Citations

Abstract Objectives Embedded pragmatic clinical trials (ePCTs) are conducted as part of routine clinical care and therefore use data collected from real-world data sources, such as electronic health record systems and administrative claims. A common approach for using these types of data across all phases of trial conduct is to create a computable phenotype (an explicitly defined data query data, including specified data types, data codes, and logical parameters) to capture patients with a clinical condition, exposure, symptom, characteristic, treatment, or outcome of interest. Materials and Methods The Electronic Health Record (EHR) Core Working Group of the NIH Pragmatic Trials Collaboratory captured the experiences of investigators in developing, adapting, and applying computable phenotype definitions in their studies. Results Four case studies describe different approaches to developing and using computable phenotypes in pragmatic trials. Discussion We recommend: (1) developing computable phenotypes as part of a team with multiple areas of expertise; (2) appropriate validation; (3) dissemination of salient details regarding phenotype creation; and (4) capturing and reporting any modifications made to phenotypes during the conduct of the trial. Conclusion A range of issues and decisions influence how computable phenotypes are developed and used in pragmatic clinical trials. Multidisciplinary teams that understand the context of the data, including the reason the data are collected and potential sources of bias, are best suited for the development and validation of phenotypes for ePCTs.

Abstract Background The NIH Pragmatic Trials Collaboratory supports the design and conduct of 31 embedded pragmatic clinical trials, and many of these trials use patient-reported outcome measures (PROMs) to provide valuable information about the patients' health and wellness. Often these trials enroll medically underserved patients, including people with incomes below the federal poverty threshold, racial or ethnic minority groups, or rural or frontier communities. Objectives In this series of trial case reports, we provide lessons learned about collecting PROMs in these populations. Unbiased collection of PROM data is critical to increase the generalizability of trial outcomes and to address health inequities. Use of electronic health records (EHRs) and other digital modes of PROM administration has gained traction. However, engagement with these modes is often low among populations prone to disparity due to lower digital proficiency, device access, and uptake of EHR portals and web interfaces. Methods To maximize the completeness and representativeness of their trial outcome data, study teams tested a range of strategies to improve PROM response rates with emphasis on disparities prone and underserved patient groups. This manuscript describes the approaches, their implementation, and the targeted populations. Conclusion Optimized PROM collection required hybrid approaches with multiple outreach modes, high-touch methods, creativity in promoting digital uptake, multimodal participant engagement, and text messaging.

Abstract Embedded pragmatic clinical trials (ePCTs) play a vital role in addressing current population health problems, and their use of electronic health record (EHR) systems promises efficiencies that will increase the speed and volume of relevant and generalizable research. However, as the number of ePCTs using EHR-derived data grows, so does the risk that research will become more vulnerable to biases due to differences in data capture and access to care for different subsets of the population, thereby propagating inequities in health and the healthcare system. We identify 3 challenges—incomplete and variable capture of data on social determinants of health, lack of representation of vulnerable populations that do not access or receive treatment, and data loss due to variable use of technology—that exacerbate bias when working with EHR data and offer recommendations and examples of ways to actively mitigate bias.

Abstract Objective The Genomic Medicine Working Group of the National Advisory Council for Human Genome Research virtually hosted its 13th genomic medicine meeting titled “Developing a Clinical Genomic Informatics Research Agenda”. The meeting’s goal was to articulate a research strategy to develop Genomics-based Clinical Informatics Tools and Resources (GCIT) to improve the detection, treatment, and reporting of genetic disorders in clinical settings. Materials and Methods Experts from government agencies, the private sector, and academia in genomic medicine and clinical informatics were invited to address the meeting's goals. Invitees were also asked to complete a survey to assess important considerations needed to develop a genomic-based clinical informatics research strategy. Results Outcomes from the meeting included identifying short-term research needs, such as designing and implementing standards-based interfaces between laboratory information systems and electronic health records, as well as long-term projects, such as identifying and addressing barriers related to the establishment and implementation of genomic data exchange systems that, in turn, the research community could help address. Discussion Discussions centered on identifying gaps and barriers that impede the use of GCIT in genomic medicine. Emergent themes from the meeting included developing an implementation science framework, defining a value proposition for all stakeholders, fostering engagement with patients and partners to develop applications under patient control, promoting the use of relevant clinical workflows in research, and lowering related barriers to regulatory processes. Another key theme was recognizing pervasive biases in data and information systems, algorithms, access, value, and knowledge repositories and identifying ways to resolve them.

Abstract Objectives The study aimed to understand potential barriers to the adoption of health information technology projects that are released as free and open source software (FOSS). Methods We conducted a survey of research consortia participants engaged in genomic medicine implementation to assess perceived institutional barriers to the adoption of three systems: ClinGen electronic health record (EHR) Toolkit, DocUBuild, and MyResults.org. The survey included eight barriers from the Consolidated Framework for Implementation Research (CFIR), with additional barriers identified from a qualitative analysis of open-ended responses. Results We analyzed responses from 24 research consortia participants from 18 institutions. In total, 14 categories of perceived barriers were evaluated, which were consistent with other observed barriers to FOSS adoption. The most frequent perceived barriers included lack of adaptability of the system, lack of institutional priority to implement, lack of trialability, lack of advantage of alternative systems, and complexity. Conclusion In addition to understanding potential barriers, we recommend some strategies to address them (where possible), including considerations for genomic medicine. Overall, FOSS developers need to ensure systems are easy to trial and implement and need to clearly articulate benefits of their systems, especially when alternatives exist. Institutional champions will remain a critical component to prioritizing genomic medicine projects.

