been surging in recent years due to the ease of filing complaints online. Health informatics allow standardizing many patient care decisions which makes it easier for healthcare organizations to limit their liability and assure compliance with the Nursing Practice Act and other medical care standards.

In fact, in a study by the Agency for Healthcare Research and Quality (AHRQ), a majority of nurses reported that when they have access to EHRs, they have fewer problems with getting patients ready for discharge, fewer medication errors, and better quality of care. And when it comes to transfers between departments, nearly 15 percent of the nurses surveyed reported that information was more likely to be shared and less likely to "fall between the cracks" when electronic systems are used.
Reduced Costs
Medical errors cost nearly $40 billion every year, and many of those errors are preventable with informatics. Not only does information provide nurses with alerts to avoid errors, it also helps to automate certain tasks, such as doctor note templates creation, both improving nurse productivity and preventing some of the costs associated with healthcare.
Improved Coordination of Care
Nurses are often called upon to help coordinate the care of their patients. This often means relaying information from physicians, therapists, pharmacy, billing, and more, both during care and at discharge. Without all of the necessary information, patient care could suffer. Informatics improves the coordination of this information, allowing nurses to give their patients all of the information they need, improving both outcomes and the satisfaction with care.

While there are some who criticize the use of informatics and technology to help manage care, arguing that it makes health care more impersonal, with the provider-patient relationship being replaced with data and algorithms, there is actually overwhelming evidence that informatics actually improves care. Nurses who are trained in this technology and how to best incorporate data into their workflow are often more productive and able to provide better care than ever before.

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by Scott Rupp advanced nursing programs, coordination of care, health informatics nurses, healthcare informatics, patient safety

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How Nurses Are Using Health Informatics to Improve Patient Care

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Nursing is increasingly becoming as "high tech" as it is a "high touch" profession. Today's nurses have more technology at their disposal than any nurses ever before, and as one might expect, it's considerably improving patient care. #Nursing explains all the necessities as well as the advantages and benefits of selecting nursing as a career.

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This article grabbed my attention because it will be interesting to see what the future holds in healthcare. This article mentioned voice dictation. I worked in a clinic where this feature is being used. However, there were many issues with spelling errors and incorrect data reported that added additional work to the provider. They would have to go in and try to remember exactly what they intended to say. I foresee this being convenient for physicians but also know that it can cause some setbacks if the dictation is not completed correctly.

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UNE Online College of Graduate and Professional Studies

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Health informatics, which is the intersection of people, technology, and data to improve the safety and quality of patient care, can be found in many forms. A few examples include patient portals, electronic medical records (EMRs), telehealth, healthcare apps, and a variety of data reporting tools. But what comes next? We assembled a list of seven examples of health informatics technologies or advances that are either being explored or improved in the field right now.
Learn more about a career in health informatics with our infographic
1. Voice Dictation

Now that healthcare organizations have moved their medical records to digital systems, health informaticists are diving into the possibilities of improving artificial intelligence voice recognition to digitally transcribe healthcare providers' notes and medical orders in real time. This could drastically reduce the amount of time spent on data entry and increase the amount of time that healthcare providers can spend interacting directly with their patients.

At the moment, there is still some resistance to incorporating voice transcription due to its error rate of about 7.4%. In a study, this error rate was connected to a lack of proofreading by the clinician. After proofreading, the error rate in the same study fell to 0.3%. (Zhou, Blackley & Kowalski, 2018) Detailed proofreading also takes up a decent amount of time, so it will take insightful and innovative health informaticists to find a voice recognition solution that is both completely accurate and time efficient.

2. EHR Alert Optimization

Instantaneous alerts can act as an incredible useful tool to improve care by notifying a healthcare provider about a number of issues: This may include screenings, drug interactions, chronic disease management, overdue lab tests, prescription recommendations, admission, discharge, transfer (ADT) notifications, and more. However, when everything creates an alert, none of them seem important. "Alert fatigue," when healthcare providers become desensitized to alerts because there are so many that go off so often, is just as dangerous as not having the alerts in the first place.

