Toward standardization, harmonization, and integration of social determinants of health data: A Texas Clinical and Translational Science Award institutions collaboration.

Introduction: The focus on social determinants of health (SDOH) and their impact on health outcomes is evident in U.S. federal actions by Centers for Medicare & Medicaid Services and Office of National Coordinator for Health Information Technology. The disproportionate impact of COVID-19 on minorities and communities of color heightened awareness of health inequities and the need for more robust SDOH data collection. Four Clinical and Translational Science Award (CTSA) hubs comprising the Texas Regional CTSA Consortium (TRCC) undertook an inventory to understand what contextual-level SDOH datasets are offered centrally and which individual-level SDOH are collected in structured fields in each electronic health record (EHR) system potentially for all patients. Methods: Hub teams identified American Community Survey (ACS) datasets available via their enterprise data warehouses for research. Each hub's EHR analyst team identified structured fields available in their EHR for SDOH using a collection instrument based on a 2021 PCORnet survey and conducted an SDOH field completion rate analysis. Results: One hub offered ACS datasets centrally. All hubs collected eleven SDOH elements in structured EHR fields. Two collected Homeless and Veteran statuses. Completeness at four hubs was 80%-98%: Ethnicity, Race; < 10%: Education, Financial Strain, Food Insecurity, Housing Security/Stability, Interpersonal Violence, Social Isolation, Stress, Transportation. Conclusion: Completeness levels for SDOH data in EHR at TRCC hubs varied and were low for most measures. Multiple system-level discussions may be necessary to increase standardized SDOH EHR-based data collection and harmonization to drive effective value-based care, health disparities research, translational interventions, and evidence-based policy.

The need to strengthen the evaluation of the impact of Artificial Intelligence-based decision support systems on healthcare provision.

Despite the renewed interest in Artificial Intelligence-based clinical decision support systems (AI-CDS), there is still a lack of empirical evidence supporting their effectiveness. This underscores the need for rigorous and continuous evaluation and monitoring of processes and outcomes associated with the introduction of health information technology. We illustrate how the emergence of AI-CDS has helped to bring to the fore the critical importance of evaluation principles and action regarding all health information technology applications, as these hitherto have received limited attention. Key aspects include assessment of design, implementation and adoption contexts; ensuring systems support and optimise human performance (which in turn requires understanding clinical and system logics); and ensuring that design of systems prioritises ethics, equity, effectiveness, and outcomes. Going forward, information technology strategy, implementation and assessment need to actively incorporate these dimensions. International policy makers, regulators and strategic decision makers in implementing organisations therefore need to be cognisant of these aspects and incorporate them in decision-making and in prioritising investment. In particular, the emphasis needs to be on stronger and more evidence-based evaluation surrounding system limitations and risks as well as optimisation of outcomes, whilst ensuring learning and contextual review. Otherwise, there is a risk that applications will be sub-optimally embodied in health systems with unintended consequences and without yielding intended benefits.

Managing Pandemic Responses with Health Informatics - Challenges for Assessing Digital Health Technologies.

OBJECTIVES: To highlight the role of technology assessment in the management of the COVID-19 pandemic. METHOD: An overview of existing research and evaluation approaches along with expert perspectives drawn from the International Medical Informatics Association (IMIA) Working Group on Technology Assessment and Quality Development in Health Informatics and the European Federation for Medical Informatics (EFMI) Working Group for Assessment of Health Information Systems. RESULTS: Evaluation of digital health technologies for COVID-19 should be based on their technical maturity as well as the scale of implementation. For mature technologies like telehealth whose efficacy has been previously demonstrated, pragmatic, rapid evaluation using the complex systems paradigm which accounts for multiple sociotechnical factors, might be more suitable to examine their effectiveness and emerging safety concerns in new settings. New technologies, particularly those intended for use on a large scale such as digital contract tracing, will require assessment of their usability as well as performance prior to deployment, after which evaluation should shift to using a complex systems paradigm to examine the value of information provided. The success of a digital health technology is dependent on the value of information it provides relative to the sociotechnical context of the setting where it is implemented. CONCLUSION: Commitment to evaluation using the evidence-based medicine and complex systems paradigms will be critical to ensuring safe and effective use of digital health technologies for COVID-19 and future pandemics. There is an inherent tension between evaluation and the imperative to urgently deploy solutions that needs to be negotiated.

Understanding enterprise data warehouses to support clinical and translational research.