Abstract Objective The study sought to identify barriers to and facilitators of point-of-care information seeking and use of knowledge resources. Materials and Methods We searched MEDLINE, Embase, PsycINFO, and Cochrane Library from 1991 to February 2017. We included qualitative studies in any language exploring barriers to and facilitators of point-of-care information seeking or use of electronic knowledge resources. Two authors independently extracted data on users, study design, and study quality. We inductively identified specific barriers or facilitators and from these synthesized a model of key determinants of information-seeking behaviors. Results Forty-five qualitative studies were included, reporting data derived from interviews (n = 26), focus groups (n = 21), ethnographies (n = 6), logs (n = 4), and usability studies (n = 2). Most studies were performed within the context of general medicine (n = 28) or medical specialties (n = 13). We inductively identified 58 specific barriers and facilitators and then created a model reflecting 5 key determinants of information-seeking behaviors: time includes subthemes of time availability, efficiency of information seeking, and urgency of information need; accessibility includes subthemes of hardware access, hardware speed, hardware portability, information restriction, and cost of resources; personal skills and attitudes includes subthemes of computer literacy, information-seeking skills, and contextual attitudes about information seeking; institutional attitudes, cultures, and policies includes subthemes describing external individual and institutional information-seeking influences; and knowledge resource features includes subthemes describing information-seeking efficiency, information content, information organization, resource familiarity, information credibility, information currency, workflow integration, compatibility of recommendations with local processes, and patient educational support. Conclusions Addressing these determinants of information-seeking behaviors may facilitate clinicians' question answering to improve patient care.

Purpose The authors sought to summarize quantitative and qualitative research addressing electronic knowledge resources and point-of-care learning in a scoping review. Method The authors searched MEDLINE, Embase, PsycINFO, and the Cochrane Database for studies addressing electronic knowledge resources and point-of-care learning. They iteratively revised inclusion criteria and operational definitions of study features and research themes of interest. Two reviewers independently performed each phase of study selection and data extraction. Results Of 10,811 studies identified, 305 were included and reviewed. Most studies (225; 74%) included physicians or medical students. The most frequently mentioned electronic resources were UpToDate (88; 29%), Micromedex (59; 19%), Epocrates (50; 16%), WebMD (46; 15%), MD Consult (32; 10%), and LexiComp (31; 10%). Eight studies (3%) evaluated electronic resources or point-of-care learning using outcomes of patient effects, and 36 studies (12%) reported objectively measured clinician behaviors. Twenty-five studies (8%) examined the clinical or educational impact of electronic knowledge resource use on patient care or clinician knowledge, 124 (41%) compared use rates of various knowledge resources, 69 (23%) examined the quality of knowledge resource content, and 115 (38%) explored the process of point-of-care learning. Two conceptual clarifications were identified, distinguishing the impact on clinical or educational outcomes versus the impact on test setting decision support, and the quality of information content versus the correctness of information obtained by a clinician–user. Conclusions Research on electronic knowledge resources is dominated by studies involving physicians and evaluating use rates. Studies involving nonphysician users, and evaluating resource impact and implementation, are needed.

Objective: Infobuttons appear as small icons adjacent to electronic health record (EHR) data (e.g., medications, diagnoses, or test results) that, when clicked, access online knowledge resources tailored to the patient, care setting, or task. Infobuttons are required for “Meaningful Use” certification of US EHRs. We sought to evaluate infobuttons’ impact on clinical practice and identify features associated with improved outcomes. Methods: We conducted a systematic review, searching MEDLINE, EMBASE, and other databases from inception to July 6, 2015. We included and cataloged all original research in any language describing implementation of infobuttons or other context-sensitive links. Studies evaluating clinical implementations with outcomes of usage or impact were reviewed in greater detail. Reviewers worked in duplicate to select articles, evaluate quality, and abstract information. Results: Of 599 potential articles, 77 described infobutton implementation. The 17 studies meriting detailed review, including 3 randomized trials, yielded the following findings. Infobutton usage frequency ranged from 0.3 to 7.4 uses per month per potential user. Usage appeared to be influenced by EHR task. Five studies found that infobuttons are used less often than non–context-sensitive links (proportionate usage 0.20–0.34). In 3 studies, users answered their clinical question in > 69% of infobutton sessions. Seven studies evaluated alternative approaches to infobutton design and implementation. No studies isolated the impact of infobuttons on objectively measured patient outcomes. Conclusions: Weak evidence suggests that infobuttons can help providers answer clinical questions. Research on optimal infobutton design and implementation, and on the impact on patient outcomes and provider behaviors, is needed.