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Key Takeaways:
-Basic ethical principles include autonomy, equality and justice, beneficence, non-maleficence, impossibility, and integrity
-Principles of informatics ethics include information-privacy and disposition, openness, security, access, legitimate infringement, least intrusive, and accountability

This article discusses some of the ethical issues in informatics that were brought to light during the Covid-19 pandemic. There was substandard reporting in many areas such as hospital capacity, healthcare supply chains, staffing needs, and infectious disease data which created vulnerabilities in the system during the pandemic. Having an ethics-based rationale for decisions around informatics can help get the public more on board with public health recommendations.

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Abstract

The COVID-19 pandemic response in the United States has exposed significant gaps in information systems and processes that prevent timely clinical and public health decision-making. Specifically, the use of informatics to mitigate the spread of SARS-CoV-2, support COVID-19 care delivery, and accelerate knowledge discovery bring to the forefront issues of privacy, surveillance, limits of state powers, and interoperability between public health and clinical information systems. Using a consensus-building process, we critically analyze informatics-related ethical issues in light of the pandemic across 3 themes: (1) public health reporting and data sharing, (2) contact tracing and tracking, and (3) clinical scoring tools for critical care. We provide context and rationale for ethical considerations and recommendations that are actionable during the pandemic and conclude with recommendations calling for longer-term, broader change (beyond the pandemic) for public health organization and policy reform.

INTRODUCTION

The United States was unprepared for the 2019 Novel Coronavirus Disease (COVID-19) pandemic, despite experiencing recent outbreaks from the same virus family such as the 2003 severe acute respiratory syndrome (SARS) epidemic. General warnings about and predictions of future pandemics and calls for global preparation as well as specific early warnings concerning the COVID-19 outbreak in Wuhan, China went unheeded. The responses from US public health agencies were generally disparate, uncoordinated, and inadequate to the challenge, resulting in insufficient supplies of protective equipment, a dearth of testing facilities and kits, and delays in test processing and results. Taken together, this lack of coordination made evident a fragmented information infrastructure that could not promptly and reliably provide even the most basic information related to daily case trends, hospital capacity, and healthcare supply chain. Various "social distancing" strategies and economic shutdowns across states curbed the initial spread of the virus in many parts of the country, but the rapid "reopening" in several areas-due to concerns about exacerbating the economic crisis and the public's desire to return to work and social activities-resulted in infection surges across communities. Emerging from the pandemic and preventing additional cycles of the disease will require advances in scientific understanding of SARS-CoV-2 and extensive public health resources, in addition to a vaccine.

These events provide informaticians with an opportunity to reflect on how to effect much-needed changes in the US health system and the health information infrastructure and to inform public health policy with more reliable data and evidence. The biomedical informatics community, in collaboration with others, has a responsibility to assess the current information systems, regulations, and policies in responding to the pandemic and identify needed systemic changes. A substantial part of this assessment should address ethical, legal, and social issues that the SARS-CoV-2 outbreak brought to the fore. Consensus had been building prior to the pandemic that both privacy and research regulations were outdated and needed revisions to reflect technological changes, newer conceptions of privacy and bioethics, and the emerging view that expands health data to include many types of data collected by diverse entities, health related or otherwise., The pandemic has brought further scrutiny to previously identified complex ethical, political, and social issues., Building upon prior experiences and scholarship, we examine key informatics-related ethical issues in light of the COVID-19 pandemic and provide short- and longer-term recommendations for public health organization and policy.

BACKGROUND

The Ethical, Legal, and Social Issues (ELSI) Working Group of the American Medical Informatics Association (AMIA) has a long history of advocating for ethical and human-centered practices in applications of healthcare information technology. During April 2020, when the majority of US states had imposed stay-at-home orders or other activity restrictions, the ELSI working group launched a collaborative effort to identify and raise awareness about ethical issues that are crucial to the informatics community at large and to policy makers. The group identified 3 thematic areas of particular importance using a consensus-building process (see Supplementary Appendix A for details on methods): (1) public health reporting and data sharing, (2) contact tracing and tracking, and (3) clinical scoring tools. Although these themes intersect and have far-reaching implications at all levels of government and policy making, we consider issues related to public health reporting and data sharing to be centered at the national level (ie, the need for rigorous coordination at the national level, while the actual data collection occurs locally), issues related to contact tracing and tracking to be centered at the community level, and those related to clinical scoring and assessment tools to be more relevant at the health system level. In the following sections, we lay out context and rationale for ELSI considerations, followed by recommendations that are actionable during the pandemic and recommendations that call for long-term, broader change (beyond the pandemic) for public health organization and policy (see Appendix A for a summary of recommendations). Other topics identified by the group (eg, how the Health Insurance Portability and Accountability Act applies in light of COVID-19) were deemed outside the scope of this work, as they warrant separate and more extensive analyses or were addressed elsewhere.

PUBLIC HEALTH REPORTING AND DATA SHARING

Public health responses should be premised on an evidence base which relies heavily on the collection, assessment, and dissemination of results from standardized public health reporting. Critical data elements required for reporting include several domains such as 1) hospital capacity (eg, number of intensive care unit beds and mechanical ventilators), 2) healthcare supply chains (eg, inventory of personal protective equipment (PPE) and testing and ventilator supplies), 3) healthcare staffing needs (eg, required number of respiratory therapists), and 4) demographic and outcome data on both presumptive and confirmed infections. Since the first confirmed case in the United States in late January 2020, the COVID-19 outbreak has exposed systemic vulnerabilities in the national public health reporting system that impeded timely evidence-based decision-making. These vulnerabilities stem in part from nonstandardized, ad hoc reporting,, as opposed to standardized, systematic, electronic reporting of data from state to federal agencies, such as the Center for Disease Control and Prevention (CDC) and the Federal Emergency Management Agency (FEMA). Furthermore, the CDC's decision not to use the World Health Organization (WHO) COVID test, but to develop its own, and the subsequent failure to deliver reliable results, initially resulted in delays of distributing approved testing kits. This approach motivated local institutions to develop their own testing and reporting protocols, albeit with varying clinical validity and persistent delays in test results. To further exacerbate reporting issues, the Department of Health and Human Services (HHS) abruptly changed the process used by hospitals to submit daily COVID-19 reports about testing, hospitalizations, and hospital capacity in July 2020. Hospitals were instructed to submit data through a system developed by a commercial contractor-TeleTracking Technologies Inc. Data would then be aggregated and analyzed using a new platform, called HHS Protect, built by another commercial entity-Palantir Technologies Inc., effectively bypassing the CDC. The stated purpose of this change was to streamline data collection and analysis, however it is as yet unclear whether and how aggregate trend data will be made publicly available. These events, together, highlight issues of not only efficiency and timeliness in obtaining and analyzing data but also trust and transparency in how this data will inform policy responses as the pandemic evolves.

Short-term recommendations

Disease surveillance efforts that report critical data elements (eg, viral and antibody testing results, hospital capacity, PPEs, and healthcare staffing and supply needs) should be consolidated, coordinated, and well-supported at local, state, and national levels. Current reporting standards are not robust enough, and may lead local and state public health departments to take precaution- or fear-driven, rather than evidence-driven, action. In the aftermath of the 2014-2016 Ebola pandemic, for example, the CDC provided funding to develop PPE guidance and surveillance measures that can be reliably integrated, interpreted, and used for modeling and decision-making. However, this ongoing effort was too late for deployment of nationwide standards. Researchers and journalists have also sought to compile their own COVID datasets in the absence of centralized efforts to track infections and supply shortages at local and regional levels.,

The principle of justice instills a responsibility to understand how COVID-19 may disproportionately impact some communities, workers, and demographic groups. Outbreaks among nursing home residents, prisoners, and low-wage workers in meat-processing plants and higher rates of severe illness among racial and ethnic minority groups, underscore the need to report data elements that make subgroup analysis possible. To better capture the social inequalities observed in COVID-19 patient outcomes, public health reporting should include sociodemographic factors such as age, race, income (zip code or census tract level), gender, gender identity, ethnicity, disability status, and comorbidities in health outcome analyses. As of this writing (late July 2020), the CDC had released early reports on geographic and demographic differences and clinical outcomes among COVID-19 patients. Although these results are useful, more nuanced and more rapid reporting is needed at local and state levels to direct public health resources to communities at greatest disease risk, and expose broader health disparities that have long plagued the US health services infrastructure.

Longer-term recommendations

Informed by this pandemic and prior AMIA work,, we concluded that a more robust, standardized national reporting system is needed to effectively respond to future infectious disease outbreaks. A federal agency, such as the CDC, should be further empowered with coordinating data collection with local and state public health departments, and funded at a level that reflects the complexity and importance of this work. Major investments in building an informatics-based infrastructure are needed, as demonstrated by the continuing use of fax machines for case reporting to local health departments. Our call to modernize the infrastructure and process of disease surveillance is not new. The CDC's National Notifiable Diseases Surveillance System was launched in 2014, but results have fallen far short of the comprehensive change needed to efficiently and effectively conduct public health surveillance. Modernizing the public health reporting system will require 1) effective use of data standards and interoperable systems that use those standards (eg, LOINC codes established during the pandemic to identify laboratory tests for viral RNA or antibodies to the virus) 2) unique identifiers and metadata for testing facilities and tests, 3) robust management of the entire data pipeline from local to state to national public health agencies, and 4) abolishment of paper-based systems and authentic collaboration with and support from electronic health record (EHR) vendors and the standards community to build and maintain the necessary technical infrastructure to automatically collect and report critical data elements.

CONTACT TRACING AND TRACKING

One of the major pandemic mitigation strategies promoted by WHO has been summarized as Trace, Test, and Treat, which stands for identifying new cases, tracing their social contacts, and then testing and treating them. Given the inadequate response to measures intended to limit the spread of the virus and avert a worsening economic crisis, the US is contemplating a "tracking" strategy rather than contact tracing. Trace and Track seems similar at first glance, but there is a difference between contacting an infected person and asking them (voluntarily) for their contacts who then can be alerted versus an automated, and potentially covert, system that tracks the general public and may lack transparency.

Automated tracking raises ethical concerns related to privacy and control of personal devices. Countries such as Taiwan have shown that geo-tracking using mobile phones can be used not only to enable contact tracing, but also to ensure that citizens are complying with self-quarantine orders. Such constant data collection and analysis through mobile applications is highly intrusive and is prone to abuse when the data are sold or reused for commercial purposes, such as in advertising, targeted marketing, or employment and credit decisions. Tracking puts privacy and autonomy at risk and may endanger safety, including financial security, especially when data are used by banking or billing apps.

In the context of tracking during this crisis, we assert that all collected data have implications to health, insofar as a trip to the grocery store or gas station, a jog in the park, or a take-away food order all contribute to an individual's potential exposure to SARS-CoV-2. With certain coordinates, the collected geodata can identify an individual. The majority of mobile apps do not specify how long their data collections will persist nor whether the collected data will be purged. The extent of (ab)use of such collected data, sometimes deprecated as "digital or data exhaust," has been exposed by both scholars and journalists but is still not widely known to the public.

Short-term recommendations

Few privacy-preserving methods and tools exist to support benign contact tracing. Such tracing does not identify persons to others but, rather, notifies exposed persons directly about when and where they may have been exposed., These methods are more reliable because information is retained on the user's phone while only general geolocation details, such as a visit to a specific location, are known to the developer or vendors. It is important to leverage such techniques and use the least intrusive technology and collect the minimally required data for tracking. If tracking must be based on a particular technology, the choice should be justified by providing integral privacy protections to safeguard the data from unintended or undisclosed use and to ease concern about surveillance. With resurgence of infections throughout the country and mounting determination to "flatten the curve," it may be ethically defensible to heighten tracking as a way to enhance safeguards during public health emergencies. However, the long-term risks to privacy and the potential repurposing of collected information remain problematic. Balancing the use of data and technology for public good versus protection of privacy is key. Laws and/or regulations might be enacted and/or amended as necessary to prevent COVID-19 data from being exploited, whether by governments or commercial entities. Such amendments may include mandating complete transparency about what data are being used and how, in both short- and long-term.

Longer-term recommendations

Location and contact tracing illustrate how all data can function as health data, which then implies that all personal data should receive the same protection as health data. Both concurrent and retrospective analyses are needed that examine how effectively and efficiently different technological solutions have addressed problems during the pandemic in terms of public health outcomes (eg, infection and case fatality rates and other clinical outcomes), at what cost to the economy, and at what cost to personal, family, work, and civic life. The highly mobile nature of American culture necessitates a national effort to be maximally effective.

Given some relaxation in HIPAA enforcement (eg, for telehealth providers and in using/disclosing protected health information related to substance abuse to authorized personnel) due to the public health emergency, discussion of HIPAA, and other privacy reform should continue at the national level. This is an excellent opportunity for the US to consider harmonizing the patchwork of sector-based privacy regulations to enable a more uniform and responsive set of protections nationwide, along with significant attention to improved cybersecurity.

CLINICAL SCORING TOOLS

Several clinical scoring systems that assess the severity of the disease and estimate the risk of mortality and other intensive care unit (ICU) outcomes are available for critical care medicine. Examples include Acute Physiology and Chronic Health Evaluation (APACHE), Sequential Organ Failure Assessment (SOFA), quick SOFA (qSOFA), modified SOFA (mSOFA), Pediatric Risk of Mortality (PRISM) and others. Clinicians need reliable appraisals of the varying efficacy and safety of these different critical care-scoring tools and how they apply to COVID-19 and other patients. We highlight this as an important need because several crisis standard-of-care protocols incorporate such scores as an integral part of decision-making related to ICU admission triage, ventilator allocation/reallocation, and initiation of cardiopulmonary resuscitation.,

Short-term recommendations

EHR vendors are rapidly building and deploying features to collect underlying clinical data needed for the score, compute the severity score, and repeat measurements as appropriate, though not all institutions have enabled such features. It is important to ensure that computerized versions of clinical scores are well-calibrated and used as intended in crisis standard-of-care guidelines. Scoring tools should not be used for purposes for which they were not designed. For example, the original SOFA score should be repeatedly assessed to evaluate the severity of the disease and the duration of ICU resource needs but has not been validated for predicting the risk of mortality. Should there be shortages of ICU beds and ventilators due to the crisis, clinicians and triage panels need to be able to reliably use the scores to allocate resources. Second, EHR implementations of clinical scores should be adaptive to local circumstances and to emerging observations and evidence from pandemic investigations. For instance, early studies suggest that not many COVID-19 patients go into respiratory failure earlier than organ dysfunction, which makes the use of SOFA as a part of standard of care less appropriate.

Longer-term recommendations

The pandemic postdrome is an unparalleled opportunity to study, critique, and improve crisis standard-of-care guidelines and tools. First, there needs to be a robust, retrospective evaluation of the specific scoring tools used for triage and resource allocation, which often include assessment of COVID-19 severity, possible need for critical care, and likelihood of interventions such as intubation. Second, local conditions, patient preferences, and continuous monitoring and availability of critical resources must be considered. Some patients may not want measures such as ventilator care. If resources or treatment options that were previously unavailable (eg, extracorporeal membrane oxygenation) now become available, reassessment of decisions are needed. Algorithms and clinical scores rarely include these data. If scoring tools are used for ICU admission triage and allocation of resources, they may well need reengineering to incorporate patient preferences, resource availability, and other aspects that are not now considered. Third, healthcare organizations and public health agencies should develop future crisis standards in consultation with key stakeholders and community groups. For instance, a number of disability rights organizations have faulted COVID-19 treatment guides as embedding discrimination against people with disabilities, in part because of the use of comorbidities as a way to fine-tune SOFA scores. Although these concerns were adequately addressed in some jurisdictions, more work is needed to build and sustain the trust of vulnerable populations.

CONCLUSION

By early April 2020, the United States reported the world's highest incidence of, prevalence of, and mortality from COVID-19. Gross shortages in medical supplies at the point of care, rapid community transmission, insufficient testing capacity, and ultimately, increasing incidence and mortality have come to define the national emergency in the United States thus far. Underlying issues include the lack of a standardized, nationally-coordinated reporting system for critical data elements, lack of trustworthy and accountable ways to deploy technological solutions for contact tracing, and lack of well-calibrated algorithms that can be used in standards-of-care applicable during a crisis. This work introduces each of these issues, along with short- and longer-term recommendations to guide future public health and institutional policies and practices. Having a sound ethics-based rationale and transparent approach for responses to severe public health threats can lead to better public acceptance, harmonized and strengthened standards across different domains related to data and information technologies, and increased public trust in governmental and commercial entities for routine as well as crisis practice. In addition to the longer-term recommendations described in this perspective, future work at the intersection of ELSI and informatics will include a stronger focus on (a) regulatory waivers and potential policy reform as it relates to telemedicine and other digital health solutions, (b) impact of informatics infrastructure in enabling health equity in the context of clinical trials and distribution of vaccines for COVID-19, (c) public-private partnerships in pandemic data management, governance, and/or analytics, and (d) role of digital health technologies (eg, mobile apps for symptom tracking and contact tracing; digital immunity passports) in reopening of workplaces, including research and educational institutions.

FUNDING

VS was supported in part by the National Science Foundation under grant #1838745 and the Arizona Board of Regents' Technology Research and Innovation Fund (TRIF).

AUTHOR CONTRIBUTIONS

VS, AS, and MC initiated the project and organized the author group into 4 initial thematic areas: (1) public health reporting, (2) policy and legal issues, (3) clinical issues, and (4) contact tracing. VR, MC, and CUL contributed to area 1. CP, EP, PD, and YS contributed to area 2. RS, CUL, VS, and KG contributed to area 3. AS, BK, RK, and PD contributed to area 4. VS drafted the manuscript based on contributions from each area. All authors reviewed the manuscript and provided critical feedback. CP, CUL, and RS assisted with final copyediting.

SUPPLEMENTARY MATERIAL

Supplementary material is available at Journal of the American Medical Informatics Association Online

CONFLICT OF INTEREST STATEMENT

None declared.

REFERENCES

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Ethics and informatics in the age of COVID-19: challenges and recommendations for public health organization and public policy

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The COVID-19 pandemic response in the United States has exposed significant gaps in information systems and processes that prevent timely clinical and public health decision-making.

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Key Takeaways:
-Exploratory analysis is what you need to understand the data; explanatory analysis is the story you want to tell with the data
-Try different charts or designs to see which one will best explain your data

This case report discusses best practices for data visualization in nursing. The report looked at best practices from cognitive science and computer science and used them to develop fall prevention materials in a hospital.

