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Systematic reviews in comparative effectiveness research require timely evidence synthesis. With the rapid advancement of medical research, preprint articles play an increasingly important role in accelerating knowledge dissemination. However, as preprint articles are not peer-reviewed before publication, their quality varies significantly, posing challenges for evidence inclusion in systematic reviews.

Clinician implicit bias can impede patient-centered communication, leading to health care inequities. While the field of implicit bias education is evolving with advances in technology, clinicians' perspectives remain underexplored. This study investigated clinicians' perceptions of educational strategies to complement communication feedback technology in the implementation of an implicit bias education intervention.

We discuss challenges using computational modeling approaches for personalized prediction in clinical practice to predict treatment response for rare diseases treated by novel therapies using clinical oncology as an example context. Several challenges are discussed, including data scarcity, data sparsity, and difficulties in establishing interdisciplinary teams. Machine learning (ML), mechanistic modeling (MM), and hybrid modeling (HM) are discussed in the context of these challenges.

Pediatric hospital readmissions increase healthcare costs and highlight gaps in care. Social determinants of health (SDOH), such as housing and transportation insecurity, significantly impact outcomes but are underexplored in pediatric populations.

Accurately measuring patient similarity is essential for precision medicine, enabling personalized predictive modeling, disease subtyping, and individualized treatment by identifying patients with similar characteristics to an index patient. This study aims to develop an electronic health record-based patient similarity estimation framework to enhance personalized predictive modeling for Acute Kidney Injury (AKI), a complex and life-threatening condition where accurate prediction is critical for timely intervention.

To improve prediction of chronic kidney disease (CKD) progression to end-stage renal disease (ESRD) using machine learning (ML) and deep learning (DL) models applied to integrated clinical and claims data with varying observation windows, supported by explainable artificial intelligence (AI) to enhance interpretability and reduce bias.

Electronic health record (EHR) systems have become integral to hospital-based care, with studies showing physicians spending significant time interfacing with these systems. While EHR interactions are necessary for patient care, understanding usage patterns can identify opportunities for system optimization and workflow improvement. Previous studies have focused on outpatient settings, making this study among the first to comprehensively analyze inpatient EHR interaction patterns.This study aims to characterize EHR utilization patterns among [...]

While nursing summaries in electronic health records are used for initial orientation to a patient's status, research on nurses' use of these summaries remains scarce. This case study conducted an eye-tracking simulation to identify (1) key information types (orders, vital signs, etc.), (2) frequently paired information types, and (3) common sequential patterns of information types within nursing summaries as nurses review simulated patient cases.We recruited 33 medical-surgical nurses from a [...]

Hypertension is a significant contributor to cardiovascular disease, yet evidence-based blood pressure (BP) control practices are inconsistently applied. The Collaboration Oriented Approach to Controlling High Blood Pressure (COACH) is a digital clinical decision support tool designed to improve BP self-management and support clinician workflows. While the patient perspective on COACH has been evaluated in a separate study, this study evaluates organizational readiness for COACH implementation across three health systems using [...]

The Health Level 7 (HL7) Electronic Health Record Workgroup identified home medication list reconciliation as a prime opportunity to improve patient safety and reduce clinician burden. We developed a platform-neutral, Fast Healthcare Interoperability Resources (FHIR)-enabled reference model and demonstration wireframe to articulate the concepts of an interoperable, patient-centric home medication list management ecosystem.Four principal artifacts describe the reference model: (1) a conceptual (high-level) model, (2) a data architecture (detailed) model [...]