ArticleJournal of general internal medicine2022
Understanding Physician Work and Well-being Through Social Network Modeling Using Electronic Health Record Data: a Cohort Study.
Article in Journal of general internal medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed, 11 citations in OpenAlex.
- Impact of Primary Care Team Configuration on Access and Quality of Care.Journal of general internal medicine · 2025Observational
- Large Language Model-Based Responses to Patients' In-Basket Messages.JAMA network open · 2024Article
- GRANDPA: GeneRAtive network sampling using degree and property augmentation applied to the analysis of partially confidential healthcare networks.Applied network science · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundUnderstanding association between factors related to clinical work environment and well-being can inform strategies to improve physicians' work experience.
objectiveTo model and quantify what drivers of work composition, team structure, and dynamics are associated with well-being.
designUtilizing social network modeling, this cohort study of physicians in an academic health center examined inbasket messaging data from 2018 to 2019 to identify work composition, team structure, and dynamics features. Indicators from a survey in 2019 were used as dependent variables to identify factors predictive of well-being.
participantsEHR data available for 188 physicians and their care teams from 18 primary care practices; survey data available for 163/188 physicians. MAIN MEASURES: Area under the receiver operating characteristic curve (AUC) of logistic regression models to predict well-being dependent variables was assessed out-of-sample. KEY
resultsThe mean AUC of the model for the dependent variables of emotional exhaustion, vigor, and professional fulfillment was, respectively, 0.665 (SD 0.085), 0.700 (SD 0.082), and 0.669 (SD 0.082). Predictors associated with decreased well-being included physician centrality within support team (OR 3.90, 95% CI 1.28-11.97, P=0.01) and share of messages related to scheduling (OR 1.10, 95% CI 1.03-1.17, P=0.003). Predictors associated with increased well-being included higher number of medical assistants within close support team (OR 0.91, 95% CI 0.83-0.99, P=0.05), nurse-centered message writing practices (OR 0.89, 95% CI 0.83-0.95, P=0.001), and share of messages related to ambiguous diagnosis (OR 0.92, 95% CI 0.87-0.98, P=0.01).
conclusionsThrough integration of EHR data with social network modeling, the analysis highlights new characteristics of care team structure and dynamics that are associated with physician well-being. This quantitative methodology can be utilized to assess in a refined data-driven way the impact of organizational changes to improve well-being through optimizing team dynamics and work composition.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.