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Key Takeaways:
-Data visualization can help with data interpretation and dissemination
-Data is more compelling when it is presented as a story

This week's article comes from MIT's Sloan School of Management. As part of the MBA curriculum, they offer a course on Communications and Data Storytelling because it is such an important part of being successful in business. The section on empathy is particularly interesting, stating that one of the more difficult skills in data storytelling is understanding where the audience is coming from and how they'll react. Tailoring data storytelling to the audience is important for having an impact.

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Countless organizations are dialing up analytics to turn the glut of enterprise data into actionable business insights.

But many of the endless charts, dashboards, and visualizations fall flat with their intended audience. Sometimes it's a matter of overwhelming recipients with too much data; other times, it's about presenting the wrong data or not fully understanding how to create an effective narrative that will resonate with recipients.

Enter data storytelling, a skill set handcrafted for the era of big data. While interpretations vary, most experts describe data storytelling as the ability to convey data not just in numbers or charts, but as a narrative that humans can comprehend.

Just as with any good story, a data tale has to have a beginning, a middle, and an end. It needs to be presented without bias and with the proper empathy and context so business users can absorb and leverage the insights for more intelligent decision-making.

"If you want people to make the right decisions with data, you have to get in their head in a way they understand. Throughout human history, the way to do that has been with stories," said Miro Kazakoff,an MIT Sloan lecturer who teaches Communications & Data Storytelling as part of the school's Masters of Business Analytics curriculum.

If you want people to make the right decisions with data, you have to get in their head in a way they understand.
Miro Kazakoff Lecturer, MIT Sloan
Would-be data storytellers are coached to anticipate an audience's likely response to analysis, Kazakoff said. Students learn to structure their planning and presentation to address the needs of a specific audience - whether it's a colleague, a customer, or a boss - so they're able to take away the right insights and initiate appropriate actions.

That's not always possible with common analytics dashboards that simply alert business users to a specific change - say, a dip in sales or a spike in customer support calls - without providing insight into the entire story.

"It's hard for a dashboard to explain why something is happening," Kazakoff said.

Communicate with context

This year, Glassdoor ranked data scientist as the third most desired job in the U. S. with more than 6,500 openings. But PhD experts in statistics and mathematical modeling, or techies fluent in languages like Python and R, are just part of what's required to be successful with data analytics.

It's also essential to effectively communicate the insights and understand the perspective of an audience, which may or may not share that same view or have comparable fluency with the data.

More often than not, data analysts and data scientists don't have range across both skill sets, said J.T. Wolohan, author of " Mastering Large Datasets with Python," who has experience hiring data scientists for the private sector.

"Data scientists typically have point-and-shoot skills, but they can't explain why they are doing what they're doing," Wolohan said. "They have a hard time working backwards from questions into practical business solutions. That's really the missing skill set."

Ruthless editing

Proficiency with data storytelling means being able to present information without injecting bias and to recognize what's important and what's not with the aim of keeping things simple. This requires effective data storytellers to be ruthless editors, Kazakoff said; avoiding the tendency to adjust data to fit preexisting story lines and making sure to frame the data into a story that the audience cares about.

"The skill of data storytelling is removing the noise and focusing people's attention on the key insights," explained Brent Dykes, a data strategy consultant and author of " Effective Data Storytelling: How to Drive Change with Data, Narrative, and Visuals."

The skill of data storytelling is removing the noise and focusing people's attention on the key insights.
Brent Dykes Data Strategy Consultant
"Part of the skill is building narrative and revealing data in the proper order and sequence, and then there is the visualization piece," Dykes said.

Adept data storytellers not only have good sensibilities for presenting data graphically, they are also able to synthesize the findings down to a core set of visuals that gets the point across in the most direct, succinct manner, he said.

Perhaps the most difficult data storytelling skill to master is empathy - to understand where the audience is coming from and which parts of the data analysis they'll react to, Kazakoff said.

For example, a sales executive and a software development head typically have countering worldviews, so when sharing the same data with them, there is likely to be vastly different reactions. It's critical, therefore, that whoever is tasked with the data analysis has the capacity to interpret the different viewpoints and present relevant material accordingly.

"It's not going to be a black and-white answer - it's very much a translation task," Wolohan said.

Job skill or job title?

To fill the gap, should businesses create new data storyteller roles or upskill its workforce so everyone has a foundational ability to understand, work with, and analyze data? Experts contend organizations should be doing both.

Dykes argued data storytelling is a skill that is essential for the broader workforce for success in what he called "the last mile of analytics."

Kazakoff agreed.

"Being literate with data and able to explain the stories it reveals is as important a form of literacy as being able to read, write, and speak clearly," Kazakoff maintained. "It's a core skill, not a job function, and it cuts across all division and roles at a company."

Just like communications, some roles will require a deeper understanding than others, but Kazakoff said no one whose job is informed by data will escape the need to understand and explain that data to others.

Althea Davis, enterprise data governance manager at Etihad Aviation Group, agreed that data storytelling is a much-needed enterprise skill, but said she'd love to see a specific role take root to balance out the range of a data analytics organization.

"It's such a steep learning curve for business people to grasp data literacy to a level where they can benefit," Davis said. "They need molding and mentoring in a way that they can absorb. If we had really good data storytellers, it would make it so much easier."
Read next: 'How to build data literacy in your company'

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The next chapter in analytics: data storytelling | MIT Sloan

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Countless organizations are dialing up analytics to turn the glut of enterprise data into actionable business insights. But many of the endless charts, dashboards, and visualizations fall flat with their intended audience. Sometimes it's a matter of overwhelming recipients with too much data; other times, it's about presenting the wrong data or not fully understanding how to create an effective narrative that will resonate with recipients.

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Key Takeaways:
-HIPAA provides privacy and security for clinical data in the health care setting
-Authentication, authorization, and accountability are important for maintaining security of data
-Data breaches are serious issues in informatics today

This article discusses the misconceptions about HIPAA, which were discussed particularly with regard to Covid vaccination. Despite claims to the contrary from certain political figures, it is not a violation of HIPAA to ask a person's vaccination status. HIPAA is also not applicable to employers, retail stores, journalists, or other organizations not related to health care.

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How the HIPAA Law Works and Why People Get It Wrong

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The measure prohibits health professionals from revealing your medical records, but it is perfectly legal to ask whether someone has been vaccinated.

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Week 10: Quality and Unintended Consequences

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Week 10: Quality and Unintended Consequences

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Key Takeaways:
-Medical errors can occur when alerts are ignored
-Patient portals can help engage the patient in their own care

This article from the Agency for Healthcare and Research Quality (affiliated with HHS) pointed out an interesting limitation to efforts to addressing alert fatigue. There are potential legal consequences to removing alerts, so organizations may be more reluctant to remove alerts for this reason. Switching from high to low priority alerts rather than removing alerts altogether is one way to mitigate this issue in some situations.

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Background

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

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

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

Effect of alert fatigue on patient safety

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

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

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

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

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

Current context

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

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

Increase alert specificity by reducing or eliminating clinically inconsequential alerts.

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

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

Make only high-level (severe) alerts interruptive.

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

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

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

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

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

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Key Takeaways:
-Gamification is the use of game thinking and mechanics to engage users
-Health games have been used in patients with dementia and Type 1 DM, among others

This week I've attached a blog article with some interesting examples of gamification in healthcare. It seems many of these games are focused on wellness, physical fitness, and prevention, though there are others that aid in chronic disease management as well.

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

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

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Key Takeaways:
-remote patient monitoring, virtual visits, and stored/forwarded information are all types of telehealth
-reimbursement for telehealth has improved
-telehealth can help reach patients who otherwise might not be able to come in for a visit, but drawbacks include a lack of physical exam or in-person connection

I've posted an article about the progress that has been made in telehealth since the start of the Covid-19 pandemic, when telehealth became more necessary than ever. The article points out some issues that remain, including legislation for telehealth, establishing evidence-based practical guidelines, and overcoming inequalities in telehealth usage.

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Connect Health. Author manuscript; available in PMC 2022 Feb 28.

Published in final edited form as:

Stefano Omboni, 1, ² Raj S. Padwal, ³ Tourkiah Alessa, ⁴ Béla Benczúr, ⁵ Beverly B. Green, ⁶ Ilona Hubbard, ⁷ Kazuomi Kario, ⁸ Nadia A. Khan, ⁹ Alexandra Konradi, ¹⁰ Alexander G. Logan, 11, ¹² Yuan Lu, ¹³ Maurice Mars, 14, ¹⁵ Richard J. McManus, ¹⁶ Sarah Melville, ¹⁷ Claas L. Neumann, ¹⁸ Gianfranco Parati, 19, ²⁰ Nicolas F. Renna, ²¹ Philippe Ryvlin, ⁷ Hugo Saner, ²² Aletta E. Schutte, 23, ²⁴ and Jiguang Wang ²⁵

Abstract

During the COVID-19 pandemic, telemedicine has emerged worldwide as an indispensable resource to improve the surveillance of patients, curb the spread of disease, facilitate timely identification and management of ill people, but, most importantly, guarantee the continuity of care of frail patients with multiple chronic diseases. Although during COVID-19 telemedicine has thrived, and its adoption has moved forward in many countries, important gaps still remain. Major issues to be addressed to enable large scale implementation of telemedicine include: (1) establishing adequate policies to legislate telemedicine, license healthcare operators, protect patients' privacy, and implement reimbursement plans; (2) creating and disseminating practical guidelines for the routine clinical use of telemedicine in different contexts; (3) increasing in the level of integration of telemedicine with traditional healthcare services; (4) improving healthcare professionals' and patients' awareness of and willingness to use telemedicine; and (5) overcoming inequalities among countries and population subgroups due to technological, infrastructural, and economic barriers. If all these requirements are met in the near future, remote management of patients will become an indispensable resource for the healthcare systems worldwide and will ultimately improve the management of patients and the quality of care.

Keywords: COVID-19, telemedicine, telehealth, m-health, Africa, America, Asia, Australia, Europe

Introduction

The public health emergency that resulted from COVID-19 increased public interest and demand for telemedicine worldwide and led to a rapid expansion of this care modality as an integral part of outpatient care delivery[1]. Concomitantly with a dramatic reduction of in-person consultations, a surge in the use of remote patient monitoring and televisits has been observed. At last, telemedicine has emerged worldwide as an indispensable resource to restrain the spread of the disease through improving the surveillance of patients, facilitating early identification, and allowing prompt management of infected people, as well as enabling continuity of care of vulnerable patients with multiple chronic diseases[2,3].

However, to date, the use of telemedicine worldwide remains suboptimal due to a heterogeneity of solutions and differences in the quality of infrastructures and technologies, as well as the level of acceptance of patients and doctors across different countries. With the aim of providing an update of the status and use of different modalities of telemedicine during the COVID-19 pandemic (from early 2020 to the end of 2021), we reviewed the current literature and asked experts from all over the world to summarize their experiences.

The objective of the present paper is not to provide a comprehensive picture of the use of telemedicine worldwide but to report experts' perspectives, mainly focusing on the management of patients with chronic conditions requiring strict surveillance during home isolation[4,5].

Main Applications of Telemedicine During Covid-19

The COVID-19 pandemic has favored the rapid adoption of digital solutions and advanced technology tools in healthcare. Among these solutions, telemedicine, tools based on artificial intelligence (AI), big data analytics, and mobile tracing apps for surveillance, were widely employed to diagnose, prevent, monitor, and treat individuals worldwide[6,7].

A scoping review covering the early scientific literature in response to COVID-19 (January to June 2020) showed a wide use of telemedicine, principally in high-income countries (86.6% of the articles) [ Figure 1 ][8]. Of the 543 articles included in the review, the majority (92.3%) reported provision of telemedicine services for conditions not related to COVID-19, and only a few articles (7.7%) focused on the provision of services related to COVID-19. The majority of the articles focused on telemedicine use in internal medicine (mainly endocrinology, oncology, geriatrics, cardiology, and orthopedics), preventive medicine (for prevention of COVID-19, noncommunicable diseases and health education, in different settings, including primary care), psychiatry, surgery, neurology, otolaryngology, and dermatology. The most common purpose for using telemedicine was the provision of integrated clinical care (49.7%), including any combination of triage, diagnosis, treatment, follow-up, and rehabilitation services. Other purposes were follow-up care (15.3%), medical education (9.9%), diagnosis (7.2%), and rehabilitation (4.4%). The relevant role of telemedicine for the screening and managing of COVID-19 cases and the provision of continuity of care to chronic disease patients during the COVID-19 pandemic has been confirmed by other systematic reviews[9-12].

During the COVID-19 pandemic, digital health and telemedicine solutions based on smartphone apps (so-called m-health) gained increasing popularity. Apps for digital contact tracings were used to collect and analyze data on people's proximity, location, movement, and health status[13]. When used at scale, particularly when integrating AI-based technologies, these helped contain the spread of the virus and mitigate the effect of COVID-19 on population health[13,14]. Other m-Health applications were used for symptom checking, remote monitoring, and video consultation, and they were also valuable means for health information dissemination and awareness raising in the population. According to a systematic review conducted in the early phases of the COVID-19 pandemic, apps for contact tracings or symptom checking were prevalent in Asia, whereas the apps developed in Europe and North America remained focused on information[15].

The specific experience of 15 countries or areas of the world in five continents are discussed in detail in the following sections.

Experience In Different Countries

Africa

North and mid Africa

The effect of COVID-19 in Africa has been different from most parts of the world. By July 2021, with fewer than 2% of the population fully vaccinated, the mortality rate in the Africa Region was 92 per million population. This is 5% of the mortality rate of the Region of the Americas, 1906 per million, and 7% of that of Europe, 1290 per million. Several possible explanations include a much younger population (the median age in sub-Saharan Africa is 18 years), few long-term care facilities for the elderly, insufficient testing, and prior exposure to local coronavirus strains[16].

The COVID-19 pandemic has heightened interest in and awareness of telemedicine as in the rest of the world. As of September 2021, there were nearly 7500 papers on telemedicine and COVID-19 indexed in PubMed, 124 (1.6%) report activity in Africa, and, of these, only 28 (0.4%) report on actual telemedicine use during and in response to the pandemic.

Telemedicine activity is reported in 14 of the 54 countries, with most reports from Nigeria (seven papers). Eight of the papers are surveys of doctors in a particular specialty seeking to determine whether there had been uptake of telemedicine during the pandemic. These provide little detail of actual telemedicine services and the mode of telemedicine delivery. Overall, telemedicine activity has been reported in 16 clinical disciplines. The most common is mental health, reported in seven countries. Other examples include pediatrics, rheumatology, oncology, urology and obstetrics, and gynecology.

Given the generally poor information and technology and communications technology infrastructure in Africa, it is unsurprising that the telephone (10 papers), WhatsApp® (8 papers), and e-mail (2 papers) were the most common methods used. Videoconferencing using Skype, Zoom®, and WhatsApp® was reported in five papers.

Using e-mails and videoconferencing were unpopular because of technical limitations in Africa in general, and lack of insurance reimbursement for telemedicine and medico-legal and ethical concerns were noted as hindrances to telemedicine in many African countries. However, as elsewhere in the world, the COVID-19 pandemic will serve as a catalyst for telemedicine in Africa.

South Africa

Before COVID-19, telemedicine use was limited in South Africa, partly because of the lack of remuneration for telemedicine and, more importantly, the restrictive General Ethical Guidelines for Good Practice in Telemedicine in South Africa produced by the Health Professions Council of South Africa[20,21]. While their definition of telemedicine restricts it to communication between healthcare practitioners, doctor-to-patient consultation is allowed if there is an existing doctor-patient relationship.

South Africa has a two-tiered health system: state-funded public health serving roughly three-quarters of the population and the private sector, funded largely by individual contributions to medical aid/insurance schemes. Public sector doctors are salaried, while the private sector is fee for service based. Despite attempts to introduce telemedicine in the public sector over the last twenty years, telemedicine activity has been limited and largely confined to a few clinical and educational services provided by academic departments at medical schools. Radiology information systems, picture archiving systems, and laboratory information systems are used in both the private and public sectors. Informal telemedicine using instant messaging and videocalls between colleagues in both sectors is widespread but poorly documented.

COVID-19 has markedly increased telemedicine use in the private sector. Health professionals have resorted to telemedicine to overcome lockdown restrictions, triage patients, reduce unnecessary personal patient contact, and maintain an income[22]. This has been facilitated by temporary relaxation of the guidelines for the duration of the pandemic, allowing telemedicine to be practiced " without an established practitioner-patient relationship"[23,24].

New issues have arisen. Payment for services varies from 55% to 100% of face-to-face fees and by discipline and telemedicine modality used - telephone or videoconference. Concerns about data ownership by medical aid schemes and interoperability issues of medical records are emerging in what has suddenly become a competitive market. Software developers and vendors are unhappy about practitioners' use of free applications such as Zoom® or WhatsApp® for telemedicine.

What is the future? Many health professionals who were either ambivalent or opposed to telemedicine have become users. As elsewhere, will the enabling regulatory changes remain? If not, is there now enough demand and telemedicine experience, especially in the private sector, for practitioners and consumers to fight for change and the continuance of telemedicine?

Americas

Canada

Virtual care delivery and remote patient monitoring (RPM) are the two most common telemedicine modalities currently offered in Canada[26,27]. Virtual care delivery typically consists of video (or telephone) visits between patients and providers, with multiple private companies offering virtual platforms that may include encrypted video connection, appointment scheduling, virtual waiting rooms, and document file sharing and storage. Mental health services, teledermatology, and primary care visits are the common indications for telemedicine use[28]. RPM is used primarily to monitor patients who have been discharged from acute care and/or admitted to virtual hospital programs, including chronic conditions such as hypertension, diabetes, chronic obstructive pulmonary disease, and heart failure[27,28]. This approach can be cost-effective if focused on high-risk patients[29].