Informaticists are working to winnow down the number of EHR alerts to make them more useful. Some proposed approaches include customizing alerts to each patient, tiering alerts by severity, or changing the design of alerts by color or format to be more or less intrusive as needed.

3. Predictive Analytics

In healthcare, predictive analytics studies a massive amount of data to find indications of common symptoms, diagnoses, workflows, and other outcomes. One example is a study done in which an artificial intelligence algorithm analyzed the speech patterns of children, compared their pitch, inflections, and words against data from other clinical interviews and parent questionnaires, and was able to identify diagnoses of depression 80% of the time. (McGinnis et al, 2019)

While this technology is still in its early stages, it is incredibly versatile and could help healthcare professionals optimize their care decisions. Predictive analytics can also be found within our next examples of health informatics technology.

4. Syndromic Surveillance

Using both predictive analytics and EHR alerts, real-time patient surveillance can instantly identify potential adverse events. Instantaneous clinical decision support tools can then take that analyzed surveillance and apply it in real time to signal healthcare professionals to take immediate or precautionary action.

Currently, the implementation of real-time clinical decision support tools is being hampered by poor usability, the aforementioned alert fatigue, and the program's understanding of different end-users' needs. (Mann et al, 2019) The latter two factors will likely be addressed as predictive analytics algorithms and EHR alert optimization improve. In the future, these tools may lower health costs and complications through faster, more customized care that can reduce the potential for human error, and even improve public health-for instance, by instantly sharing diagnoses of communicable diseases. (Heekin et al, 2018)
Read more: Analysis of a Health Informatics Job Description
5. Clinical Image Capture

Just like digital cameras can now capture moments in intense detail, medical imaging has improved by leaps and bounds-and the files have similarly increased in size. After a patient goes through an MRI, CAT scan, X-ray, or other procedure, storing or retrieving imaging in the EHR can be difficult. Each image may have a different intent or function, and they may come from different technologies, so they may be saved in different file formats or locations. They also need to be matched to the correct patient, and be saved and accessed only by authorized users.

Informaticists are currently trying to balance these many factors. An ideal imaging system would be able to easily capture any type of image and share it in a single, secure yet easily-accessed file that is also connected to the rest of the patient's record.

Looking farther into the future, imaging data could be combined with predictive analytics to help healthcare professionals find patterns that could lead to improved diagnoses.

6. Secure Texting

Patient portals have brought a host of accessibility to patients and providers, opening up patients' access to their own records, prescriptions, and lab results, and creating a new line of communication between the patient and the provider.

However, logging into a portal on a regular basis can be difficult for many patients. Providers have started to look into alternatives to notifying patients through secure texts, which can be automated and thus reduce time that providers spend on the phone or that patients spend on the computer trying to access their portal. Of course, the hard part is making sure those texts remain secure!

7. Shift to the Cloud

It may seem like everyone is moving their data to the "cloud" these days, including photos, music, movies, and now health records. The benefits of cloud computing over an on-site server are fairly straightforward: there's no hardware or software installation, updates are automatic, and authorized users can access the system anywhere they can get an internet connection. When combined, these benefits can also mean huge cost savings.

Of course, just because the data is in the cloud doesn't mean it's automatically perfect. Health informaticists are needed to set up cloud-based systems and monitor them for security, connectivity, correct data input/output, and outages.

Do these health informatics examples sound right up your alley?

Through these examples of health informatics technology, patient health and healthcare providers' work are being improved little by little. While many technologies have been proposed or implemented to a point, the healthcare field needs more health informatics professionals to bring them to full fruition. For more information:
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Tags: Graduate Programs in Health Informatics | Health Informatics | Master of Science in Health Informatics

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7 Examples of Health Informatics Coming in the Near Future

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Health informatics, which is the intersection of people, technology, and data to improve the safety and quality of patient care, can be found in many forms. A few examples include patient portals, electronic medical records (EMRs), telehealth, healthcare apps, and a variety of data reporting tools. But what comes next?