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Neuroinflammation: Busting Limitations and Bridging Gaps.
Increasingly, interventions aimed at improving care are likely to use such technologies as machine learning and artificial intelligence. However, health care has been relatively late to adopt them. This article provides clinical examples in which machine learning and artificial intelligence are already in use in health care and appear to deliver benefit. Three key bottlenecks toward increasing the pace of diffusion and adoption are methodological issues in evaluation of artificial intelligence-based interventions, reporting standards to enable assessment of model performance, and issues that need to be addressed for an institution to adopt these interventions. Methodological best practices will include external validation, ideally at a different site; use of proactive learning algorithms to correct for site-specific biases and increase robustness as algorithms are deployed across multiple sites; addressing subgroup performance; and communicating to providers the uncertainty of predictions. Regarding reporting, especially important issues are the extent to which implementing standardized approaches for introducing clinical decision support has been followed, describing the data sources, reporting on data assumptions, and addressing biases. Although most health care organizations in the United States have adopted electronic health records, they may be ill prepared to adopt machine learning and artificial intelligence. Several steps can enable this preparing data, developing tools to get suggestions to clinicians in useful ways, and getting clinicians engaged in the process. Open challenges and the role of regulation in this area are briefly discussed. Although these techniques have enormous potential to improve care and personalize recommendations for individuals, the hype regarding them is tremendous. Organizations will need to approach this domain carefully with knowledgeable partners to obtain the hoped-for benefits and avoid failures.In the past 2 decades, the United States has seen widespread adoption of electronic health records (EHRs) and a transition from mostly locally developed EHRs to commercial systems. However, most research on quality improvement and safety interventions in EHRs is still conducted at a single site, in a single EHR. Although single-site studies are important early in the innovation lifecycle, multisite studies of EHR interventions are critical for generalizability. Because EHR software, configuration, and local context differ considerably across health care organizations, it can be difficult to implement a single, standardized intervention across multiple sites in a study. This article outlines key strengths, weaknesses, challenges, and opportunities for standardization of EHR interventions in multisite studies and describes flexible trial designs suitable for studying complex interventions, including EHR interventions. It also outlines key considerations for reporting on flexible trials of EHR interventions, including sharing details of the process for designing interventions and their content, details of outcomes being studied and approaches for pooling, and the importance of sharing code and configuration whenever possible.By enabling more efficient and effective medical decision making, computer-based clinical decision support (CDS) could unlock widespread benefits from the significant investment in electronic health record (EHR) systems in the United States. Evidence from high-quality CDS studies is needed to enable and support this vision of CDS-facilitated care optimization, but limited guidance is available in the literature for designing and reporting CDS studies. selleck chemicals llc To address this research gap, this article provides recommendations for designing, conducting, and reporting CDS studies to 1) ensure that EHR data to inform the CDS are available; 2) choose decision rules that are consistent with local care processes; 3) target the right users and workflows; 4) make the CDS easy to access and use; 5) minimize the burden placed on users; 6) incorporate CDS success factors identified in the literature, in particular the automatic provision of CDS as a part of clinician workflow; 7) ensure that the CDS rules are adequately tested; 8) select meaningful evaluation measures; 9) use as rigorous a study design as is feasible; 10) think about how to deploy the CDS beyond the original host organization; 11) report the study in context; 12) help the audience understand why the intervention succeeded or failed; and 13) consider the financial implications. If adopted, these recommendations should help advance the vision of more efficient, effective care facilitated by useful and widely available CDS.Ninety percent of health care systems now offer patient portals to access electronic health records (EHRs) in the United States, but only 15% to 30% of patients use these platforms. Using PubMed, the authors identified 53 studies published from September 2013 to June 2019 that informed best practices and priorities for future research on patient engagement with EHR data through patient portals, These studies mostly involved outpatient settings and fell into 3 major categories interventions to increase use of patient portals, usability testing of portal interfaces, and documentation of patient and clinician barriers to portal use. Interventions that used one-on-one patient training were associated with the highest portal use. Patients with limited health or digital literacy faced challenges to portal use. Clinicians reported a lack of workflows to support patient use of portals in routine practice. These studies suggest that achieving higher rates of patient engagement through EHR portals will require paying more attention to the needs of diverse patients and systematically measuring usability as well as scope of content. Future work should incorporate implementation science approaches and directly address the key role of clinicians and staff in promoting portal use.Electronic health records (EHRs) are an increasingly important source of real-world health care data for observational research. Analyses of data collected for purposes other than research require careful consideration of data quality as well as the general research and reporting principles relevant to observational studies. The core principles for observational research in general also apply to observational research using EHR data, and these are well addressed in prior literature and guidelines. This article provides additional recommendations for EHR-based research. Considerations unique to EHR-based studies include assessment of the accuracy of computer-executable cohort definitions that can incorporate unstructured data from clinical notes and management of data challenges, such as irregular sampling, missingness, and variation across time and place. Principled application of existing research and reporting guidelines alongside these additional considerations will improve the quality of EHR-based observational studies.
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