In the 1970s, Canada was a global leader in telemedicine[30]; however, in recent decades, prior to the coronavirus (COVID-19) pandemic, uptake of telemedicine has been extremely low. For example, in 2014, 411,778 telehealth clinical sessions were delivered, representing only 0.15% of the $270 million billable services provided nationally[26,27]. Conversely, virtual care visits have surged during the pandemic as Canadians have a high prevalence of connectivity. In 2020, 92% of Canadians had broadband access to the Internet, 69% used the Internet to search for health information, 84% used smartphones on a regular basis, and 14% of the population used Internet-connected wearable smart devices[31]. A study using population-based data analysis from Ontario, the largest Canadian province (population 14.6 million), found that virtual care use increased from 1.6% of total ambulatory visits in the second quarter of 2019 to 70.6% in the second quarter of 2020[32]. In a survey of family physicians in the province of British Columbia, telephone visits for hypertension increased from 18% to 80.7% during the pandemic[33]. In a 2021 survey of 2071 practicing Canadian physicians, it was reported that 94% of physicians were using virtual care and the most common modalities used were telephone (93%), video (51%), and secure e-mail/messaging (36%)[34]. Only 5% of physicians reported use of RPM platforms and 96% planned to continue to use virtual care in the future.

By the end of the summer 2021, more than 80% of Canadians have received the first dose of vaccine and 50% the second one[35]. With governments easing restrictions on movement and gatherings, few expect the health system will fully abandon virtual care as comfort using technology has grown, particularly among older adults. Instead, a hybrid model is envisioned in which healthcare services will transition to a " flexible" approach using digital technology to bridge the communication gap between clinic visits and provide a continuum of care. For health professionals, this means greater reliance on digital tools such as electronic health records, telehealth, and e-pharmacy, while, for patients, it means easier access to personal health records, expanded use of home monitoring devices, greater use of electronic messaging, and greater reliance on self-management, which is strongly promoted as a way to improve health outcomes and quality of care[36]. Thus, utilization of virtual care will likely increase and become the standard practice for persons with long-term conditions.

To facilitate the use of telemedicine services, a Virtual Care Task Force was established by the Canadian Medical Association, the College of Family Physicians of Canada, and the Royal College of Physicians and Surgeons of Canada[40]. The identified challenges and barriers to telemedicine uptake are those listed in Table 1 .

Table 1

Lack of physician reimbursement for virtual care visits

Lack of virtual care services via the public health sector

Lack of interoperability & compatibility of digital technology

Governance of insured services and licensure restrictions

Among the recommendations made by the task force for the implementation of virtual care in Canada, implementation of a national standardized framework to ensure the delivery of quality health services in virtual care was emphasized[40]. Other key recommendations included those listed in Table 2 .

Table 2

Establishing national standards for patient health information access

Supporting federal regulatory authorities to simplify physician registration and licensure processes in order to enable virtual care services across provincial and territorial boundaries

Developing revenue-neutral fee schedules for in-person and virtual care appointments

Implementing education and training about virtual care technology for healthcare delivery in the undergraduate and postgraduate medical curriculum as well as providing continuing professional medical education on the topic for physicians and other healthcare providers

Ensuring sufficient and equitable broadband access and speed in remote areas and other digital deserts and providing digital literacy training for the public

Overall, virtual care services should ideally be funded by the public sector in order to mitigate healthcare inequities. In addition, more stringent federal regulatory standards are required to prevent clinically inaccurate health monitoring devices from entering the consumer market, given that most of the devices on the market are not clinically validated[41]. This is a serious patient safety issue that should also be addressed as part of the development of a national virtual care program. It is also important that only clinically validated medical devices and Health Information Portability and Accountability Act (HIPAA)/Personal Information Protection and Electronic Documents Act (PIPEDA) compliant platforms should be used in order to ensure safe, quality healthcare service delivery.

United States of America

The utilization of telemedicine has rapidly expanded in the United States (US) in response to the COVID-19 pandemic. Data from the Centers for Medicare and Medicaid Services showed an increase in weekly telehealth visits from 13,000 pre-COVID to 1.7 million visits in the week of April 2020[42]. Compared with data from 2019, telehealth visits in October 2020 increased by more than 3000%. This unprecedented expansion of telemedicine occurred across nearly all medical specialties and included a broad range of services, such as video and audio-only visits, care chat, secure e-mail, and telemonitoring transfer of remote vital data. The increase was mainly fueled by reimbursement and policy changes[43]. During the pandemic, Medicare - the federal insurance program for the elderly in the US - rapidly expanded reimbursement for telehealth services and increased uptake of these services nearly overnight[44]. Additionally, the Department of Health and Human Services for Services Offices for Civil Rights waived penalties for HIPAA violations who were using everyday communication technologies to provide telehealth services[45].

The utilization of telemedicine has had positive impacts on facilitating access to non-life-threatening healthcare, reducing risk for transmission of SARS-CoV-2, conserving scarce medical supplies, allowing rapid deployment of large numbers of healthcare providers, and supporting continuity of care when local hospitals and healthcare centers were unable to meet demand. However, access to telehealth has not been equal in all segments of the population. COVID-19 has highlighted the digital divide as a critical determinant of widening disparities in healthcare by race and economic status[46]. For example, pre-COVID-19 hypertension was cared for primarily at face-to-face visits, with non-white race, lack of health insurance, and access to care the major risk factors for poor control[47]. COVID-19 decreases in face-to-face visits have increased the importance of capturing blood pressure (BP) measurements remotely. In the second quarter of 2020, after COVID-19 stay at home orders were enacted in many states, ambulatory care BP assessments decreased by more than 50% compared to the second quarter of 2019[48]. Furthermore, unequal access to home BP monitors, smartphones, and broadband Internet could worsen BP measurement and control disparities[49]. In a survey of federally qualified health centers (community clinics caring for underserved populations), telehealth uptake was lower in Southern states, rural health centers, and those with staffing challenges[50]. Larger well-resourced healthcare systems such as Kaiser Permanente accelerated the development and implementation of telemonitoring programs for chronic conditions including hypertension, diabetes, and heart failure. However, the reach (number of people enrolled) of these programs is still limited because of limited resources for implementing population management (such as pharmacist and nurse medication management programs) and patient reimbursement for the purchase of home BP monitors. Partnerships with the industry hold promise for extending BP telemonitoring reach. Some vendors offer cellular enabled monitors that can directly transfer BP, weight, and other vital data to a website or an electronic health record without a smartphone or broadband Internet, which would improve access to and usability of chronic condition telemonitoring[51].

As the COVID-19 pandemic continues, maintaining the expansion of telehealth is critical to providing access to care, and the facilitators and barriers to its implementation need to be carefully considered. Lieneck et al.[52] performed a systematic review of implementation of telehealth services in the US during the COVID-19 pandemic and found 24 US articles focusing on multi-level facilitators and barriers to adoption of telehealth in ambulatory care settings [ Table 3 ][53]. These telehealth facilitators and barriers are not unique to the US.

Table 3

Facilitators

Barriers

Patient engagement, including patient access to technology, education, family support, and interpreter services

Organizational readiness, including workflow planning for remote visits, appointing, check-on, and standardized templates for documentation

Regulatory and policy changes, including reimbursement parity as noted above

Patient limitations, including access to smartphones, computers, and broadband Internet, technology capability, and comfort with telehealth visits

Clinical care issues including unavoidable deviations from clinical standards of care (such as patients rather than healthcare professionals taking vital signs, limited ability to do physical exams)

Technology availability and training, weak information technology infrastructure, and inadequate reimbursement for non-clinical care costs

Future challenges include potential redaction of telehealth policies, particularly those that ensure pay equity similar to face-to-face visits and restrictions of the type communications that will be reimbursed. Systems for engaging patients in telemonitoring and chronic condition programs, still in their infancy, have been sped up by COVID-19, but progress could slow once the pandemic recedes[54]. Strategies including greater outreach, education, and infrastructure support are needed to ensure access to telehealth in marginalized, vulnerable, and underrepresented populations.

Latin America

The adoption of telemedicine in Latin America (LATAM) in recent years has been extremely slow. Insufficient communication infrastructure, biases, and financial resources have hampered its use. The regulatory framework was also out of date. As of 2019, while 65% of hospitals in Chile used telemedicine, less than 30% of hospitals in Argentina, Costa Rica, Mexico, Peru, and Colombia were doing so[55]. Only Colombia and Mexico followed the recommendations of the 2015 Global Survey on eHealth Reports on Teledermatology, Telepsychiatry, Teleradiology, and Telepathology, and reported programs in all four specialty lines[55]. In Chile, physician resistance was considered the second most important barrier to telemedicine adoption[56,57]. Notably, countries with a comparatively more individualistic culture, such as Argentina or Uruguay, may be more ready to adopt telemedicine initiatives than culturally collectivistic countries such as Guatemala[55].

Argentina

In Argentina, the COVID-19 pandemic was a shock to the system. After the onset of the pandemic, a package of laws to validate electronic prescriptions was passed, and TeleCOVID, a strategy funded by the Pan American Health Organization and World Health Organization, was launched. By September 2020, the Senate had approved a new bill that would regulate telemedicine in the future.

With the introduction of mobility restrictions, the use of telemedicine increased dramatically, particularly among certain age and health groups. This may have paved the way for a wider acceptance of telemedicine after COVID-19. The impact of the restrictions emerged the week of March 13. By March 15, 2020, the government had closed international borders and ordered schools to suspend face-to-face classes. Five days later, the government declared a total lockdown, resulting in a sharp drop in the mobility of all kinds. During the week of March 13, the total number of calls and the number of calls from first-time telemedicine users increased by 233% and 226%, respectively, and the number of calls that resulted in prescriptions increased by 342%. This increase persisted through the subsequent months of blockages and even beyond. Indeed, while telemedicine use decreased slightly after restrictions were relaxed, it remained significantly higher than during the pre-pandemic period, indicating a potentially permanent shift towards acceptance of innovative medical practice[61,62].

It was not just the number of new users that made the difference. The characteristics of telemedicine users also changed. Patients who used telemedicine during 2019 had an average age of 30 years with no underlying health problems: the substantial increase in the use of telemedicine during the pandemic was driven by patients 65 and older and those with pre-existing conditions.

For telemedicine to become a reality in the region, many problems will need to be addressed, including a more robust legal and regulatory framework for patient privacy, insurance, and professional licensing. In addition, in Argentina, in September 2021, the CHARGE-app study was launched: an iconic study in Latin America, through which it will be possible to evaluate the telemedicine strategy versus classic care in the management of hypertension[63].

Asia

Western Asia

In western Asia (Arab countries), each country and city had its own issues during the COVID-19 pandemic, but they faced broadly the same problems when administering care for patients with chronic conditions. Restrictions surrounding COVID-19 have led most countries to offer alternative systems to enable patients to access care during the pandemic, including telemedicine.

Clinics in Saudi Arabia and the other Gulf countries had varying degrees of access to and experience with utilizing telemedicine infrastructure prior to the COVID-19 pandemic. As the pandemic developed, these clinics had to pivot to offering remote services only. Most clinics in the Gulf countries turned to telemedicine to enact this, via methods such as phone calls, e-mail, Zoom®, and Microsoft Teams®, as well as smartphone-based technologies such as WhatsApp® and specific health apps[64]. They used these platforms for virtual visits and consultations, medication refills, rescheduling appointments, reviewing the measurements of vital signs, and so on[65-67]. Prior to COVID-19, most patients of these clinics had not used telemedicine platforms, but, due to high levels of access to smartphones and other devices and the wide availability of Internet in Saudi Arabia, relatively simple telemedicine clinics could be implemented quickly and smoothly, without too much additional burden being placed on the health system[65]. Research reported high patient satisfaction following attendance of a virtual session, and 88% of these patients recommended continuing this activity as a virtual session every year[64].

China

The community lockdown due to the COVID-19 pandemic in China encouraged the use of telemedicine for both the prevention and management of COVID-19 and the management of chronic diseases. Three types of telemedicine platforms providing online services were adopted during the epidemic in China: (1) Internet hospitals operated by public hospitals; (2) Internet hospitals operated by online healthcare enterprises; and (3) local government digital service platforms[68]. The services provided included: (1) consultations services for COVID-19; (2) consultation services for derived health problems, particularly psychological counseling; (3) telemedicine and medical imaging teleconsultation; (4) general practice medicine for common and chronic diseases; (5) screening solutions to identify highly suspected COVID-19 patients, often based on AI; and (6) medical assistant robots.

According to a report from one of the largest hospitals in western China, from February 1 to April 1, 2020, 10,557 online COVID-19 consultations were conducted for 6662 individuals, and 32,676 patients without COVID-19 completed virtual follow-ups[69]. Of the 10,557 COVID-19 consultations, 487 individuals (7.3%) were suspected cases. After comprehensive testing, four individuals were confirmed cases (0.82%, 4/487). Of the 32,676 non-COVID-19 consultations, 10,981 received e-prescriptions through an app. In this large hospital alone, the monthly average of patients receiving online follow-up from February 1 to April 1, 2020 increased by nearly fivefold, from 3400 to 16,338. The situation was very similar across the hospitals and for various diseases in the whole country of China.

The use of telemedicine services was also widespread among physicians. A survey conducted among 148 physicians from 57 hospitals in 16 provinces across China revealed that 94.6% of them adopted a telemedicine system during the pandemic; 34.1% of the physicians had never used a telemedicine system before; and only 9.3% used it with a frequency of at least once per week[70]. Overall, 91.5% and 88.4% of physicians were willing to use telemedicine during and after the pandemic, respectively. Physicians considered the inability to examine patients in person to be the biggest concern (78.3%) and the most important hurdle (58.0%) to implementing telemedicine.

Hypertension is the most common cardiovascular risk factor observed in COVID-19 hospitalized patients. The COVID-19 pandemic and consequent lockdown sped up establishing an intelligent Hypertension Excellence Center system, an endeavor to use a telemedicine platform to ensure continuity of care for hypertension cases diagnosed before the pandemic[71]. In regional medical centers, physicians working with the platform are connected with their patients by using an app that collects clinic, ambulatory, and home BP data and other health information. Physicians may do electronic counseling and even prescription through the system. Millions of patients have been on the platform and hopefully improve their hypertension management and cardiovascular health before and during the pandemic. In addition, with the established telemedicine platform, the May Measurement Month project in China continued over 2020 and 2021[72]. Individuals measured their BP with a fully automated BP measurement system. BP was measured in about 100,000 and 300,000 people in 2020 and 2021, respectively, at more than 200 measurement sites in more than 20 China provinces. This opportunistic mass screening program commenced in 2017 worldwide, and helped more than one million Chinese people know their " numbers"[72].

Japan

In Japan, since April 2020, the number of outpatients with chronic diseases such as hypertension, diabetes, and cardiovascular disease decreased in clinics and hospitals. Among 100 patients who answered a telephone survey in a university hospital in Tokyo during the COVID-19 pandemic, 20% reported depressive symptoms, and 33% were hesitant to contact medical staff in the event of cardiovascular disease exacerbation[73]. The frequency of depressive symptoms was sustained even after a decline in the number of newly COVID-19-infected patients. Following the rise of the COVID-19 pandemic, the Ministry of Health, Labor, and Welfare (MHLW) recommended the use of telemedicine. In April 2020, the government temporarily allowed the adoption of telemedicine for the first consultation between a patient and a doctor and the online prescription counseling by a pharmacist. After the first decision to temporarily expand telemedicine, the Japanese government sped up its pre-pandemic discussions on telemedicine and finally declared in June 2021 that these measures would be maintained permanently. MHLW will develop detailed guidelines to ensure the safety and quality of the treatment, followed by formal discussions on the reimbursement rate reform. Since the first decision about telemedicine, doctors were permitted to use telemedicine. However, almost all the clinics and hospitals were not prepared for sophisticated telemedicine applications. The majority of doctors used the telephone for teleconsultation as a basic form of telemedicine.

According to a survey by the MHLW in January 2021, the number of medical institutions which introduced telemedicine after the authorization of the government increased from 10,812 (9.7% of all institutions in Japan) in April 2020 to 16,095 (14.5%) in June[74]. However, this number nearly plateaued for the rest of the year. Initial online consultations for the first visit were less than 10,000 nationwide, even in the peak month of May 2020, and have decreased since. Both the usage and the rate of expansion seem somewhat limited compared to other high-income countries, such as the US.

Telemedicine was also popular among Japanese patients. In a large Internet survey conducted in two different periods during the COVID-19 pandemic in Japan, of 24,526 participants aged 18-79 years (50.8% women), 2.0% ( n = 497) reported using telemedicine in April 2020 and 4.7% ( n = 1159) during August-September 2020[75]. During the pandemic the use of telemedicine was increased more in younger than older individuals, except for subjects in their 70s who showed higher frequencies of telemedicine use. Inequality in telemedicine use by educational attainment and urbanicity of residence widened during the COVID-19 pandemic. Between August and September 2020, individuals with a university degree were more likely to use telemedicine than those with a high school diploma or less (6.6% vs. 3.5%). Residents in urban areas exhibited higher rates of telemedicine utilization than those living in rural areas only during August-September 2020 (5.2% vs. 3.8%). Disparities in telemedicine use by income level were not observed in either period.

The ICT technology is highly developed in Japan, and the Japanese Society of Hypertension generated the new research area of " digital hypertension", promoting telemedicine in the clinical practice[78,79]. Telemonitoring and telemedicine will increase the accuracy of the diagnosis of hypertension and improve the assessment of BP control status. This will improve healthcare management and treatment for hypertension[80]. However, significant challenges remain: they are related to the cost, the data integration, the redesigning of team-based care, and better user experience in Japan.

Australia

The first patient with COVID-19 in Australia was detected on January 25, 2020. To reduce the risk of patient-to-patient and patient-to-clinician transmission of COVID-19, the government responded quickly by introducing several tranches of new and temporary telehealth items to the national Medical Benefits Schedule on March 13, 2020. Compared to most countries, the rate of infections in Australia has been minuscule due to the government's early actions to close the country's borders and impose strong lockdown within districts when cases are detected. Followed by highly effective track-and-trace record keeping, the impact of the first two waves was small. However, the third wave with the Delta variant entered the country by the end of June 2021 and forced a strategy overhaul seeing for the first-time daily infection rates exceeding 1000 in August 2021.

Despite low numbers of cases and deaths in Australia, the previouly limited uptake of telehealth in the country changed rapidly with quick adoption, resulting in years of challenges for health system reform being achieved in a matter of days[81]. Overall, the introduction of " whole-of-population telehealth ", which includes remote consulting via telephone and video, is regarded as a major policy success.

Despite a policy emphasis on video telehealth as the preferred substitute for face-to-face care[81], Australian healthcare providers demonstrated a clear inclination towards telephone rather than video consultations[82]. This has been especially true for general practitioners [ Figure 2 ].

