SHM Converge Scientific Abstract ePoster Gallery-A Precision Science Approach to Work Design: Linking Ehr Workload Metadata, Clinician Well-Being, and Patient Outcomes Across 12 Hospitals

A Precision Science Approach to Work Design: Linking Ehr Workload Metadata, Clinician Well-Being, and Patient Outcomes Across 12 Hospitals

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This multisite observational study examined whether EHR-derived workload measures can validly reflect inpatient workload and relate to clinician well-being and patient outcomes. The researchers studied 12 US hospital medicine groups, including 420 clinicians and 20,242 discharges. Data came from a one-time clinician and leader survey, six months of Epic Signal workload metadata, and Vizient outcomes data. Analyses included role-stratified correlations, mixed-effects regression, and exploratory predictive modeling.<br /><br />The study found that EHR-derived use measures showed construct validity when compared with clinician-reported workload measures, suggesting they can be used at scale in inpatient hospitalist settings. Work design factors were associated with clinician burnout, likelihood of leaving, reducing hours, and with patient outcomes such as ICU transfer, length of stay, and cost. The results suggest that clinician well-being and patient outcomes are influenced not only by patient complexity, but also by how work is organized at the group level.<br /><br />A key implication is that aggregated EHR use metrics can capture sustained workload burden and may help health systems identify workload problems and redesign inpatient work. However, these measures do not capture within-shift variation, limiting their ability to fully describe real-time workload dynamics. This highlights the need for more granular workload measures to better guide interventions.<br /><br />Overall, the study concludes that EHR-derived workload measures are feasible and meaningful for evaluating inpatient work design, and that work design is an important driver of both clinician well-being and patient outcomes.

Asset Subtitle

Marisha Burden

Meta Tag

Author List Angela Keniston, CT Lin, Ethan Molitch-Hou, Kathryn Filson, Kathyrn Colborn, Kirsten N. Kangelaris, Lauren McBeth, Lotte Dyrbye, Lucie Uncapher, Marisha Burden, Matthew Sakumoto, Natalie v. Schwatka, Naveen Baskaran, Nima Yazdani, Oluseyi A. Fayanju,

Category Research

Concept Inpatient Workload

Concept Clinician Burnout

Concept Patient Harm

Concept Validated Measure

Concept Structured EHR Data

Distinguished Finalist

Presenter Organization University of Colorado School of Medicine

Presenting Author Marisha Burden

Track Translating Research into Practice

Keywords

EHR-derived workload

inpatient workload

hospital medicine

clinician burnout

work design

clinician well-being

patient outcomes

Epic Signal

mixed-effects regression

workload measures

Inpatient Workload

Clinician Burnout

Patient Harm

Validated Measure

Structured EHR Data