OBJECTIVE: Among National Institutes of Health Clinical and Translational Science Award (CTSA) hubs, adoption of electronic data warehouses for research (EDW4R) containing data from electronic health record systems is nearly ubiquitous. Although benefits of EDW4R include more effective, efficient support of scientists, little is known about how CTSA hubs have implemented EDW4R services. The goal of this qualitative study was to understand the ways in which CTSA hubs have operationalized EDW4R to support clinical and translational researchers. MATERIALS AND METHODS: After conducting semistructured interviews with informatics leaders from 20 CTSA hubs, we performed a directed content analysis of interview notes informed by naturalistic inquiry. RESULTS: We identified 12 themes: organization and data; oversight and governance; data access request process; data access modalities; data access for users with different skill sets; engagement, communication, and literacy; service management coordinated with enterprise information technology; service management coordinated within a CTSA hub; service management coordinated between informatics and biostatistics; funding approaches; performance metrics; and future trends and current technology challenges. DISCUSSION: This study is a step in developing an improved understanding and creating a common vocabulary about EDW4R operations across institutions. Findings indicate an opportunity for establishing best practices for EDW4R operations in academic medicine. Such guidance could reduce the costs associated with developing an EDW4R by establishing a clear roadmap and maturity path for institutions to follow. CONCLUSIONS: CTSA hubs described varying approaches to EDW4R operations that may assist other institutions in better serving investigators with electronic patient data.

Theoretical Foundations for Evidence-Based Health Informatics: Why? How?

A scientific approach to health informatics requires sound theoretical foundations. Health informatics implementation would be more effective if evidence-based and guided by theories about what is likely to work in what circumstances. We report on a Medinfo 2015 workshop on this topic jointly organized by the EFMI Working Group on Assessment of Health Information Systems and the IMIA Working Group on Technology Assessment and Quality Development. We discuss the findings of the workshop and propose an approach to consolidate empirical knowledge into testable middle-range theories.

Experts speak: advice from key informants to small, rural hospitals on implementing the electronic health record system.

The US government has allocated $30 billion dollars to implement Electronic Health Records (EHRs) in hospitals and provider practices through a policy called Meaningful Use. Small, rural hospitals, particularly those designated as Critical Access Hospitals (CAHs), comprising nearly a quarter of US hospitals, had not implemented EHRs before. Little is known on implementation in this setting. We interviewed a spectrum of 31 experts in the domain. The interviews were then analyzed qualitatively to ascertain the expert recommendations. Nineteen themes emerged. The pool of experts included staff from CAHs that had recently implemented EHRs. We were able to compare their answers with those of other experts and make recommendations for stakeholders. CAH peer experts focused less on issues such as physician buy-in, communication, and the EHR team. None of them indicated concern or focus on clinical decision support systems, leadership, or governance. They were especially concerned with system selection, technology, preparatory work and a need to know more about workflow and optimization. These differences were explained by the size and nature of these small hospitals.

New legal protections for reporting patient errors under the Patient Safety and Quality Improvement Act: a review of the medical literature and analysis.

OBJECTIVES: The Patient Safety and Quality Improvement Act (PSQIA) of 2005, inspired by the Institute of Medicine's (IOM) 1999 report To Err Is Human, affords federal protections in exchange for error disclosures. However, the PSQIA is unlikely to be effective unless frontline providers are aware of its existence. In this study, we assessed the quantity of publications regarding this protection within the medical literature. METHODS: Four reviewers independently evaluated 2060 safety-related articles, identified through a PubMed database search, to determine whether they discussed actual (or proposed) national legal protections for voluntary reporting of medical errors. Using a reviewer method based on a standard Delphi consensus model, agreement was achieved if at least 3 of 4 reviewers agreed with the decision. RESULTS: Articles were separated into pre-IOM report (1990-1999) and post-IOM report (2000-2008) literature. No articles were determined to be "on topic" in the pre-IOM period (n = 624). Twenty-seven articles were considered "on topic" in the post-IOM period (n = 1436), constituting 1.8% of the period total (95% confidence interval, 1.2%-2.6%). Of the 27 on topic articles, 7 appeared in practice-related journals, whereas the remaining 20 were in journals with a health policy or health care administration focus. CONCLUSIONS: Few published studies were found in clinical journals describing the PSQIA. This raises serious concerns and indicates that physicians may not be aware of the new legal protections afforded for error disclosure. If the health care system is to realize the benefits of error reporting systems, greater education of physicians regarding their legal protections may be needed.

Factors to weigh when considering electronic data collection.

Researchers are increasingly considering the adoption of electronic data-collection methods--which entail the use of the Web, e-mail, and desktop and handheld computers--for surveys, diaries, research instruments, and focus groups. Based on prior research findings on electronic data collection, the authors delineate the key factors, which incorporate population, data, and resource characteristics, that guide researchers in selecting the data-collection method most appropriate for their research question. Population factors to consider in data-collection decisions are age, gender, socio-economic status, and access to technology. Key data factors influencing the selection of collection method include sensitivity of the topic, time sensitivity, longitudinal data, and contextual data. Data-collection decisions are also based on financial, time, and technological resources. Technology and demographics will continue to change and affect data-collection methods and possibilities. By examining these key factors, however, researchers will be able to reach data-collection decisions that are appropriate for each project.