The reasons are not clear yet, but it may be pragmatic in nature, such as familiarity, need for rapid implementation, and availability of infrastructure. Other providers, such as psychiatrists and mental health consultations, were much more successful in incorporating video consults - often exceeding telephone consults[82]. Video was also a more common consultation method during the pre-COVID era, especially for psychiatrists and specialists[82].

When it comes to quality of care and patient satisfaction, the telehealth environment seems less rosy. In an online survey of 683 surgeons[83], 38% of respondents felt that the quality of care was equivalent to face-to-face consultations - with particularly the inability to perform a clinical examination a concern, and expressing that telehealth was an inappropriate means to break bad news[83]. In another survey, the clinician respondents felt that using telehealth services had been a " forced adoption" with many using telehealth for the first time[84]. Based on the responses, the authors concluded that, despite several positives, " it could be that once the pandemic passes, previous policies and practises will re-assert themselves"[84]. It is, however, often the acceptability of telehealth to patients that is the cornerstone on whether this mode of care has longevity. In a survey with outpatients that use only telephone consultations (88% aged 50 years or older), one in five indicated consistent dissatisfaction with telehealth. They often had lower literacy, qualifications, and access to the Internet[85]. Many other patients found teleconsultations convenient, but - similar to clinicians[83] - there were concerns regarding absence of physical examinations. Patients expressed a dominant desire for a mixed-model clinic in the future[85].

Europe

Germany

At the start of the pandemic, the state of North Rhine-Westphalia established a state-wide available intensive care hospital-to-hospital teleconsultation service (Virtuelles Krankenhaus NRW). Consultation requests are collected via a web portal, actioned via video conferencing, and documented in electronic case files (EFA)[91].

Interest groups, such as the German Association for Digital Health Care (SVDGV) and the German Society for Telemedicine, and parts of medical societies work to improve the situation.

Telemedicine application in Germany falls short of full utilization of the technological possibilities. The acceleration of the adoption of telemonitoring and telediagnostics into the German healthcare system with appropriate recharge mechanisms for service providers is urgently required. Strengthening cross-cutting telemedical and digital health and care applications (DiGAs/DiPAs) is vital for the pandemic response and beyond. General practitioners and hospitals require low barriers to access telemonitoring infrastructure to further hybrid healthcare solutions and improve doctor-patient relations.

Recharge through statutory health insurers, education of all actors in the sector on evidenced digital healthcare applications, and the accelerated implementation of cooperation agreements between insurers and e-Health providers are the most important measures to improve the provision of telemedicine and telemonitoring across the sector. Here, the main focus needs to be on advancing already scientifically and economically proven telemedical applications.

Hungary

The COVID-19 pandemic has hit Hungary, as other European countries, hard during the second and mainly the third wave, claiming more than 30,000 lives, leading to one of the highest mortality rates in Europe considering the relatively small population of Hungary (313.32 deaths per 100,000 inhabitants). The subsequent restrictions, leading to limited personal access to medical care and general practitioners, made the optimal management and care of chronic diseases, such as hypertension, type 2 diabetes mellitus, chronic heart failure, or kidney diseases, more difficult. Despite the potential for telemedicine in this situation, the Hungarian telemedical providers and services were not developed or prepared enough to support and implement remote care, leading to initial missed opportunities. Nevertheless, some telemedical solutions were available in Hungary during COVID-19, notably the National e-Health Infrastructure (EESZT)[94]. This communication interface using cloud-based technologies connects public and private healthcare providers, pharmacies, and the population in the whole of Hungary. The EESZT was a significant milestone in the history of Hungary's e-healthcare from the end of 2017. The system's primary purpose is to interconnect the earlier fragmented healthcare data systems in all of Hungary and centralize all data in one system, taking into account foreign examples. Hence, the operating services of the infrastructure would allow access to the necessary information from the different treatment locations. Another important objective is to provide modern central services such as subsystems for issuing electronic receipts, electronic referrals and medical documents or e-imaging, or the e-profile, which facilitate the widespread adoption of modern healthcare.

Based on the EESZT, more subjects suffering from chronic conditions (e.g., chronic heart failure, hypertension, and type 2 diabetes mellitus) could access medical care and assess their condition during the pandemic. The main problem was sending their data [BP measurement, self-measured blood glucose values, and electrocardiograms (ECGs)] to their doctors. A basic solution was that the collected data of self-measured BP or blood sugar values were sent by e-mail from patients to their doctors, and the report or medical advice was sent back in the same way. The other options are mobile apps or smart devices capable of collecting and forwarding the measured BP readings. Unfortunately, an effective telemedical system is not yet established in Hungary, but the Hungarian Hypertension Registry employs special devices provided to family physicians and hypertension outpatient clinics. Office BP values are transmitted online to an evaluating center using the Medistance system of Omron[95].

More and more patients with diabetes in Hungary use the so-called D-cont e-Diary system, which means smart self-measuring devices with the ability to upload blood sugar values to the Internet and an e-diary system. This can provide structured analyses to diabetologists (the last week, month, or longer duration statistics and different timepoint of measurements regarding fasting or nonfasting condition or carbohydrate intake), which results in better care of their patients.

Anticoagulated patients were also in a challenging situation during the pandemic, mainly those treated with vitamin K antagonists, because of the need for regular international normalized ratio checks and feedback on potential dose modification from their doctors. COVID-19 caused a switch in most patients to direct oral anticoagulant (DOAC) therapy, preferred because of the lack of need for regular blood checks. Modern telemedicine solutions could facilitate the e-prescriptions of DOACs, provide online educational materials, and solve perioperative management problems.

Italy

During the first two months of lockdown (from March 9 to May 18, 2020), patients with acute conditions not related to COVID-19, or with chronic disease needing regular medical surveillance, could not easily access hospitals, general practices, or outpatient specialist clinics. This led to a dramatic reduction in hospitalization, an increase in the severity of the disease on admission, and an excess death rate in these patients[99]. The isolation at home also markedly increased the numbers of patients accessing health content on the Internet, as they were seeking qualified and certified telehealth services to help manage their condition. A significant increment in the use of home telemedicine solutions, particularly those based on m-health, has been observed during the lockdown due to the halting of face-to-face visits. The national health authority set up in March 2020 special patient monitoring units called USCA (Unità Speciali di Continuità Assistenziale or, in English, Special Units of Remote Care) in the frequency of one unit for every 50,000 inhabitants. These units included one or more physicians who could be activated at the request of the family doctor to perform interventions at the home of patients affected by COVID-19 but not requiring hospitalization[100]. This represented a rudimentary first attempt offered by the national health system to remotely manage, at scale, patients isolated at home. Concomitantly, local hospitals implemented or reinforced telemedicine services offered to Italian patients with different chronic conditions (including cardiovascular disease, diabetes, oncological disease, skin disease, neurological or psychiatric disease, and immunological disease)[101-108]. This " health maintenance strategy " provided individualized treatment and helped maintain the patient's health status as close as possible to the ideal. Unfortunately, notwithstanding the recommendations of the National Institute of Health, the use of telemedicine by patients at home was hampered by the paucity of available services and the limited ICT infrastructure of the country.

According to a recent publication, the number of home users of telemonitoring services and the number of data exchanged between patients and doctors increased during the pandemic [ Figure 3C ][109]. The proportion of abnormal values (particularly for SpO ₂ and BP) was significantly lower during the lockdown [ Figure 3D ]. However, in the same period, access to existing telemedicine and telecounseling services provided to patients daily through community pharmacies and general practices showed a dramatic drop (ranging between 49% and 85%) because of the reduced access of patients to these premises due to fear of contagion[109-111] [ Figure 3A ]. In the pharmacy setting, the overall proportion of abnormal tests was larger during the outbreak [ Figure 3B ]: in particular, an increase in the prevalence of abnormal ECGs due to myocardial ischemia was observed[109].

Russia

The COVID-19 pandemic was a stimulus for telemedicine development in Russia as well as in other countries. Changes involved two spheres: (1) direct use of teleconsultations and distant monitoring for the treatment and monitoring of infected and contact subjects; and (2) use of telehealth for chronic diseases management (hypertension, heart failure, diabetes, etc.).

Telemedicine is a part of an extensive national program for healthcare informatization in the Russian Federation, which was highly accelerated by the current situation with COVID-19. The overall system has a part called " Vertically Integrated Medical Information System", which is aimed to integrate all data from all regions in one block and analytics[112]. Currently, oncology and cardiology are two blocks that have already been introduced. The national registry of COVID-19 patients was quickly developed in April 2020, and now it is regularly updated by all regions: some of the data are downloaded directly from electronic medical records.

Limited to Moscow, and since April 2020, about 25% of outpatients with COVID-19 were managed by telemedicine services. A special telehealth center was organized for online consultations. Moreover, all chest computed tomography scans were collected in one large repository and analyzed by remote service. These innovations helped significantly reduce the load on outpatient clinics and improved the process of triage and organization of patients' flow, including urgent hospitalization and ambulance visits.

In St. Petersburg, a specialized telemedicine system with the blocks of " doctor to doctor" and " doctor to patient " services was organized in 2020 for COVID-19. It included 18 specialties and more than 100 experts for specialized consultations, including intensive care in the critically ill. Two hundred fifty medical organizations are connected in the system, and the online interaction with patients and between hospitals is being actively developed.

BP telemonitoring with remote counseling intervention was organized in several regions of the Russian Federation. For example, in St. Petersburg, a simple free website and a mobile application were developed for patient-physician communication and storage and exchange of medical information. The design of the application and the telemonitoring process were previously described[113]. During the pandemic period, the usage of the system increased fivefold compared to 2019. BP telemonitoring solutions relying on different mobile applications and remote technologies have been implemented in 44 regions of the Russian Federation.

Each region introduced separate pilot projects, some of which were very successful. In Penza, e-Health services are currently used by 18% of the population. Online patients' diaries were set up with about 8000 active users. Telehealth services are provided by state clinics and private organizations[114], while more than half of activates are organized by non-medical providers such as Sberbank, Rostelecom, and others[115,116].

Switzerland

Switzerland offers an excellent telemedical ecosystem, insomuch as several large commercial telemedical companies are headquartered in Switzerland[117]. With 24 h per day availability across the year, these services were already popular and widespread before the COVID-19 pandemic, with 2.5 million patient contacts per year[118]. Many major health insurance providers offer the " Telmed model", requiring the insured to have a telehealth consultation before visiting a medical practitioner's office[119]. This has led to almost half of all emergency hospital walk-ins to consult via a telehealth service before leaving their home[120]. As could have plausibly been expected, the number of Swiss health insurance providers offering a telemedicine consultation system for their customers rapidly increased during the first COVID-19 pandemic lockdown[121]. However, the rise in telemedicine use during the first lockdown did not involve individuals changing to telemedical services but rather using technology solutions to remain in contact with their primary care physicians[122]. Compared to commercial providers, the uptake of telemedicine by the wider Swiss medical community (e.g., general practitioners) was stagnating before the COVID-19. There were still several challenges to digitalization: (1) the lack of interoperability with their own systems; (2) security and liability concerns; (3) the inadequate representation of digital services in reimbursement tariffs; and (4) concerns about too much or too little work[117]. Unfortunately, these challenges do not seem to have markedly changed during the COVID-19 pandemic.

Nonetheless, the Swiss Association of Medical Doctors (FMH) has most recently been actively supporting the implementation of remote care services by assessing partnerships with telehealth manufacturers and by releasing a fact sheet proposing guidelines to the safe use of communication technologies and advice on billing for virtual consultations[117,123].

Furthermore, progress has been made in cardiac rehabilitation, where telerehabilitation has become a standard procedure in larger cardiovascular treatment centers. The University Hospital in Bern now provides connected devices, such as BP and heart rhythm monitors, for remote diagnosis of cardiac insufficiencies[126]. Telemedical consultations are also increasingly being used by neurologists. For instance, in specialized epilepsy centers, teleconsultation, epileptic seizure monitoring with or without wearables, and Internet-based therapy programs, are commonly established either for everyday clinical practice or in the context of scientific studies[127]. Notably, the management of COVID-19 found direct support from the domain of telemedicine, with the country-wide downloads of the "SwissCovid App" used for contact tracing, which has strengthened the population's trust in the security of healthcare data[117,126]. Other new technologies, such as the COVID-Guide, use artificial intelligence-driven chatbots for rapid self-assessment of symptoms and a personalized follow-up[128].

United Kingdom

In primary care, there has been a rapid change to much higher levels of remote (largely telephone) consulting[131] [ Figure 4 ].

Despite relying on the trailing edge technology of a standard telephone or mobile, remote consulting jumped from 30% to 89% in the months immediately before and after the start of lockdown. In the UK, the use of short message service text messaging also increased by more than three times[131]. More complex technology was far more rarely used with very few video consultations undertaken, but patients often sent photos of, for example, rashes to allow remote management. Overall, consulting rates were also reduced in the pandemic by about 10% compared to pre-pandemic levels. Few data are available regarding case mix, but small increases were seen in the rates of consultation for mental health problems and by those who were shielding (the UK term for advice to self-isolate pre-vaccine availability for people in groups considered to be " extremely vulnerable" including those with transplants, significant cancer, and immunosuppresion)[132].

In maternity care, NHS England procured 16,000 monitors, which were distributed to 127 hospital trusts nationally for use in pregnancy hypertension. This was accompanied by new guidance from the Royal College of Obstetrics and Gynaecology, providing advice on the use of self-monitoring in pregnancy for the first time in the UK. At least three providers offered apps to allow telemonitoring of results[135-137]. There are no current published data regarding these apps, although evaluations are in progress.

Progress and Challenges in Telemedicine During Covid-19

The use of telemedicine during the recent pandemic has been rated as highly satisfactory by users, and, as a consequence, the majority of the patients and healthcare providers reported a willingness to continue using telemedicine after the pandemic[138,139]. In a global survey conducted in May 2021 involving 293 professionals across the healthcare delivery spectrum, 73% of respondents stated that telemedicine was the subsegment of digital health that experienced the largest growth during the pandemic[140]. In a previous survey conducted at the beginning of the pandemic, the percentage of positive responses was slightly lower (65%)[140].

The situation relative to the adoption of telemedicine in the various continents or countries during COVID-19 as presented in the specific sections of this paper appears to be fragmented and heterogenous. The level of implementation, main barriers to uptake, primary services provided, and availability of insurance reimbursement plans and telehealth policies at a national level are summarized in Table 4 . When insurance reimbursements or telehealth policies were available, these were limited to specific services and, in most cases, the pandemic period: in federal countries such as the US, the regulations may have varied from state to state.

Table 4

Continent/Country

Implementation level

Main barriers to implementation

Main services (in order of importance)

Insurance reimbursement

Specific telehealth policies at a national level

Africa

Low

General and digital illiteracy

No

No

Canada

Medium

Yes (partial)

No

United States of America

Medium/High

Yes (partial)

Yes (partial)

Latina America

Low

No

Yes (partial)

Western Asia

Low/Medium

No

No

China

High

* General and digital illiteracy

No

No

Japan

Medium

No

Yes (partial)

Italy

Low/Medium

No

Yes (partial)

Russia

Low

No

No

As summarized in Table 5 , during the COVID-19 outbreak, several steps forward in adopting telemedicine have been made. Although some pre-existing barriers to the pandemic were removed, important gaps still need to be filled in to favor large-scale implementation of telemedicine. Technological, infrastructural, educational, economic, and legal issues still undermine the long-term sustainability of telemedicine beyond the COVID-19 pandemic.

Table 5

Progress

Gaps

Several countries relaxed laws and regulations pertaining to the use of telemedicine (licensing of healthcare operators, privacy, reimbursement)

Many countries have issued national guidelines and protocols guiding the implementation of telemedicine in the community

Insurance companies and national health authorities started reimbursing expenses for patient care delivered via telemedicine

Increased awareness of the usefulness of telemedicine among healthcare professionals and patients

Increased adoption of telemedicine solutions (particularly televisit and telemonitoring) in the majority of countries worldwide

More older patients with chronic diseases moved online compared to pre-COVID-19

Increased use of applications based on AI that may improve diagnostic accuracy and treatment, transforming healthcare management from passive to active or proactive

Increased integration of telemedicine with traditional (in-person) healthcare services

Several countries are still affected by the lack of policy to legislate telemedicine

Current guidelines are often generic and do not provide practical recommendations for the routine clinical use of telemedicine (target population, types of application, remuneration, etc.)

There is no integration and standardization at an international level among protocols and guidelines

Health reimbursement plans may not be available in some countries and may not be provided to all the different social strata of the population

Funding frameworks for telemedicine in the context of public healthcare must be defined

Illiteracy in low- and middle-income countries reduces the awareness about the importance of telemedicine

Clinicians' unwillingness to adopt telemedicine persists in some cases and needs to be overcome with adequate training and education

The technological and infrastructural requirements and the high costs of telemedicine bear the risk of widening inequity among countries with different income levels and across various population subgroups

Tailored solutions according to users' features must be envisaged to scale up the use of telemedicine

Most advanced solutions may not be affordable to all subjects and may be available only in high-income countries

Quality of care in telemedicine is not always optimal compared to in-person care

Heterogeneity of available solutions and technologies do not allow cross-platform interoperability and easy data exchange

Reorganization of the healthcare network is necessary to switch toutpatient remote management

Perspectives and Conclusions

During the COVID-19 pandemic, progress has been made in the adoption of telemedicine to screen for infected people, oversee affected subjects, and ensure continuity of care of chronically ill people. However, the use of telemedicine was not homogeneous across the various countries hit by the pandemic. This was due to differences in the awareness of the importance of telemedicine, variability in the quality of the infrastructures, level of informatics literacy of healthcare professionals and patients, and reimbursement schemes and plans. The experience collected during the COVID-19 pandemic should help strengthen available solutions and develop a more coordinated general strategy to favor the implementation of telemedicine at scale in the healthcare system. Achieving this goal will help prepare well for future pandemic waves and ultimately improve the management of COVID-19 and non-COVID-19 patients. In particular, second-line home-based telemedicine-based solutions serving as " virtual hospitals " may help reduce the burden on the first line, particularly emergency departments and intensive care units.

Financial support and sponsorship

Richard McManus is a National Institute for Health Research (NIHR) Senior Investigator (NIHR200201) and received funding from the NIHR Applied Research Collaboration Oxford and Thames Valley (NIHR200172). Lu Yuan is supported by the National Heart, Lung, and Blood Institute (K12HL138037) and the Yale Center for Implementation Science. She was a recipient of a research agreement, through Yale University, from the Shenzhen Center for Health Information for work to advance intelligent disease prevention and health promotion.

