Information technologies that facilitate care coordination: provider and patient perspectives

Health information technology is a core infrastructure for the chronic care model, integrated care, and other organized care delivery models. From the provider perspective, health information exchange (HIE) helps aggregate and share information about a patient or population from several sources. HIE technologies include direct messages, transfer of care, and event notification services. From the patient perspective, personal health records, secure messaging, text messages, and other mHealth applications may coordinate patients and providers. Patient-reported outcomes and social media technologies enable patients to share health information with many stakeholders, including providers, caregivers, and other patients. An information architecture that integrates personal health record and mHealth applications, with HIEs that combine the electronic health records of multiple healthcare systems will create a rich, dynamic ecosystem for patient collaboration

Physicians' perceptions of an electronic health record-based clinical trial alert approach to subject recruitment: A survey

<p>Abstract</p> <p>Background</p> <p>Physician participation in clinical research recruitment efforts is critical to many studies' success, but it is often limited. Use of an Electronic Health Record (EHR)-based, point-of-care Clinical Trial Alert (CTA) approach has led to significant increases in physician-generated recruitment and holds promise for wider benefit. However, little is known about physicians' decision-making regarding recruitment in EHR-equipped settings or the use of such EHR-based approaches. We sought to assess physicians' perceptions about recruitment in general and using the CTA approach in particular.</p> <p>Methods</p> <p>We developed and delivered a Web-based survey consisting of 15 multiple-choice and free-text questions. Participants included the 114 physician subjects (10 endocrinologists and 104 general internists) who were exposed to CTAs during our preceding 4-month intervention study. Response data were descriptively analyzed, and key findings were compared between groups using appropriate statistical tests.</p> <p>Results</p> <p>Sixty-nine physicians (61%) responded during the 10-week survey period. Respondents and non-respondents did not differ significantly. Twenty-seven percent of respondents felt very comfortable recruiting patients to trials in general, and 77% appreciated being reminded about a trial via a CTA. Only 11% percent felt the CTA was difficult to use, and 27% felt it was more than somewhat intrusive. Among those who ignored all CTAs, 37% cited a lack of time, 28% knowledge of the patient's ineligibility, and 13% limited knowledge about the trial as their most common reason. Thirty-eight percent wanted more information about the trial presented in the CTA, and 73% were interested in seeing CTAs for future trials. Comments and suggestions were submitted by 33% of respondents and included suggestions for improvement of the CTA approach.</p> <p>Conclusion</p> <p>Most physicians were comfortable recruiting patients for clinical trials at the point-of-care, found the EHR-based CTA approach useful and would like to see it used in the future. These findings provide insight into the perceived utility of this EHR-based approach to subject recruitment, suggest ways it might be improved, and add to the limited body of knowledge regarding physicians' attitudes toward clinical trial recruitment in EHR-equipped settings.</p

Research IT maturity models for academic health centers: Early development and initial evaluation

This paper proposes the creation and application of maturity models to guide institutional strategic investment in research informatics and information technology (research IT) and to provide the ability to measure readiness for clinical and research infrastructure as well as sustainability of expertise. Conducting effective and efficient research in health science increasingly relies upon robust research IT systems and capabilities. Academic health centers are increasing investments in health IT systems to address operational pressures, including rapidly growing data, technological advances, and increasing security and regulatory challenges associated with data access requirements. Current approaches for planning and investment in research IT infrastructure vary across institutions and lack comparable guidance for evaluating investments, resulting in inconsistent approaches to research IT implementation across peer academic health centers as well as uncertainty in linking research IT investments to institutional goals. Maturity models address these issues through coupling the assessment of current organizational state with readiness for deployment of potential research IT investment, which can inform leadership strategy. Pilot work in maturity model development has ranged from using them as a catalyst for engaging medical school IT leaders in planning at a single institution to developing initial maturity indices that have been applied and refined across peer medical schools

Better together: Integrating biomedical informatics and healthcare IT operations to create a learning health system during the COVID-19 pandemic

The growing availability of multi-scale biomedical data sources that can be used to enable research and improve healthcare delivery has brought about what can be described as a healthcare data age. This new era is defined by the explosive growth in bio-molecular, clinical, and population-level data that can be readily accessed by researchers, clinicians, and decision-makers, and utilized for systems-level approaches to hypothesis generation and testing as well as operational decision-making. However, taking full advantage of these unprecedented opportunities presents an opportunity to revisit the alignment between traditionally academic biomedical informatics (BMI) and operational healthcare information technology (HIT) personnel and activities in academic health systems. While the history of the academic field of BMI includes active engagement in the delivery of operational HIT platforms, in many contemporary settings these efforts have grown distinct. Recent experiences during the COVID-19 pandemic have demonstrated greater coordination of BMI and HIT activities that have allowed organizations to respond to pandemic-related changes more effectively, with demonstrable and positive impact as a result. In this position paper, we discuss the challenges and opportunities associated with driving alignment between BMI and HIT, as viewed from the perspective of a learning healthcare system. In doing so, we hope to illustrate the benefits of coordination between BMI and HIT in terms of the quality, safety, and outcomes of care provided to patients and populations, demonstrating that these two groups can be better together

Reimagining the research-practice relationship: policy recommendations for informatics-enabled evidence-generation across the US health system

