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The development of phenotypes using electronic health records is a resource-intensive process. Therefore, the cataloging of phenotype algorithm metadata for reuse is critical to accelerate clinical research. The Department of Veterans Affairs (VA) has developed a standard for phenotype metadata collection which is currently used in the VA phenomics knowledgebase library, CIPHER (Centralized Interactive Phenomics Resource), to capture over 5000 phenotypes. The CIPHER standard improves upon existing phenotype library metadata [...]

Informatics researchers and practitioners have started exploring racism related to the implementation and use of electronic health records (EHRs). While this work has begun to expose structural racism which is a fundamental driver of racial and ethnic disparities, there is a lack of inclusion of concepts of racism in this work. This perspective provides a classification of racism at 3 levels-individual, organizational, and structural-and offers recommendations for future research, practice [...]

Electronic health record (EHR) data are a valuable resource for population health research but lack critical information such as relationships between individuals. Emergency contacts in EHRs can be used to link family members, creating a population that is more representative of a community than traditional family cohorts.

Identifying ethical concerns with ML applications to healthcare (ML-HCA) before problems arise is now a stated goal of ML design oversight groups and regulatory agencies. Lack of accepted standard methodology for ethical analysis, however, presents challenges. In this case study, we evaluate use of a stakeholder "values-collision" approach to identify consequential ethical challenges associated with an ML-HCA for advanced care planning (ACP). Identification of ethical challenges could guide revision and [...]

The All of Us Research Program makes individual-level data available to researchers while protecting the participants' privacy. This article describes the protections embedded in the multistep access process, with a particular focus on how the data was transformed to meet generally accepted re-identification risk levels.

We conducted a systematic review to characterize and critically appraise developed prediction models based on structured electronic health record (EHR) data for adverse drug event (ADE) diagnosis and prognosis in adult hospitalized patients.

There are over 363 customized risk models of the American College of Cardiology and the American Heart Association (ACC/AHA) pooled cohort equations (PCE) in the literature, but their gains in clinical utility are rarely evaluated. We build new risk models for patients with specific comorbidities and geographic locations and evaluate whether performance improvements translate to gains in clinical utility.

Physicians' low adoption of diagnostic decision aids (DDAs) may be partially due to concerns about patient/public perceptions. We investigated how the UK public views DDA use and factors affecting perceptions.

The 21st Century Cures Act and the rise of telemedicine led to renewed focus on patient portals. However, portal use disparities persist and are in part driven by limited digital literacy. To address digital disparities in primary care, we implemented an integrated digital health navigator program supporting portal use among patients with type II diabetes. During our pilot, we were able to enroll 121 (30.9%) patients onto the portal. Of [...]

Estimating the deterioration paths of chronic hepatitis B (CHB) patients is critical for physicians' decisions and patient management. A novel, hierarchical multilabel graph attention-based method aims to predict patient deterioration paths more effectively. Applied to a CHB patient data set, it offers strong predictive utilities and clinical value.