Declarations

Contributed by

Authors' contributions

Made the literature search and drafted the paper: Omboni S Revised the draft and helped finalize the paper: Padwal RS

Writing of the sections related to their country and revised the final version of the paper: Omboni S, Padwal RS, Alessa T, Benczúr B, Green BB, Hubbard I, Kario K, Khan NA, Konradi A, Logan AG, Lu Y, Mars M, McManus RJ, Melville S, Neumann CL, Parati G, Renna NF, Ryvlin P, Saner H, Schutte AE, Wang JG

.

Conflicts of interest

Stefano Omboni is scientific consultant of Biotechmed Ltd., an Italian telemedicine company. Raj Padwal is CEO of mmHg Inc., a digital health software development company and provider of cloud-based home and ambulatory BP telemonitoring. Richard McManus is working with Omron to develop and evaluate the Hypertension Plus Telemonitoring system. Aletta Schutte received consultant fees from Abbott, and speaker honoraria from Servier, Sanofi, Takeda, Novartis, and Sun Pharmaceuticals, and is Immediate Past President of the International Society of Hypertension. Other authors declared that there are no conflicts of interest.

Ethical approval and consent to participate

Not applicable.

Consent for publication

Not applicable

Availability of data and materials

Not applicable.

References

49. Webber EC, McMillen BD, Willis DR. Health care disparities and access to video visits before and after the COVID-19 pandemic: findings from a patient survey in primary care. Telemed J E Health. 2021 doi: 10.1089/tmj.2021.0126. [[ PubMed] [ CrossRef] Google Scholar]

56. Herrera AV. Telemedicine: a little exploited tool. Rev Obstet Ginecol - Hosp Santiago Oriente Dr Luis Tisne Brousse. 2006;1:233-6. [ Google Scholar]

62. Busso M, González MP, Scartascini C. (IDB Working Paper Series).On demand for telemedicine: evidence from the COVID-19 pandemic. 2021 doi: 10.18235/0003225. [ CrossRef]

63. ClinicalTrials.gov. Improving hypertension control in CHina and ARGEntina with a Mobile APP-based Telecare System (CHARGE-APP) [Last accessed on 22 Dec 2021]. Available from: https://clinicaltrials.gov/ct2/show/ NCT03206814?term= NCT03206814&draw=2&rank=1 .

86. Scheidt-Nave C, Barnes B, Beyer AK, Busch M, Hapke U, Heidemann C. Care for the chronically ill in Germany - The challenges during the COVID-19 pandemic. Journal of Health Monitoring. 2021 doi: 10.25646/7168. [[ PMC free article] [ PubMed] [ CrossRef] Google Scholar]

108. Bergamaschi R, Tronconi L, Bosone D, et al. Description and preliminary experience with Virtual Visit Assessment (ViVA) during the COVID-19 pandemic, a structured virtual management protocol for patients with multiple sclerosis. Neurol Sci. 2021 doi: 10.1007/s10072-021-05371-3. [[ PMC free article] [ PubMed] [ CrossRef] Google Scholar]

128. Marini M, Chokani N, Abhari RS. COVID-19 Epidemic in Switzerland: Growth Prediction and Containment Strategy Using Artificial Intelligence and Big Data. medRxiv. 2020 doi: 10.1101/2020.03.30.20047472. [[ CrossRef] Google Scholar]

# data[].attributes.attachment_link.title
The worldwide impact of telemedicine during COVID-19: current evidence and recommendations for the future

# data[].attributes.attachment_link.description
Connect Health. Author manuscript; available in PMC 2022 Feb 28. Published in final edited form as: Stefano Omboni, 1, 2 Raj S. Padwal, 3 Tourkiah Alessa, 4 Béla Benczúr, 5 Beverly B. Green, 6 Ilona Hubbard, 7 Kazuomi Kario, 8 Nadia A. Khan, 9 Alexandra Konradi, 10 Alexander G.

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Key Takeaways:
-HIE provides a way for health care providers to securely share patient information electronically
-Current efforts for HIE adoption vary from state to state
-State-wide HIE programs rely on grant funding

This week I'm posting a blog article that suggests HIE regulations should be more robust and should take a national approach rather than state by state. A national approach was considered in 2009, however a regional strategy was chosen to stimulate competition with the goal of improved technologies. The authors suggest the current state of HIEs has fallen short of this goal and that the U.S. would be better served by a national HIE program.

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Key Takeaways:
-Computerized Provider Order Entry (CPOE) works with the EMAR to reduce medication errors
-specialty applications can be integrated into EHRs
-Clinical decision support helps providers drive practice and keep patients safe

The study I'm posting this week is a systematic review and meta-analysis showing that clinical decision support systems have been shown to improve physician performance and patient outcomes. Interestingly, the article breaks down effectiveness by various specialties, diseases and diagnoses.

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Research article

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BMC Medical Informatics and Decision Making 21, Article number: 98 (2021) Cite this article

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Abstract

Background

Clinical Decision Support Systems (CDSSs) for Prescribing are one of the innovations designed to improve physician practice performance and patient outcomes by reducing prescription errors. This study was therefore conducted to examine the effects of various CDSSs on physician practice performance and patient outcomes.

Methods

This systematic review was carried out by searching PubMed, Embase, Web of Science, Scopus, and Cochrane Library from 2005 to 2019. The studies were independently reviewed by two researchers. Any discrepancies in the eligibility of the studies between the two researchers were then resolved by consulting the third researcher. In the next step, we performed a meta-analysis based on medication subgroups, CDSS-type subgroups, and outcome categories. Also, we provided the narrative style of the findings. In the meantime, we used a random-effects model to estimate the effects of CDSS on patient outcomes and physician practice performance with a 95% confidence interval. Q statistics and I ² were then used to calculate heterogeneity.

Results

On the basis of the inclusion criteria, 45 studies were qualified for analysis in this study. CDSS for prescription drugs/COPE has been used for various diseases such as cardiovascular diseases, hypertension, diabetes, gastrointestinal and respiratory diseases, AIDS, appendicitis, kidney disease, malaria, high blood potassium, and mental diseases. In the meantime, other cases such as concurrent prescribing of multiple medications for patients and their effects on the above-mentioned results have been analyzed. The study shows that in some cases the use of CDSS has beneficial effects on patient outcomes and physician practice performance (std diff in means = 0.084, 95% CI 0.067 to 0.102). It was also statistically significant for outcome categories such as those demonstrating better results for physician practice performance and patient outcomes or both. However, there was no significant difference between some other cases and traditional approaches. We assume that this may be due to the disease type, the quantity, and the type of CDSS criteria that affected the comparison. Overall, the results of this study show positive effects on performance for all forms of CDSSs.

Conclusions

Our results indicate that the positive effects of the CDSS can be due to factors such as user-friendliness, compliance with clinical guidelines, patient and physician cooperation, integration of electronic health records, CDSS, and pharmaceutical systems, consideration of the views of physicians in assessing the importance of CDSS alerts, and the real-time alerts in the prescription.

Peer Review reports

Background

Various types of CDSS systems based on clinical guidelines, alerts, reminders, instructions, and recommendations are included in this study. For instance, the alert-based type of CDSS uses reminders and drug interaction alerts [ 7]. CDSS benefits involve reducing prescribing errors by using alerts and immediate reminders, automated dosing error checks, and drug interactions. E-prescribing systems with support for clinical decision-making have the potential to decrease errors and improve clinical practice [ 8]. The assessment of the effects of all computerized health care interventions is important in managing the health care process and patient outcomes [ 9]. Over the past years, a number of systematic studies have been conducted with the goal of analyzing the effect of CDSSs on prescription errors or CPOEs on patient safety, the care process, or the performance of physicians. In 2003, a systematic review of the two major databases revealed a reduction in drug errors due to the use of CDSS; however, the specifics of the findings have not been disclosed [ 10]. Another systematic review was also conducted in 2008 with an emphasis on the effects of CPOEs on medication errors. The results of this study showed a decrease in risk failure errors in 23 out of 25 included studies. While demonstrating the effectiveness of CPOEs, this research did not explain the outcome of the patients [ 11]. In the same way, another systematic review examined the effect of CDSS on prescribing errors in 2010. Since this analysis omitted the Randomized Controlled Trial (RCT) tests, the findings indicated a small change in the patient outcome. However, there has been a significant improvement in the care outcome process [ 3]. In another study in 2015, a review of the systematic reviews of the CDSS on patient safety was conducted. The results of this study showed improvement in outcomes. However, the authors argued that they need to include more studies with greater data pools in order to be able to further validate the CDSSs effect on outcomes [ 12]. In this systematic review, the most recent sample was collected in 2014 on a limited medication laboratory domain for some particular diseases [ 13]. In addition, another systematic study was carried out in 2017 to evaluate the effects of various forms of alerts on patient safety and medical outcomes. Surprisingly, the findings of the study showed no significant difference between various types of alerts except for some interrupting alerts that did not contribute to any improvement in outcomes [ 14]. CPOE was used for pediatrics in another systematic study whose purpose was to determine the errors. Results of this study demonstrated the usefulness of the system [ 15].

Considering the literature we have reviewed so far, the results of most studies have indicated the efficacy of CDSS compared to conventional clinical practices. The literature on CDSS has also shown progress in the physicians' efficiency; however, the effect of these programs on patient outcomes is still uncertain [ 3, 16, 17, 18]. Due to the fact that CDSSs have been verified as useful tools to reduce prescribing errors, we decided to consider all types of CDSSs for all diseases and patients since 2005. Given the importance of CDSSs, in the present study, we examine the effects of CDSSs on physician prescribing performance and patient outcomes.

Methods

We used a systematic review and meta-analysis in this study. The method section is divided into a variety of subsections, including search strategy, inclusion/exclusion criteria, screening and data extraction, quality assessment, data synthesis, and statistical analysis. Each subsection is described in more detail, as follows.

Search strategy

The initial search was performed in PubMed to identify the keywords. We used Medical Subject Headings (MeSH) in PubMed, Emtree in Embase, and other words/phrases used in related papers as the basis for a search strategy. The major search was then conducted in PubMed, Embase, Web of Science, Scopus, and Cochrane Library. We performed the search in 2018 and used an approach tailored for each database without any language restrictions. Alerts were used to access published papers after the search date, and all database alerts were checked until July 2019. Reference tracking and citation search were also used to improve the retrieval of eligible studies. An example of the complete search strategy is given below:

(("clinical decision support system*" OR "clinical Decision Support*" OR "computerized decision support tool*" OR "Information System*" OR "computerized physician order entry*" OR "hospital information system*" OR "computerized medical record system*" OR "point-of-care system*" OR "medical order entry system*" OR "computer-assisted decision making" OR "computerized medical record system*" OR "reminder system*" OR "computer-assisted diagnosis" OR "clinical informatics*")) AND ("medical mistake*" OR "medical error*" OR "therapeutic error*" OR "diagnostic error*" OR "drug interaction*" OR "drug dose-response relationship" OR "drug administration schedule" OR "drug monitoring").

Registration number on PROSPERO is CRD42018079936 [ 19].

Inclusion/exclusion criteria

We used the PICO criterion to conduct the search strategy: Participants (P) were individual practitioners or graduate trainees (e.g. medical residents); intervention (I) was any form of CDSS/CPOE system applied to the prescribing process; comparator (C) were those papers that used other systems or did not use any system; outcome (O) was any patient outcomes and physician performance outcomes. In this study, we included randomized CDSS clinical trial papers such as alert-based, recommendation-based, instruction-based, and reminder-based systems to assess their effects on patients and providers. In selecting a paper for this study, we first prepared a list of questions whose answers form the key criterion for inclusion as follows:

Does the research concentrate on assessing the prescribing CDSS/CPOE based on any category of patient outcomes and physician performance outcomes?

Is the study a randomized clinical trial in which the patient care was compared with and without prescribing CDSS/CPOE?

Have experts such as physicians, specialists, and residents used the CDSS for prescribing CPOE in these studies?

Does the decision support system/CPOE evaluate patient-specific information in the form of management or likelihood choices or recommendations for physicians?

Has the practice been identified as a measure of the improved care process or the outcome of patients with any improvement in the study?

We excluded non-experimental studies as well as the studies in which the system was used exclusively by students who were not experts, or "no" was given as the answer to these five main questions.

Screening and data extraction

The papers were screened in three separate steps based on title, abstract, and full text. In the meantime, we used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist as a reporting guide-line in our study. The results of the search are shown in Fig. 1. PRISMA checklist is a well-established standardized checklist for systematic review studies [ 20]. The evaluation was carried out by two authors of this study (S.T) and (F.V). The selection, screening, and data extraction phases were independently performed to prevent bias. Any differences between researchers have been resolved by consulting an expert in this field (F.S). The data extracted from the included studies are first author, year of publication, country, and type of disease, design of the study, intervention, and type of intervention, number of centers/providers/patients, patient outcomes, provider outcomes, outcome impact, and statistical output.

Quality assessment

We assessed the quality of studies by Jadad scale, the Oxford research methods scoring system for bias in clinical trials [ 21, 22]. We also determined the quality score by adding total scores for each sample. Meanwhile, we used zero and one for the exclusion/inclusion of items such as randomization, blindness, removal, dropouts, inclusion criteria, assessment of findings, and explanation of the statistical analysis.

Data synthesis and statistical analysis

We extracted data from qualified articles using a structured data extraction method. The findings of the studies were presented in a descriptive-narrative form. In the meantime, we have conducted a meta-analysis with Comprehensive Meta-Analysis (CMA) statistical tools [ 23]. For all the analyzed data, the assessments of both the CDSS and the control groups were summarized as the mean standard deviation for each study and the comparison of pooled estimates between the intervention group and the control group. An effect size of std diff in the means of change in outcomes between groups was presented as standard error and 95% CI. The size of the effect with a lower limit greater than 0 implies that the intervention group has a positive effect on the outcome. The CDSS group does not affect the outcome compared to the control group when the lower limit is less than 0. Also, when std diff in means equals 0, it means that the change in outcomes was similar between the CDSS and the control groups. Meta-analysis using a random-effects model was performed to predict physician practice performance and patient outcomes. We used Q statistics and I2 to calculate heterogeneity (I2 greater than 50% is considered heterogeneous). Sensitivity analysis was also conducted to define and reduce the sources of heterogeneity. In the next step, the funnel plot was used to assess publication bias. A funnel plot is a valuable method for assessing potential visual publication bias [ 24].

Results

On the basis of the inclusion criteria, we selected 45 qualified articles (Fig. 1). The assessment of the studies provided us with valuable information on the research goals, the types of electronic prescription systems, the types of diseases, and patients. Table 1 indicates that the findings of the quality evaluation of the studies were appropriate.

Table 1 Quality assessment for trials

The findings also demonstrated the effectiveness of CDSS in many diseases such as cardiovascular disease, high blood pressure, and diabetes, or cases such as simultaneous prescription of drugs. Findings from the analyzed studies are presented in Table 2 in which * stands for p values indicating a statistically significant difference.

Table 2 Data extracted for CDSS trials

The number of studies based on multiple evaluation results and types of studies is also shown in Figs. 2 and 3, respectively. Table 2 shows the variety of outcomes for several medication scopes (for example, the outcome "Increasing the ratio of prescribing prophylaxis" is specific for cardiovascular domain, or the outcome "Reducing blood pressure" is related to hypertension disorders). Meanwhile, Table 2 shows various kinds of CDSSs for prescribing classified according to alerts, reminders, recommendations, instruction, and a combination of these types. Table 2 also briefly presents the outcome of the thirteen medication scopes involved.

The effect of CDSS on cardiovascular diseases

For patients admitted to the hospital, the level of venous thromboembolism prophylaxis, and the proportion of prescribed prophylaxis increased during 6-24 h after admission [ 25]. In another study, the discrepancies among physicians over the thromboprophylaxis treatment decreased with the aid of CDSS by offering treatment recommendations ( p = 0.02) [ 26]. In other studies, alert-based CDSSs have positive effects on physician performance and treatment improvement in anti-inflammatory and lipid-lowering drugs [ 28, 29, 31]. By following medical recommendations in another study, physicians in the intervention group were able to improve the prescribing level of secondary preventive medication through using a regular CDSS [ 30]. Also, in other trials, the short messages of the program had a positive effect on patient adherence to medication and diet ( p < 0.01) [ 32, 33].

The effect of CDSS on hypertension

In one study, the electronic monitoring and recall program had no effect on blood pressure reduction and the admission of patients [ 34]. However, in another study, the patient outcome improved following the implementation of the CDSS [ 35].

The effect of CDSS on diabetes

In some studies, the Real-Time Medication Monitoring (RTMM) system, equipped with a short message reminder, improved the precision of patients' compliance and missed dose [ 36, 37, 39, 40]. In another study, HbA1c and group differences were greater in the intervention group using recommendation CDSS than that of the control group [ 38]. The use of statins ( p = 0.03) and the problem areas in diabetes (PAID) ( p = 0.01) improved in another study for the intervention group that used CDSS [ 37].

The effect of CDSS on digestive diseases

In all studies, the CDSS had an effect on prescribing non-steroidal anti-inflammatory drugs, proton pump inhibitors, and increasing the standard use of oral rehydration solution without any difference in other results [ 41, 42, 43]. Also, alert-based CDSS improved the quality of patient care in another study [ 42].

The effect of CDSS on pulmonary diseases

In some trials, the use of CDSS which was integrated with electronic health record or prediction rules resulted in a decrease in the prescribing of antibiotics and macrolides; therefore, it helped minimize the inappropriate use of antibiotics ( p = 0.0005), reduce the resistance to antibiotics ( p = 0.04), and enhance primary care [ 44, 45, 46, 47, 49]. The patients adhered to the reminder message in another study; however, the messages did not affect the success rate of therapy [ 48].

The effect of CDSS on AIDS

Results of this study showed that the reminder system for short text messages had a positive effect on the treatment process. Also, the number of messages did not have a significant effect on patients' compliance rates ( p = 0.12) [ 50].

The effect of CDSS on appendicitis

This study showed that the system's systematically developed order set, which used clinical guidelines, improved system usability ( p = 0.05), and reduced system-related problems ( p = 0.05). This is the result of Computerized Provider Order Entry (CPOE) which improved efficiency, quality, and safety [ 51].

