Introduction: Addressing physician burnout is critical for healthcare systems. As electronic health record (EHR) workload and teamwork have been identified as major contributing factors to physician well-being, we aimed to mitigate burnout through EHR-based interventions and a compassion team practice (CTP), targeting EHR workload and team cohesion.
Methods: A modified stepped wedge-clustered randomized trial was conducted, involving specialties with heavy InBasket workloads. EHR interventions included quick-action shortcuts and recommended practice for secure chats. The CTP comprised 30-s practice between physicians and their dyad partners. Survey and EHR data were collected over four assessment periods. Linear and generalized mixed-effects models assessed intervention effects, accounting for covariates.
Results: Forty-four physicians participated (20% participation rate). While burnout prevalence decreased from 58.5% at baseline to 50.0% at the end of the study, burnout reduction was not statistically significant after EHR (OR 0.43, 95% CI 0.12 to 1.61, = 0.21) or EHR + CTP (OR 0.60, 95% CI 0.17 to 2.10, = 0.42) interventions. Statistically significant greater perceived ease of EHR work resulted from both the EHR intervention (coefficient 0.76, 95% CI 0.22 to 1.29, = 0.01) and EHR + CTP intervention (coefficient 0.80, 95% CI 0.26 to 1.35, < 0.01). EHR + CTP increased perceived workplace supportiveness (coefficient 0.61, 95% CI -0.04 to 1.26, = 0.07). Total number of InBasket messages/week increased significantly after EHR interventions (coefficient = 27.4, 95% CI 6.69 to 48.1, = 0.011) and increased after EHR + CTP (18.5, 95% CI -3.15 to 40.2, = 0.097).
Conclusion: While burnout reduction was not statistically significant, EHR interventions positively impacted workload perceptions. CTP showed potential for improving perceived workplace supportiveness. Further research is needed to explore the efficacy of CTP with more participants. The interventions gained interest beyond our institution and prompted consideration for broader implementation.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11733442 | PMC |
http://dx.doi.org/10.1002/lrh2.10444 | DOI Listing |
J Am Med Inform Assoc
January 2025
Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, 37203, United States.
Objective: To develop a framework that models the impact of electronic health record (EHR) systems on healthcare professionals' well-being and their relationships with patients, using interdisciplinary insights to guide machine learning in identifying value patterns important to healthcare professionals in EHR systems.
Materials And Methods: A theoretical framework of EHR systems' implementation was developed using interdisciplinary literature from healthcare, information systems, and management science focusing on the systems approach, clinical decision-making, and interface terminologies.
Observations: Healthcare professionals balance personal norms of narrative and data-driven communication in knowledge creation for EHRs by integrating detailed patient stories with structured data.
Diabetes Care
January 2025
Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ.
Objective: We derive and validate D-RISK, an electronic health record (EHR)-driven risk score to optimize and facilitate screening for undiagnosed dysglycemia (prediabetes + diabetes) in clinical practice.
Research Design And Methods: We used retrospective EHR data (derivation sample) and a prospective diabetes screening study (validation sample) to develop D-RISK. Logistic regression with backward selection was used to predict dysglycemia (HbA1c ≥5.
Introduction: Addressing physician burnout is critical for healthcare systems. As electronic health record (EHR) workload and teamwork have been identified as major contributing factors to physician well-being, we aimed to mitigate burnout through EHR-based interventions and a compassion team practice (CTP), targeting EHR workload and team cohesion.
Methods: A modified stepped wedge-clustered randomized trial was conducted, involving specialties with heavy InBasket workloads.
Learn Health Syst
January 2025
Department of Biostatistics, Epidemiology and Informatics University of Pennsylvania Perelman School of Medicine Philadelphia Pennsylvania USA.
Introduction: The rapid adoption of electronic health record (EHR) systems has resulted in extensive archives of data relevant to clinical research, hospital operations, and the development of learning health systems. However, EHR data are not frequently available, cleaned, standardized, validated, and ready for use by stakeholders. We describe an in-progress effort to overcome these challenges with cooperative, systematic data extraction and validation.
View Article and Find Full Text PDFLearn Health Syst
January 2025
Department of Biomedical Informatics University of Arkansas for Medical Sciences, College of Medicine Little Rock Arkansas USA.
Objective: This project demonstrates the feasibility of connecting medical imaging data and features, SARS-CoV-2 genome variants, with clinical data in the National Clinical Cohort Collaborative (N3C) repository to accelerate integrative research on detection, diagnosis, and treatment of COVID-19-related morbidities. The N3C curated a rich collection of aggregated and de-identified electronic health records (EHR) data of over 18 million patients, including 7.5 million COVID-positive patients, seen at hospitals across the United States.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!