Abstract. The widespread adoption and use of electronic health records and their use to enable learning health systems (LHS) holds great promise to accelerate both evidence-generating medicine (EGM) and evidence-based medicine (EBM), thereby enabling a LHS. In 2016, AMIA convened its 10th annual Policy Invitational to discuss issues key to facilitating the EGM-EBM paradigm at points-of-care (nodes), across organizations (networks), and to ensure viability of this model at scale (sustainability). In this article, we synthesize discussions from the conference and supplements those deliberations with relevant context to inform ongoing policy development. Specifically, we explore and suggest public policies needed to facilitate EGM-EBM activities on a national scale, particularly those policies that can enable and improve clinical and health services research at the point-of-care, accelerate biomedical discovery, and facilitate translation of findings to improve the health of individuals and population

Transforming primary medical research knowledge into clinical decision

While the utility of computerized clinical decision support (CCDS) for multiple select clinical domains has been clearly demonstrated, much less is known about the full breadth of domains to which CCDS approaches could be productively applied. To explore the applicability of CCDS to general medical knowledge, we sampled a total of 500 primary research articles from 4 high-impact medical journals. Employing rule-based templates, we created high-level CCDS rules for 72% (361/500) of primary medical research articles. We subsequently identified data sources needed to implement those rules. Ourfindings suggest that CCDS approaches, perhaps in the form of non-interruptive infobuttons, could be much more broadly applied. In addition, our analytic methods appear to provide a means of prioritizing and quantitating the relative utility of available data sources for purposes of CCDS

The COVID-19 health equity twindemic: Statewide epidemiologic trends of SARS-CoV-2 outcomes among racial minorities and in rural America

Background Early studies on COVID-19 identified unequal patterns in hospitalization and mortality in urban environments for racial and ethnic minorities. These studies were primarily single center observational studies conducted within the first few weeks or months of the pandemic. We sought to examine trends in COVID-19 morbidity and mortality over time for minority and rural populations, especially during the U.S. fall surge. Methods Statewide cohort of all adult residents in Indiana tested for SARS-CoV-2 infection between March 1 and December 31, 2020, linked to electronic health records. Primary measures were per capita rates of infection, hospitalization, and death. Age adjusted rates were calculated for multiple time periods corresponding to public health mitigation efforts. Results Morbidity and mortality increased over time with notable differences among sub-populations. Initially, per capita hospitalizations among racial minorities were 3-4 times higher than whites, and per capita deaths among urban residents were twice those of rural residents. By fall 2020, per capita hospitalizations and deaths in rural areas surpassed those of urban areas, and gaps between black/brown and white populations narrowed. Cumulative morbidity and mortality were highest among minority groups and in rural communities. Conclusions Burden of COVID-19 morbidity and mortality shifted over time, creating a twindemic involving disparities in outcomes based on race and geography. Health officials should explicitly measure disparities and adjust mitigation and vaccination strategies to protect vulnerable sub-populations with greater disease burden

The synchronicity of COVID-19 disparities: Statewide epidemiologic trends in SARS-CoV-2 morbidity, hospitalization, and mortality among racial minorities and in rural America

Background Early studies on COVID-19 identified unequal patterns in hospitalization and mortality in urban environments for racial and ethnic minorities. These studies were primarily single center observational studies conducted within the first few weeks or months of the pandemic. We sought to examine trends in COVID-19 morbidity, hospitalization, and mortality over time for minority and rural populations, especially during the U.S. fall surge. Methods Data were extracted from a statewide cohort of all adult residents in Indiana tested for SARS-CoV-2 infection between March 1 and December 31, 2020, linked to electronic health records. Primary measures were per capita rates of infection, hospitalization, and death. Age adjusted rates were calculated for multiple time periods corresponding to public health mitigation efforts. Comparisons across time within groups were compared using ANOVA. Results Morbidity and mortality increased over time with notable differences among sub-populations. Initially, hospitalization rates among racial minorities were 3–4 times higher than whites, and mortality rates among urban residents were twice those of rural residents. By fall 2020, hospitalization and mortality rates in rural areas surpassed those of urban areas, and gaps between black/brown and white populations narrowed. Changes across time among demographic groups was significant for morbidity and hospitalization. Cumulative morbidity and mortality were highest among minority groups and in rural communities. Conclusions The synchronicity of disparities in COVID-19 by race and geography suggests that health officials should explicitly measure disparities and adjust mitigation as well as vaccination strategies to protect those sub-populations with greater disease burden

Sustainability considerations for clinical and translational research informatics infrastructure

A robust biomedical informatics infrastructure is essential for academic health centers engaged in translational research. There are no templates for what such an infrastructure encompasses or how it is funded. An informatics workgroup within the Clinical and Translational Science Awards network conducted an analysis to identify the scope, governance, and funding of this infrastructure. After we identified the essential components of an informatics infrastructure, we surveyed informatics leaders at network institutions about the governance and sustainability of the different components. Results from 42 survey respondents showed significant variations in governance and sustainability; however, some trends also emerged. Core informatics components such as electronic data capture systems, electronic health records data repositories, and related tools had mixed models of funding including, fee-for-service, extramural grants, and institutional support. Several key components such as regulatory systems (e.g., electronic Institutional Review Board [IRB] systems, grants, and contracts), security systems, data warehouses, and clinical trials management systems were overwhelmingly supported as institutional infrastructure. The findings highlighted in this report are worth noting for academic health centers and funding agencies involved in planning current and future informatics infrastructure, which provides the foundation for a robust, data-driven clinical and translational research program