The effect of CDSS on kidney diseases

One study showed the positive effect of the multipurpose intervention on creatinine value estimation and dose adjustment to reduce the insufficient dosage of primary care drugs [ 53]. In another study, the appropriate prescription rate for kidney problems was low, as opposed to the results of the former study. Also, the effectiveness of the CDSS with physician guidelines has been improved [ 52].

The effect of CDSS on taking multiple medications

In one study, CDSSs resulted in delayed drug treatment for four patients needing urgent treatment. This suggests that the adverse effects of these systems need to be evaluated and monitored [ 56]. In another study, the CDSS improved the primary dose of medication, time intervals for drug use, and drug concentration which is to be injected intravenously compared to standard doses [ 54]. Also in another study, the average number of readmission days for each patient and the combination of re-hospitalization and emergency ward visits within 30 days after hospital discharge did not vary between the intervention group using recommendation CDSS and control groups [ 58]. In some trials, there was no discrepancy between the outcomes of the dosage rate and the Modified Medication Appropriateness Index (MMAI).

In the meanwhile, no discrepancy was seen among improper medication prescribing ( p = 0.48), the Medication Regimen Complexity Index, and the mean pain outcome difference after 6 months ( p = 0.13) and 9 months ( p = 0.78) between the intervention group using alert or reminder CDSS and the control group [ 55, 57].

The effect of CDSS on Malaria

The use of text-messaging in one study did not affect the patients' behavior in completing the course of medication for the full duration of treatment. However, when the side effects were low ( p = 0.02), it had some effects on the continuous use of the medication. In addition, text messages had an effect on physicians' knowledge about the use of medications with fatty foods. ( p < 0.0001) [ 59].

The effect of CDSS on increasing the level of blood potassium

In one study, there is no statistical difference between the control and intervention groups in terms of following alerts and patients' compliance rate. However, the physicians' compliance rate improved at the medium potassium level from 3 to 3.9 (mili-equivalents/liter) ( p < 0.01) [ 61]. Due to the rapid response of physicians to program alerts for high potassium levels in the intervention group, the positive effect of the system on physician performance was evident in another study ( p = 0.01) [ 62]. However, in another study in this section, the time-lapse in hyperkalemia monitoring ( p = 0.20) and the incidence rate of hyperkalemia ( p = 0.22) did not vary significantly even with the use of three different kinds of reminder and alert-based CDSSs [ 60].

The effect of CDSS on medication prescription for patients

Based on the results of some studies, the regular or alert based CDSSs resulted in better drug prescriptions for the proton pump inhibitor and a reduction in abbreviation prescriptions [ 5, 64]. Also, in other studies, the overall utilization of system functionalities, system utilization between two-time laps ( p < 0.0001), number of users ( p < 0.0001), and physicians' compliance with the medication recommendations provided by the CDSS improved medication prescriptions which eventually resulted in reduced side effects ( p = 0.02) [ 6, 63]. There was no difference in prescribing among physicians in one study ( p = 0.14); however, the percentage of skilled questions for the intervention group equipped with training CDSS ( p = 0.01) improved [ 65]. In another study, alert-based CDSSs have been effective in identifying evidence-based pharmacotherapies (EBP). In the meantime, compliance with treatment by health care managers has had no effect on patient outcome [ 66].

The effect of CDSS on mental disorders

CDSS alerts resulted in reduced risk of injury and reduced dose of antipsychotics and anticoagulants ( p = 0.03) over a one-year period. Therefore, CDSS reduced the risk of injury ( p = 0.02) [ 67].

Statistical and sensitivity analysis

The pooled std diff in means of p values showed a significant difference between the CDSS and the control group (std diff in means = 0.091, 95% CI 0.072-0.109, standard error = 0.010). 95% CI for the effectiveness was drawn for each study in the horizontal line format (Q = 209.2, df = 45, p = 0.0002, I2 = 78.492, Tau2: 0.004) (Fig. 4). Due to the high heterogeneity of results, a sensitivity analysis was performed. In doing so, we excluded the following studies: khonsari et al. [ 33]; Ackerman et al. [ 49]; Avansino et al. [ 51], and Bruxvoort et al. [ 59]. Because of the limited number of patients in these trials, we decided to exclude them from our meta-analysis. In Tables 2 and 3, the characteristics of these studies are presented in narrative results. The findings indicate that heterogeneity improved considerably after sensitivity analysis (Fig. 5). (Q = 164.8, df = 41, p  = 0.0001, I2 = 75.136, Tau2: 0.003). The overall effect of CDSS for prescribing medications on patient outcomes and physician practice performance based on the random-effects model was statistically significant (std diff in means = 0.84, 95% CI 0.067-0.102).

Forest plot of the overall effect of CDSS for prescribing on physician practice performance and patient outcome based on medication subgroup analysis. Meta-analysis is conducted using Comprehensive Meta-Analysis (CMA) statistical tools. The pooled std diff in means of p values showed a significant difference between the CDSS and the control group (std diff in means = 0.091, 95% CI 0.072-0.109, standard error = 0.010). Confidence Interval (CI) represents for the linear area between lower and upper limits

Table 3 Outcome classification for trials

Forest plot of the overall effect of CDSS for prescribing on physician practice performance and patient outcome based on medication subgroup analysis after sensitivity analysis. After sensitivity analysis, heterogeneity improved considerably, excluding khonsari et al. [ 33]; Ackerman et al. [ 49]; Avansino et al. [ 51], and Bruxvoort et al. [ 59]. The pooled std diff in means of p values was used for evaluating the overall and subgroup effects of CDSS which were significantly different (std diff in means = 0.084, 95% CI 0.067-0.102) as a whole. Meta-analysis results for each subgroup of medication scope showed a significant difference between CDSS and control groups for medication scopes namely as hypertension (CI 0.102-0.272); increasing blood potassium (CI 0.006-0.066); multiple medications (CI 0.084-0.332); AIDs (CI 0.038-0.444); kidney disorders (CI 0.073-0.193); diabetes (CI 0.223-0.539); cardiac (CI 0.035-0.111); mental disease (CI 0.010-0.114); medication prescription (CI 0.094-0.219); and pulmonary disease (CI 0.014-0.144)

Subgroup analysis for medication scope

Figure 5 shows the results of the meta-analysis for each subgroup of medication scope and the total analysis. Subgroup analysis is performed on different medication groups because there have been common outcomes in related similar medication scope studies. The subgroup analysis showed a significant difference between CDSS and control groups for medication scopes namely as hypertension: (std diff in means = 0.187, 95% CI 0.102-0.272); increasing blood potassium: (std diff in means = 0.036, 95% CI 0.006-0.066), multiple medications: (std diff in means = 0.208, 95% CI 0.084-0.332), AIDs: (std diff in means = 0.241, 95% CI 0.038-0.444), kidney disorders: (std diff in means = 0.133, 95% CI 0.073-0.193), diabetes: (std diff in means = 0.381, 95% CI 0.223-0.539), cardiac: (std diff in means = 0.073, 95% CI 0.035-0.111), mental diseases: (std diff in means = 0.062, 95% CI 0.010-0.114), medication prescription for patients: (std diff in means = 0.157, 95% CI 0.094-0.219), and pulmonary diseases: (std diff in means = 0.079, 95% CI 0.014-0.144). However, there was no significant difference between the intervention and control group for digestive diseases: (std diff in means = 0.182, 95% CI − 0.072 to 0.437). Figure 5 shows the forest plot for subgroup meta-analysis. However, we eliminated malaria and appendicitis diseases due to the decrease of heterogeneity among studies. We then described malaria and appendicitis diseases in narrative results. Also, Figs. 6 and 7 show the number of studies associated with each country and type of CDSS.

Categorization of outcomes

Physician practice performance and patient outcomes are presented in Table 3 as primary outcomes are categorized based on the summary of the outcome concept and the impact of CDSS. Improvement or neutrality in outcomes is shown by plus or zero in Table 3. We categorized outcomes because similar outcomes may have different impacts on various diseases. For instance, the outcome "decrease prescribing" may have a positive effect on some diseases and no effect on other medication domains.

Evaluation for publication bias

We conducted a funnel plot and Egger's regression to evaluate the publication bias regarding the effect of CDSS on patient outcomes and physician performance [ 68, 69]. There was no significant difference with respect to publication bias (std diff in means = 0.054, CI 95%: 2.116 to 2.941, p = 0.000001). Figure 8 depicts that the X-axis shows std diff in mean in the funnel diagram, and the Y-axis reflects standard error.

Funnel plot of standard error by std diff in means. There was no significant difference for publication bias for the included studies ( p value = 0.000001). X-axis shows std diff in mean in the funnel diagram and the Y-axis reflects standard error. Dispersion of studies in the funnel plot showed that there was no bias in publication

Discussion

The aim of this systematic review is to establish the effect of CDSS on patient outcomes and physician performance. The effect of CDSS was measured using different methods in the included studies. In most cases, the effect of these programs on physician performance and patient outcomes were positive. In others, however, no significant effect has been found.

The results show that the use of CDSSs in cardiovascular patients has positive effects on physician performance by increasing the prescription of anticoagulants, anti-inflammatory drugs, anti-thrombotic drugs, lipid-lowering drugs, blood pressure drugs, cardiovascular drugs recommended for the reduction of cardiovascular diseases in patients with diabetes, and observing clinical guidelines [ 25, 27, 28, 29]. The results of the current study are consistent with the results of Duke et al. and Brokel et al. in reducing inadequate prescriptions and enhancing the process of observing clinical guidelines [ 61, 70]. Also, the system's user-friendliness environment and low running cost have resulted in its efficiency in the care delivery process [ 25, 27, 28, 29].

However, the results of our study have shown that using CDSSs for cardiac patients did not affect the physician performance in a number of outcomes such as physician conduct in prescribing drugs, the Warfarin treatment system, reducing frustration with antithrombotic diagnostic guidelines, and job satisfaction [ 26, 29, 30, 31]. The results of this study are also consistent with Byrnes and Lazaro studies in that clinical factors and treatment issues were the reasons for physicians' disagreement with system recommendations [ 71, 72]. The key explanation why there was no improvement in medical guidance is the complexity of clinical problems that could increase the risk of injury to the patient and delay the decision-making process [ 26, 29, 30, 31].

Also, the results of this study indicate that the use of CDSSs in cardiovascular patients has a positive effect on a number of outcomes such as adherence to drug use by patients and following a nutrition-based diet in the Mediterranean [ 32, 33]. Similarly, according to clinical guidelines and reminders, Schedlbauer et al.'s study reported the positive effect of CDSS on cardiovascular patient outcomes [ 73]. The reason for poor adherence to the Mediterranean diet was the delivery of a short message outlining the advantages of the Mediterranean diet which resulted in an improved conformity level [ 32, 33].

The study also showed that the use of CDSS in cardiovascular patients did not affect patient outcomes such as readmission rate, mortality, and smoking cessation [ 32, 33]. Similarly, the findings of Simpson et al.'s study indicate that accurate compliance with the Short Message System (SMS) reduces mortality risk and improves health outcomes [ 74]. One of the reasons for the negligible reduction in mortality is the short duration of the study, small sample size, and inability to identify causes of mortality [ 32, 33]. Also, study results show that the use of CDSS in patients with hypertension in adherence to clinical guidelines and laboratory tests has a positive effect on physician performance [ 35]. Zwart et al.'s study, which is consistent with the results of our study, assessed the impact of CDSS on adherence to clinical guidelines. The study reported effective results about the treatment of pregnant women with hypertensive disorders [ 75]. In addition, physicians' awareness of special care during pregnancy for hypertension resulted in improved patient care and adherence to CDSS [ 35].

Based on the results of this research, the use of CDSS in diabetic patients has a positive effect on physician performance in a variety of outcomes such as adjusting the form of insulin and improving the quality of decision-making about statin prescription [ 36, 37, 38]. The findings of Den Ouden et al.'s and Mann et al.'s studies are also consistent with the results of our review which suggest physicians' strong adherence to CDSS, enhanced statin prescribing, and improved quality of medical care [ 76, 77]. In fact, the CDSS dynamically recommends the insulin dose based on the rounds of previous days, the type of insulin injected, and the glucose level of the patient on the day before [ 36, 37, 38].

Also, the results of this study show that the use of CDSS in digestive disorders has a positive effect on the physician performance in a variety of outcomes such as the standard use of oral rehydration solution, the prescription of non-steroidal anti-inflammatory drugs and proton pump inhibitors in normal and high-risk patients, and the provision of care services in line with the guidelines for primary care [ 41, 42, 43]. The results of this study are also consistent with the findings of Nicastro's study which stated that the system had positive effects on physician performance such as adherence to clinical guidelines and prescription of drugs [ 80]. The reason for the positive effect of CDSS on the prescription of non-steroidal anti-inflammatory drugs and proton pump inhibitors in high-risk patients and the use of oral rehydration solution was the systems' recommendations about the above-mentioned drugs [ 41, 42, 43].

The results of this study also showed that the use of CDSS in respiratory patients had a positive effect on physician performance and reduced antibiotic prescription [ 44, 45, 46, 47, 49]. The results of this study are therefore consistent with the findings of Mcdermott et al.'s and Butler et al.'s results on the positive effect of CDSS on the self-efficacy of physicians in managing chronic respiratory patients and reducing the prescription of antibiotics [ 81, 82]. We think that the reason for the system's positive effect on the self-efficacy of physicians was their tendency to cooperate on decision-making and not to receive mandatory CDSS recommendations [ 44, 45, 46, 47, 49].

With respect to appendicitis, the results of our review indicate that the use of CDSS has a positive effect on physician performance in certain outcomes such as performance, quality, and safety with the assistance of physicians' computerized order entry [ 51]. The results of this review are in line with Holden's study which explores how physicians using the order entry system would receive more up-to-date information and boost the system's capabilities [ 85]. Although prescriptions are not strong in terms of content, errors are decreased as CPOE encourages physicians to consider cases [ 51].

Based on the results of our review, the use of CDSS in patients with high blood potassium levels has a positive effect on physicians' performance in some outcomes such as the faster physicians' response in the intervention group to system alerts and reminders [ 62]. is study are also consistent with Helmous et al.'s and Paterno et al.'s reports which show that physicians' adherence to alerts improved by 19 percent [ 88, 89]. The key explanation for the positive effect of CDSS on physician performance was uninterrupted alerts and reminders [ 62].

Results of the study showed that the use of CDSS in prescribing drugs for patients has a positive effect on physician performance in certain outcomes such as drug prescription for proton pump inhibitors, CDSS productivity and usability, reduction of drug side effects, and improving the learning rate and physicians' skills [ 5, 6, 63, 64, 65]. The results of this study are consistent with the results of Curtis and Shah et al.'s study indicating that relevant CDSS, while providing users with performance-related information, reduces patients' harms and errors, and increases physicians' knowledge and skills [ 90, 91]. One of the main reasons for the proton pump's enhanced medication performance was the control of prescription drug dose by physicians as well as equipping pharmacies with CDSS with hard alerts which reduce costs and improve usability [ 5, 6, 63, 64, 65].

Analysis of the results of the reviewed studies shows that the use of CDSS in prescribing a number of drugs has no effect on the physician performance in outcomes such as drug prescription rates with drug suitability index, average functional status outcome, and drug complexity index [ 55, 56, 57]. The results of our study are consistent with Olsson's study which shows that CDSS for elderly people, who use multiple types of medicines, has no effect on important outcomes [ 94]. We conclude that the unexpected findings of our review may be due to the lack of information about patients with serious infections who require immediate care and the lack of an efficient checklist monitoring the patients' drug problems [ 55, 56, 57].

The most critical CDSS system factors for outcome improvement are: alignment of guidelines with registered and EHR data to make decisions about each individual patient [ 29, 31, 33]; the short massages that include only necessary alerts such as drug interaction alerts sent in the right time for prescribing and user-friendly interface for saving physicians' time [ 18, 20, 44, 45, 47, 48]; giving the choice to users by enabling them to close the alert window and move through next steps or provide uninterrupted alerts [ 37, 38, 39, 40, 41, 56]; the behaviors of physicians and patients which have positive effects on outcomes in CDSS-equipped environments through collaboration, following guidance, recommendations, alerts, and reminders that the system provides [ 37, 42]. Also, considering physician perception in defining the importance of alerts helps better understand the interruption status of alerts [ 37, 38, 39, 40, 41].

Subgroup analysis for CDSS types

The subgroup analysis for various CDSS types showed a significant relationship between CDSS and control group for alerts: (std diff in means = 0.134, 95% CI 0.082-0.0187); combination types of CDSSs: (std diff in means = 0.197, 95% CI 0.022-0.372); recommendation CDSSs: (std diff in means = 0.114, 95% CI 0.063-0.166); reminders: (std diff in means = 0.131, 95% CI 0.072-0.189); and instructional CDSSs: (std diff in means = 0.129, 95% CI 0.081-0.178). Figure 9 shows the forest plot for CDSS types.

Outcome analysis

Figure 10 shows the results of meta-analysis for outcome categories and the total analysis. The pooled std diff in means of p values did not show a significant difference between CDSS and the control group (std diff in means = 0.0110, 95% CI 0.086-0.138, standard error = 0.013). 95% CI for the effectiveness was drawn for each study in the horizontal line format (Q = 209.2, df = 45, p = 0.0003, I2 = 78.492, Tau2: 0.004). The findings indicate that heterogeneity improved considerably after sensitivity analysis (Fig. 11). (Q = 164, df = 41, p  = 0.0002, I2 = 75, Tau2: 0.003). After this change, the overall effects of clinical decision support system for prescribing on patient outcomes and physician practice performance based on the random effect model was significantly different: (std diff in means = 0.114, 95% CI 0.090-0.138).

Forest plot of the overall effect of CDSS for prescribing on physician practice performance and patient outcome based on outcome categorization. The pooled std diff in the mean p values did not indicate a significant difference between the CDSS and the control group before the sensitivity analysis was performed (std diff in means = 0.0110, 95% CI 0.086-0.138, standard error = 0.013)

The outcome analysis showed a significant difference between CDSS and control groups for the categorization of outcomes. Results showed that patient outcome improved: (std diff in means = 0.435, 95% CI 0.122-0.747); physician practice performance improved: (std diff in means = 0.105, 95% CI 0.78-0.133); physician practice performance and patient outcome improved: (std diff in means = 0.196, 95% CI 0.111-0.281); physician practice performance didn't improve: (std diff in means = 0.131, 95% CI 0.040-0.222). The outcome analysis did not confirm a significant difference between CDSS and control groups for the category of patient outcome: (std diff in means = 0.064, 95% CI − 0.038 to 0.165).

The CDSS types that have enhanced the outcome for patients or physician practice are as follows: alerts, recommendations, instructional CDSSs, reminders, and a combination of all of them. Patient outcomes and practice performance outcomes have been improved with the use of the CDSSs for prescribing. In some trials, however, the CDSS was not specifically related to patient outcomes and showed only a marginal improvement in medical practice outcomes.

Limitations and implications for research

Although we conducted a meta-analysis on the outcomes based on subgroup analysis, the heterogeneity among the included studies in our analysis prevented us from using sturdier mix methods. The effect that we expected of the system as a whole was statistically significant. Since we used the main outcome data for meta-analysis of the trials, there could be other outcomes by choosing certain secondary outcomes that are not statistically different from our findings. Further outcomes can be obtained by extending the spectrum of all kinds of CDSSs in addition to CDSS for prescribing.

Conclusion

This systematic review study was conducted with the aim of identifying the effect of CDSS on patient outcomes and physician performance. The results show that the use of CDSS in some diseases has positive effects on patient outcomes and physician performance while it has no significant effect on others. In addition, the types of outcomes and the effects of CDSS on the diseases are different. In some cases, the use of this approach yields positive outcomes for patients and physicians; however, in some other cases, it shows no significant difference compared to conventional approaches. The positive effect of CDSS seems to be attributed to factors such as the user-friendliness of the system, the number of patients requiring treatment, the rate of observance of clinical guidelines, the conformity of clinical guidelines and data registry, the rate of patients' accurate adherence to messages of the system, the usefulness of short messages, the existence of algorithms with dynamic functioning based on patient data, the existence of patient medical records, the relationship between electronic health records with CDSS and timely alerts of the system in the prescribing process. In addition, the positive effect of CDSS depends on a number of other factors such as providing an instruction section, not being confronted with mandatory recommendations, patient and physician cooperation with the aid of CDSS guidelines, not lagging between alerts where the alert is of low importance, the identification of important alerts, equipping pharmacies with CDSS and system applicability, and considering the opinions of physicians when assessing the value of alerts for drug interaction.

Availability of data and materials

All data generated or analyzed in the course of this study are included in this article.

Abbreviations

CDSS:

Clinical Decision Support System

MeSH:

Medical Subject Headings

CPOE:

Computerized Physician Order Entry

PRISMA:

Preferred Reporting Items for Systematic Reviews and Meta-Analyses

MRC:

UK Medical Research Council

RTMM:

Real-Time Medication Monitoring system

PAID:

Problem Areas in Diabetes

MMAI:

Modified Medication Appropriateness Index

SMS:

Short Message System

RCT:

Randomized Controlled Trial

References

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Acknowledgements

This article is a research project entitled "The effects of clinical decision support system for prescribing medication on patient outcomes and physician practice performance: A systematic review and meta-analysis", approved by Iran University of Medical Sciences (IUMS), in March 2018 with the ethical code "IR.IUMS.REC 1396.32465". Also, we would like to thank Professor Brian Haynes for his helpful comments.

Funding

This study is the result of research which is funded by Iran University of Medical Science (IUMS) with the Number of IUMS/SHMIS-96-04-136-32465 at the Iran University of Medical Science (IUMS). The funding institution did not contribute to any stage of the design of the study or data analysis process.

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Authors and Affiliations

1. Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran

Sharare Taheri Moghadam & Farnia Velayati

2. Health Management and Economics Research Center, School of Health Management and Information Sciences, Iran University of Medical Sciences, Rashid Yasemi Street, Vali-e Asr Avenue, Tehran, 1996713883, Iran

Farahnaz Sadoughi

3. School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran

Seyed Jafar Ehsanzadeh

4. Faculty of Medicine, University of Alberta, Alberta, Canada

Shayan Poursharif

Contributions

TM and FV searched the literature, extracted the information, and discussed the details. FS convinced the PRISMA result, reviewed the contradictions, and organized the whole outcome. SP helped analyze the clinical outcomes. The statistical analysis and meta-analysis were performed by TM. SE assisted in the writing process and language editing. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Farahnaz Sadoughi.

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Ethics approval and consent to participate

The authors obtained the Code of Ethics from the Ethics Committee for University Studies, Iran University of Medical Sciences, prior to collecting the data for this report. All stages of research have been conducted by two independent researchers. During the research project, all papers relating to the accuracy of the method were identified and analyzed.

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There is not any individual person's data in any form in this research.

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Taheri Moghadam, S., Sadoughi, F., Velayati, F. et al. The effects of clinical decision support system for prescribing medication on patient outcomes and physician practice performance: a systematic review and meta-analysis. BMC Med Inform Decis Mak 21, 98 (2021). https://doi.org/10.1186/s12911-020-01376-8
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Keywords

Computerized clinical decision support systems

Medication prescription

Systematic review

# data[].attributes.attachment_link.title
The effects of clinical decision support system for prescribing medication on patient outcomes and physician practice performance: a systematic review and meta-analysis - BMC Medical Informatics and Decision Making

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Background Clinical Decision Support Systems (CDSSs) for Prescribing are one of the innovations designed to improve physician practice performance and patient outcomes by reducing prescription errors. This study was therefore conducted to examine the effects of various CDSSs on physician practice performance and patient outcomes.

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Key Takeaways:
-2009 HITECH Act under President Obama allocated funding for incentive programs to incentivize adopting of EHRs
-EHR vendors need to be certified by CMS and need to have security, computerized provider ordering, and clinician decision support
-Health information exchange, patient-specific education, medication reconciliation, and patient electronic access, and secure messaging are also part of Meaningful Use

I found an interesting article that compared the incentive programs in the HITECH Act to venture capital funding. The article found that the Meaningful Use incentive programs were associated with increased investment in health care information technology compared with other industries, suggesting the government incentive programs were more effective than if these advances had been left to the free market, or venture capital. This highlights the importance of public policy in health care rather than treating health care like any other business.

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Key Points

Question

What is the association between the Health Information Technology for Economic and Clinical Health Act, a federal subsidy program for electronic health record adoption, and venture capital investments and innovation in health care information technology?

Findings

This economic evaluation used observational data on venture capital activity in the US and a difference-in-differences design and found that investments in health care information technology and electronic health record companies increased at a much faster rate than venture capital investments as a whole and that these investments were more likely to be seed-stage (very early) investments compared with all other industries and 13.6% more likely to be seed stage compared with non-information technology health care investments.

Meaning

The Health Information Technology for Economic and Clinical Health Act's incentive program was associated with increased investment in health care information technology and electronic health record-related companies compared with other industries, with an emphasis on early-stage companies, suggesting an important role for incentives in promoting innovation.

Abstract

Importance

Although the Health Information Technology for Economic and Clinical Health (HITECH) Act has accelerated electronic health record (EHR) adoption since its passage, clinician satisfaction with EHRs remains low, and the association of HITECH with health care information technology (IT) entrepreneurship has remained largely unstudied.

Objective

To determine whether the passage of the HITECH Act was associated with an increase in key measures of health care IT entrepreneurship.

Design, Setting, and Participants

This economic evaluation of venture capital (VC) activity in the US from 2000 to 2019 examined funding trends in health care IT, EHR-related companies, and all VC investments before and after the passage of HITECH. A difference-in-differences analysis compared investments in health care IT companies with those of companies in 3 categories: general health care (non-IT), IT (non-health care), and all US VC transactions. Data were analyzed from September 2018 to August 2019.

Exposures

Venture capital funding received by US companies before and after the HITECH Act.

Main Outcomes and Measures

Venture capital investment in health care IT companies and the proportion of those investments going to seed-stage companies, a proxy for very early-stage entrepreneurship and innovation.

Results

The data included 70 982 investments, of which 9425 (13.3%) were seed stage, 10 706 (15.1%) were early stage, and 50 851 (71.6%) were growth stage. After passage of the HITECH Act, investment in both health care IT companies and EHR-related companies increased at a rate much faster (13.0% and 11.4%, respectively) than VC as a whole (6.9%). In addition, the proportion of investments going to seed-stage health care IT companies increased compared with both overall VC investments and non-IT health care investments. Health care IT companies saw increased probabilities of transactions being seed-stage of 5.1% (SE, 2.2%; 95% CI, 0.8% to 9.3%; P = .02) compared with the entire sample of VC transactions and 13.6% (SE, 1.9%; 95% CI, 9.9% to 17.2%; P < .001) compared with non-IT health care VC transactions. Health care IT had essentially 0 increased probability of a transaction being seed stage compared with IT companies outside health care (−0.8% probability; SE, 2.4%; 95% CI, −5.4% to 3.9%; P  = .75).

Conclusions and Relevance

Although widespread clinician dissatisfaction with EHR systems remains a challenge, the HITECH Act's incentive program may have catalyzed early-stage entrepreneurship in health care IT, suggesting an important role for incentives in promoting innovation.

Introduction

In February 2009, as part of the American Reinvestment and Recovery Act-also known as the Obama administration's economic stimulus package-the US Congress passed the Health Information Technology for Economic and Clinical Health (HITECH) Act. The Act authorized tens of billions of dollars in federal subsidies for hospitals and eligible physicians to adopt certified electronic health record (EHR) systems. To qualify for subsidy payments, eligible professionals and hospitals were required to certify that they met standards of the Meaningful Use program for EHR systems. Beginning in 2015, nonparticipation in Meaningful Use caused clinicians to incur a reduction in Medicare and Medicaid reimbursements.1

Meaningful Use originally comprised 3 stages, each of which required meeting increasingly comprehensive EHR adoption standards, as defined by the Centers for Medicare & Medicaid Services and the Office of the National Coordinator for Health IT, to qualify for incentive payments. Meaningful Use stage 1 set preliminary standards for the electronic recording and reporting of clinical information, requiring eligible clinicians to satisfy a set of 15 core objectives. Stage 2 increased the number of required EHR capabilities and encouraged clinicians to use this information to improve clinical processes. In so doing, Centers for Medicare & Medicaid Services and Office of the National Coordinator for Health IT sought to align the program with the National Quality Strategy.2 Stage 3 further increased these requirements and refined the Meaningful Use standards to focus on the improvement of health outcomes. The Meaningful Use program has since been replaced by the Promoting Interoperability program.2

The government's role in incentivizing private investment and technological innovation has long been a subject of economic importance.3,4,5 Although the HITECH Act clearly accelerated hospitals' adoption of EHRs,6,7 evidence regarding the Act's association with other outcomes has been thin. Several researchers have found, at most, a small association with health or quality outcomes after more advanced adoption of health information technology (IT) capabilities, including EHR technology.8,9,10 Other researchers have documented meaningful reductions to costs for hospitals in IT-intensive areas beginning several years after EHR implementations11 and have found that hospital mortality statistics tend to improve after EHR implementations have had time to mature.12 Importantly, some have pointed to the unintended consequences of EHR adoption, such as an increased incidence of clinician burnout associated with the administrative load of EHR adherence and EHRs' difficulty of use.13,14,15 Improving EHR usability might even improve professional satisfaction and patient health outcomes.16,17

To our knowledge, however, there has not been any empirical research on the association between the HITECH Act and innovation in related health care technologies. This is surprising, given that in the health care setting, major policy changes that lead to increased demand for health care products and services have been shown to increase innovative activities. Notable examples include studies of how pharmaceutical research and development and commercialization activities responded to demand increases created by the passage of Medicare Part D.18,19

In this economic evaluation study, we investigated whether the HITECH Act-which presented a large long-term increase in demand for and use of EHR systems-was associated with an increase in health care IT entrepreneurship in the years that followed. In particular, the large-scale digitization of the medical record system was not only a major project in itself, but it would have also created a large trove of digital health data and spurred new clinical workflows upon which other health care IT tools and products could be built. Given the overall ambivalent success of EHR implementations, including some apparent cost and quality benefits at the expense of widespread clinician dissatisfaction, burnout, and usability concerns, understanding how major public subsidies for health care IT have shaped investment compared with the broader entrepreneurial landscape will be valuable for guiding future investments in this area.

Methods

We did not seek institutional review board approval for this evaluation because we did not use any individualized data, in accordance with 45 CFR §46. This study follows the Consolidated Health Economic Evaluation Reporting Standards ( CHEERS) reporting guideline.20

To look for evidence of entrepreneurial finance flowing into the health IT sector, we examined the distribution of venture capital (VC) financing transactions, which typically are investments in young, privately held companies,21 for health care IT and EHR-related companies before and after the HITECH Act's passage. We also compared investment patterns seen with those in the broader universe of VC transactions in the US. We interpreted seed-stage funding-that is, equity investments in companies in the earliest stages of financing-of private companies by VC firms as a proxy for entrepreneurship in the industry, whereby more seed-stage financing is an indicator of greater innovation in the space.

Data

We collected data on VC investments in the US from Capital IQ, a comprehensive database that aggregates information on private investment transactions from public wire services and surveys of investment firms. Our data set includes the date, funding round, transaction value (when available), target company's description, and target company's industry for each investment transaction.

Capital IQ includes a label for each transaction's funding round. We categorized each fundraising round into one of 3 types: seed, early stage, and growth or late (see the eTable in the Supplement for detailed definitions of these categories). To determine whether companies' products were associated with EHRs, we used the list of EHR search terms provided by the National Library of Medicine.22

Statistical Analysis

We restricted our analysis to transactions between January 1, 2000, and August 31, 2018, for which the target company was incorporated and headquartered in the US. We also omitted the 31.3% of transactions for which the Capital IQ-recorded funding round was "venture," "bridge," or "debt." We excluded bridge and debt transactions because they are typically not equity investments and do not fit clearly into the fundraising timeline of a typical VC-financed firm (bridge and debt, 9.5% of transactions). The "venture" label (21.8% of transactions), on the other hand, describes investments where the specific round is unknown. In robustness tests, we also included venture transactions in the group of non-seed-stage investments. After applying these criteria for inclusion, 70 982 total transactions remained. Of these, 1060 transactions involved companies with a primary industry classification of "health care industry software," which we refer to as "health care IT" companies.

Finally, to determine whether companies' products were related to EHRs, we matched the list of EHR keywords to the text of company descriptions included in the Capital IQ database. If any of the search terms were included in the company description, then we labeled the company as "EHR-related." Of the 1060 health care IT transactions in our data set, we identified 333 as EHR-related.

To analyze whether the composition of the types of funding health care IT companies received changed in the period following the passage of the HITECH Act, we used a difference-in-differences design to test whether the fraction of investment transactions that raised seed-stage funding for health care IT companies increased more than that of control groups of companies in 3 categories: general health care (non-IT), IT (non-health care), and the entire universe of US VC transactions. The non-IT segment of health care companies and the non-health care segment of IT companies, in particular, are the most natural groups of investments to compare with health care IT investments, because they are likely to draw from the same set of expert investors, in particular those with a sector-specific investment mandate (eg, IT-focused VC firms or health care-focused VC firms). We coded the period from January 1, 2000, through February 17, 2009, as the pretreatment period, and February 18, 2009, through August 31, 2018, as the treatment period.

The outcome variable in our regressions was a binary variable indicating whether a given investment was seed stage, and the independent variables included whether the investment was in the post-HITECH period, whether the investment was in health care IT, an interaction term for these 2 variables, and a running time variable. We estimated this regression using a linear probability model for ease of interpretation.23,24 In robustness tests, we also implemented a logit specification. To calculate statistical significance, we used a 2-sided t test, with P  < .05 denoting statistical significance.

All of our calculations were performed using R statistical software version 3.5.1 (R Project for Statistical Computing), and all figures were made using the ggplot2 library.25,26 Data were analyzed from September 2018 to August 2019.

Results

Table 1 shows a breakdown of the number of VC, non-health care IT, health care IT, and EHR-related investments seen both before and after the passage of the HITECH Act. Our final analysis included 70 982 investments, of which 9425 (13.3%) were seed stage, 10 706 (15.1%) were early stage, and 50 851 (71.6%) were growth stage. Of the seed-stage investments (ie, those representing the earliest form of investment transaction and, thus, likely the youngest entrepreneurial firms), 1046 (11.1%) were in the pretreatment period and 8379 (88.9%) followed the HITECH Act in the treatment period.

Table 1.

Company Type, Funding Round Group

Transactions, No. (%)

Before HITECH

After HITECH

Figure 1 shows cumulative investment in health care IT, EHR-related companies, and all VC beginning in 2000, normalized on the day the HITECH Act passed. Raw investment figures for health care IT and EHR-related investment are presented in eFigure 1 in the Supplement. Between January 1, 2000, and the passage of the HITECH Act, only $2.7 billion was invested in health care IT companies, compared with $6.2 billion between the Act and August 31, 2018. Furthermore, compared with the broader universe of VC investment, the number of investments in health care IT companies increased at a faster rate (12.4% vs 10.5% annualized), whereas the pace of investment in EHR-related companies stayed steady. Weighted by the dollar value of the transactions, however, investment in both health care IT companies and those we identify as EHR-related increased at a rate much faster (13.0% and 11.4%, respectively) than VC as a whole (6.9%) ( Figure 1 ).

Figure 2 shows the funding round mix for EHR-related, health care IT, and all VC transactions in our sample before and after the HITECH Act. In all 3 categories, the proportion of seed-stage investments increased in the post-HITECH period. This shift, though, is most pronounced among the broader category of health care IT and is somewhat attenuated for the subset of companies we identify as EHR-related.

Table 2 extends this comparison, displaying the results of the fitted difference-in-differences regressions. After controlling for the positive linear time trend, health care IT companies saw an additional 5.1% (SE, 2.2%; 95% CI, 0.8% to 9.3%; P = .02) and 13.6% (SE, 1.9%; 95% CI, 9.9% to 17.2%; P < .001) probability of transactions being seed-stage compared with the entire sample of VC transactions and with non-IT health care VC transactions, respectively. In the comparison with non-health IT, however, health care IT had essentially 0 increased probability of a transaction being seed stage (−0.8% probability; SE, 2.4%; 95% CI, −5.4% to 3.9%; P  = .75), suggesting that the trajectory of seed-stage investment in health care IT companies was similar to that of other non-health care IT investments, both before and after the HITECH Act.

Table 2.

Variable

Regression coefficient, mean (SE)b

All venture capital

Health (non-information technology)

Information technology (nonhealth)

After HITECH

0.095 (0.005) c

0.030 (0.009) c

0.169 (0.01) c

HCIT

−0.007 (0.018)

−0.008 (0.015)

−0.015 (0.019)

After HITECH × HCIT d

0.051 (0.022) e

0.136 (0.019) f

−0.008 (0.024) g

t
0.006 (0.0005) c

0.004 (0.001) c

0.005 (0.001) c

Intercept

0.010 (0.003) c

0.018 (0.005) c

0.25 (0.005) c

Observations, No.

70 982

15 580

26 153

R ²
0.052

0.03

0.083

A crucial underlying assumption of the difference-in-differences design is that the outcome variable for the treatment and control groups would have evolved in parallel in the absence of the treatment. Although this assumption is not precisely testable, we plotted the fraction of investments that were seed stage for health care IT and the rest of the VC universe in 2-year intervals around the date the HITECH Act passed (eFigure 2 in the Supplement). Visually, the parallel trends assumption appears to hold: before the HITECH Act, the fraction of investments that were seed stage within health care IT and the rest of VC moved in tandem, only decoupling in the periods following the Act's passage. Our results were also qualitatively robust to including VC transactions in the group of non-seed-stage investments and to the use of a logit regression specification.

Discussion

In this study, we analyzed the association of a large federal incentive program for EHR adoption with subsequent indicators of entrepreneurship in health care IT. We found that, in the years after the program's enactment, investments in health care IT and EHR-related companies increased at a rate much faster (13.0% and 11.4%, respectively) than VC as a whole (6.9%). In addition, the share of VC investments in seed-stage companies increased by 5.1% compared with trends in the broader VC investment landscape, suggesting that entrepreneurial activity in this sector became more attractive after the HITECH Act. To put the magnitudes of this association in perspective, for the near-decade leading up to the HITECH Act, fewer than 3% of health care IT transactions involved seed-stage financing. Furthermore, our results suggest that investment and entrepreneurship trends in the health care IT industry operated more similarly to those seen throughout the IT sector than those seen in health care investing more broadly. A decade after the HITECH Act, these results are important not only for understanding the full scope of the outcomes associated with the HITECH Act, but also for understanding how public policies may stimulate innovation writ large.

As seen in Figure 1 , there was a notable increase in overall VC funding around the time of the HITECH Act. We hypothesize that this sharp, 1-time increase in the pace of private placements is associated with the contemporaneous economic recession and recovery as investors reached for yield through riskier investment strategies. However, a formal analysis is beyond the scope of this article.

Our analysis does not imply that the HITECH Act or its subsequent implementation per se caused a shift in the type of health care IT companies that receive VC financing, nor can we claim a particular mechanism by which this may have occurred; several explanations are plausible. For example, a shift in unobserved investor demand for seed-stage health care IT investments may have been coincident with, or an input into, the Obama administration's desire to upgrade nationwide health care infrastructure; ostensibly unrelated, contemporaneous changes in the IT landscape (eg, the advent of cloud computing) may have induced greater opportunity for innovation in IT broadly (including health care IT); or, by accelerating the adoption of EHR systems, the HITECH Act may have, as a second-order consequence, created the opportunity for would-be entrepreneurs in the health care IT industry to build business models that rely on EHRs for data, processes, and/or customers.

Entrepreneurship, marked by the formation and funding of early-stage companies, has long been recognized as an important input into or proxy for innovation.27,28,29 By spurring adoption of EHRs and digitization of clinician workflows (ie, automated collection of clinical data, clinical decision support capabilities, computerized order entry, and other software-driven tools) the HITECH Act represented a substantial opportunity for demand-pull innovation in health care IT, in which greater market-wide demand for EHR capabilities in the US spurred research and innovation in the industry. The latter explanation of the experience of the post-HITECH VC industry suggests that the HITECH Act may have had a considerable association with the development of new technologies and, as a result, productivity in the health care industry. As EHR systems become more pervasive and functional, stimulated by the HITECH Act, new products can be built on top of those capabilities. Moreover, our results imply an important role for government incentive programs in promoting entrepreneurship around follow-on technologies in general, dovetailing with prior literature on the government's capacity to use grants and other financial incentives to promote innovation.30

Critically, our results exist in the larger context of EHR implementation and usage realities in the US. Electronic health records have numerous benefits: they have been shown to have a positive association with certain costs, outcomes, and some measures of quality.10,11,12,31,32 Furthermore, EHRs enabled tremendous clinical research opportunities33 and fostered better population and public health management.34 Yet EHRs have also been problematic and are associated with increased costs,35 poor usability,36 intense clinician dissatisfaction,15 and other unintended consequences.37 Our results-specifically, that VC investments in EHR-related and health IT companies increased after the HITECH Act-may be disappointing vis-a-vis many of the challenges associated with contemporary EHRs, suggesting a misalignment of funding and needs. That the funding was skewed toward early-stage companies also suggests a nascent industry that may still take time to mature.

In 2018, the Meaningful Use program was renamed Promoting Interoperability, and the focus of the incentive program shifted toward interoperability and improving patient access to health data. Interoperability has been slow to advance as EHR adoption has accelerated,38 and as a result, interoperability has become a focal point of US policy efforts, along with improving patient access to health data.39 It is unclear how this shift in focus will be associated with entrepreneurship and overall HCIT investment, although we might expect the nature of the technology to evolve to reflect these priorities.

Limitations

This study has several limitations. First, our study focused on VC investment; we are unable to address other funding streams that existing companies could have used to invest in EHR or other technologies without such external financing, such as a publicly traded company or a privately held company that invested in research and development. It is possible, for instance, that although our results do not show a marked increase in outside investment in VC-financed companies developing EHRs, existing companies have been able to finance these projects internally. In addition, we were unable to assess the outcomes for the companies that received funding (eg, how many hospital customers they had or how many health care clinicians used their products).

Conclusions

This study suggests that the HITECH Act and its EHR adoption incentive program were associated with increased health care IT and EHR-related funding as well as increased early-stage entrepreneurship in the health care IT sector. These findings suggest that, although incentive programs in health care IT may serve as an important catalyst for entrepreneurship and innovative activity and should inform future policy efforts in this space, more work is needed to understand how the money from these incentive programs has been deployed and who has benefited from it.

Notes

Supplement.

eTable. Funding Round Categorization

eFigure 1. Investment in EHR-Related and HCIT Companies

eFigure 2. Parallel Trends Analysis

References

6. Dranove D, Garthwaite C, Li B, Ody C. Investment subsidies and the adoption of electronic medical records in hospitals. J Health Econ. 2015;44:309-319. doi: 10.1016/j.jhealeco.2015.10.001 [[ PubMed] [ CrossRef] Google Scholar]

15. Friedberg MW, Chen PG, Van Busum KR, et al.. Factors affecting physician professional satisfaction and their implications for patient care, health systems, and health policy. Rand Health Q. 2014;3(4):1. [[ PubMed] PMC free article] [ Google Scholar]

18. Blume-Kohout ME, Sood N. Market size and innovation: effects of Medicare Part D on pharmaceutical research and development. J Public Econ. 2013;97:327-336. doi: 10.1016/j.jpubeco.2012.10.003 [[ PubMed] [ CrossRef] PMC free article] [ Google Scholar]

19. Krieger JL, Li D, Papanikolaou D Missing novelty in drug development: working paper 24595. Published May 2018. Revised January 2019. Accessed February 13, 2020. https://www.nber.org/papers/w24595.pdf

25. R Foundation for Statistical Computing R: a language and environment for statistical computing. Published 2018. Accessed February 13, 2020. https://www.r-project.org/

26. Wickham H. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag; 2016. [ Google Scholar]

27. Schumpeter JA. Entrepreneurship as innovation In: Swedberg R, ed. Entrepreneurship: The Social Science View. Oxford University Press; 2000:51-75. [ Google Scholar]

28. Schumpeter JA. Economic theory and entrepreneurial history In: Clemence RV, ed. Essays on Entrepreneurs, Innovations, Business Cycles and the Evolution of Capitalism. Transaction Publishers; 1991:253-271. [ Google Scholar]

29. Arrow KJ. Macro-context of innovation In: Baumol WJ, Strom RJ, Sheshinski E, eds. Entrepreneurship, Innovation, and the Growth Mechanism of the Free-Enterprise Economies. Princeton University Press; 2007:20-28. [ Google Scholar]

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Association of the Meaningful Use Electronic Health Record Incentive Program With Health Information Technology Venture Capital Funding

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What is the association between the Health Information Technology for Economic and Clinical Health Act, a federal subsidy program for electronic health record adoption, and venture capital investments and innovation in health care information technology?

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Week 4: Transforming Data, Information, Knowledge, and Wisdom

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Week 4: Transforming Data, Information, Knowledge, and Wisdom

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Key Takeaways:
-DIKW is a theoretical model that can be applied to nursing and healthcare informatics
-Informatics involves analyzing the relationships between data, information, knowledge, and wisdom to optimize outcomes

This week, I posted an article about how patient-generated health data can be used to influence clinical practice. Not only can asking patients to collect data help them become more involved in managing their health conditions, but it can also provide data that is more useful or realistic than might be collected in a clinical setting. This process is likely to work well for some patients and in some cultural settings but may be more challenging in others.

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Key Takeaways:
-important to have standardized language in order to share data
-Big Data is helpful but messy
-lack of National Patient Identifier inhibits interoperability
-arguments for and against government involvement

The article I posted this week is from Forbes and deals with Epic, the largest EHR company in the U.S. (and the one with which most of us are familiar). The CEO of Epic states that patient privacy is a main concern in limiting interoperability in the way mandated by the federal government. However, the reality is that large EHR's like Epic are hesitant to embrace interoperability for a variety of other reasons, including financial incentives and difficulty integrating old software into new systems. It seems that private EHR companies are unlikely to embrace interoperability unless mandated by the federal government.

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Council Post: Why A Lack of Interoperability In Healthcare Is A Detriment To Patients

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Doctors are sometimes able to access data, but even now in 2020, it’s still often by phone or fax rather than electronically.

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Key Takeaways:
-nurses and nurse leaders need competency in informatics to move healthcare forward
-QSEN, TIGER, and AACN are working to better prepare the nursing workforce to be competent in informatics

This week, I found an article assessing informatics competency for the nurse leader. I particularly liked this article because it was completed at Partners Healthcare (now Mass General Brigham), where I work. The article highlights that many nurses have traditionally received "on-the-job" informatics training rather than meeting formal and standardized competencies earlier in their education. I will be interested to see how education on nursing informatics evolves as the field itself evolves and whether the academic setting will be able to keep up with rapidly changing standards for informatics.

*Note that the highlights in the article I posted are not my own.

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Key Takeaways:
-health informatics is the intersection of information science, computer science, and health care
-health informatics provides information to help make health care-related decisions
-informatics is not the same as information technology
-used to improve outcomes and patient safety

The article I posted this week provides additional information on legislation and governing bodies of health informatics in the U.S. The article also introduces some important uses of health informatics in the clinical setting, such as tracking of epidemics and pandemics and predictive medicine.

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Definition/Introduction

Health informatics is the interprofessional field that studies and pursues the effective uses of biomedical data, information, and knowledge for scientific inquiry, problem-solving, decision making, motivated by efforts to improve human health. In other words, it is the science of information where the information is defined as data with meaning.

For the healthcare practitioner, the subspecialty of clinical informatics is most relevant. Clinical Informatics is an interprofessional practice that blends medical practice with information technologies and behavioral management principles. Rather than a rigid academic or technical pursuit, clinical informatics is a practical discipline that improves patient outcomes, advances medical research, and increases the value of healthcare delivery. The key to these goals is the understanding that the successful evolution of health care is determined not by technical capability, but by how effectively the technology is designed and integrated into existing cultures, regulatory frameworks, and institutional workflows.

Though clinical informatics has been practiced since the 1950s, it was not until the internet era that the discipline began achieving widespread consideration and application outside academics. In the United States, clinical informatics was driven further into the spotlight as new federal laws (see below) strongly incentivized the adoption of new healthcare information technology systems, citing these systems as solutions to the nation's soaring health care costs and chronic disease rates. [1][2][3]

Issues of Concern

Applications

As a practical discipline, clinical informatics has far-reaching applications within the healthcare framework-individual physicians, multi-center hospital systems, medical insurance firms, government agencies, medical device developers and more are all potential beneficiaries. Examples include:

Electronic Health Record (EHR): Perhaps the most publicly high-profile application of clinical informatics is the universal adoption of the EHR. The Affordable Care Act of 2009 (see below) mandated that all healthcare institutions transition from paper to exclusively digital medical record system. Since it must record every patient encounter, medication ordered, and laboratory test performed, the EHR impacts every aspect of a healthcare institution's operations. Subsequent EHR adoption achieved varied results. Successful institutions integrated the new EHR systems with existing institutional culture and workflows with minimal disruption to or even improved delivery of healthcare services. Other institutions with less effective or absent clinical informatics support saw worsened employee morale, decreased operational effectiveness, and compromised patient safety.

Predictive Medicine: One of the most promising potential applications of clinical informatics is the development of predictive medicine. Predictive medicine is the science of accurately risk-stratifying an individual for developing the disease within a specified time-frame. While predictive capabilities traditionally revolved around genetics (e.g. karyotype testing for Down Syndrome, BRCA gene testing for breast cancer), clinical informatics has helped to usher in a new era of predictive medicine based on so-called Big Data, huge quantities of data obtained from a variety of disparate sources in real-time. Predictive tools based on big data has the potential to help clinicians better predict who will get sick when and how best to intervene before the patient becomes sick. Though healthcare has yet to develop its own predictive tools, Target Corporation, a major retailer, has already developed a big-data informatics system that predicts when a customer is pregnant; the company subsequently tailors its marketing efforts towards those customers accordingly.

Epidemic Tracking: Not limited to healthcare data, clinical informaticists can assist in capturing and transforming any data source into usable information. In 2014, public health specialists published a report demonstrating how they could track and predict HIV outbreaks based on real-time data captured from the social media platform, Twitter. Prior research demonstrated how Twitter could also be used to predict outbreaks of influenza. With the measles and Ebola crises of 2015, other groups are now attempting to apply clinical informatics principles to capture non-traditional streams of data and create systems of predicting and preventing the next epidemics.

Legislation

Executive Order 13335 (2004): Executive Order 13335, also known as the Incentives for the Use of Health Information Technology and Establishing the Position of the National Health Information Technology Coordinator, created the Office of the National Coordinator of Health Information Technology (ONC). While this did not directly affect clinical informatics or healthcare at large, it was the United States Federal Government's first step in creating a nationwide health information exchange, a foundational system for collecting and exchanging data across hospitals, regions, and states. To date, a national healthcare information exchange nor standards for creating one has not yet been established.

The HITECH Act (2009): The Health Information Technology for Economic and Clinical Health (HITECH) Act passed by Congress as part of the American Recovery and Reinvestment Act of 2009 catalyzed industry adoption of clinical informatics with a goal of "Improving Health Care Quality, Safety, and Efficiency." Through a system of payments and penalties, the HITECH Act strongly incentivizes not only adopting the EHR but also achieving "Meaningful Use," a set of requirements that demonstrate effective integration and use of the EHR within the healthcare institution. The HITECH Act further strengthened patient information privacy standards for the new era of digitized and highly transferable information.

Governing and Professional Institutions

International Medical Informatics Association (IMIA): Founded in 1987, IMIA is the foremost international coordinating body for the promotion and development of medically-related informatics interests including biomedical informatics and clinical informatics. It serves as the hub that coordinates the efforts and goals of regional subsidiary institutions worldwide.

American Medical Informatics Association (AMIA): AMIA is the United States affiliate institution of its IMIA parent organization. Though officially the United States representative organization, AMIA is composed of thousands of members from over 40 countries worldwide. AMIA's goal, like IMIA's, is to promote and develop the role of informatics in improving patient care, healthcare operations, and biomedical research.

American Board of Medical Specialties (ABMS): The ABMS is the certifying body regulating and overseeing all physicians and physician specialists, including physician clinical informaticists. In 2011, ABMS officially recognized clinical informatics as a subspecialty of medicine and began offering board certification to qualifying physicians in 2013 through the American Board of Preventative Medicine.

Other Informatics Subspecialties:

Ontology:

A framework for representing knowledge (e.g., SNOMED CT).

Taxonomy:

Is the practice and science of classification. It adds structure to the information to make it easy to search and filter.

The core concern of an informatician is transforming data into information into knowledge.

Data (Pleural datum): observations (characters, symbols, signs) that may or may not be meaningful.

Information: data that has meaning or facts from which conclusion can be drawn. Data has a structure or relationship.

Knowledge: information believed to be justifiably true. Processed information for a purpose.

Wisdom: Knowledge over time

Informatics Fields

A number of highly subspecialized areas of informatics have developed. Some examples include the following:

Internet Informatics: The study of technologies behind internet-based information systems and skills needed to map problems to deployable internet-based solutions.

Data Mining & Information Analysis: Integrates the collection, analysis, and visualization of complex data and its critical role in research, business, and government.

Life Science Informatics: Examines artificial information systems, which help scientists make great progress in identifying core components of organisms and ecosystems.

Social Computing: Studies social interaction and developing systems that act as introducers, recommenders, coordinators, and record-keepers.

Human-Computer Interaction: Informatics that studies how design and development work impacts users.

Information Architecture: Information architecture studies the development of successful Web sites, software, intranets, and online communities. Architects structure the information and its presentation in a logical and intuitive way so that information can be successfully used.

Information Assurance and Cybersecurity: The practice of creating and managing safe and secure systems. It is crucial for organizations public and private, large and small. Organizational informatics:

Organizational informatics is fundamentally interested in the application of information, information systems and information and communications technology within organizations of various forms including the private sector, public sector, and voluntary sector organizations. [4][5][6][7]

Clinical Significance

Informatics involves the practice of information processing and the engineering of information systems. The field considers the interaction between humans and information. Informatics has a social impact on information technologies.

Medical informatics can be an important tool to control and address public health concerns using an interprofessional team of physicians, nurses, pharmacists, and public health workers. Some examples include patients missing immunizations or tracking the proper use of controlled substances. The future of medical informatics is promising and many healthcare professionals should have a background in informatics. (Level V)[8]

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Health informatics is the interprofessional field that studies and pursues the effective uses of biomedical data, information, and knowledge for scientific inquiry, problem-solving, decision making, motivated by efforts to improve human health. In other words, it is the science of information where the information is defined as data with meaning.