Dataset Information

Understanding Physician Work and Well-being Through Social Network Modeling Using Electronic Health Record Data: a Cohort Study.

ABSTRACT:

Background

Understanding association between factors related to clinical work environment and well-being can inform strategies to improve physicians' work experience.

Objective

To model and quantify what drivers of work composition, team structure, and dynamics are associated with well-being.

Design

Utilizing 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.

Participants

EHR 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 results

The 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).

Conclusions

Through 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.

SUBMITTER: Escribe C

PROVIDER: S-EPMC9640486 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Understanding Physician Work and Well-being Through Social Network Modeling Using Electronic Health Record Data: a Cohort Study.

Escribe Célia C Eisenstat Stephanie A SA Palamara Kerri K O'Donnell Walter J WJ Wasfy Jason H JH Del Carmen Marcela G MG Lehrhoff Sara R SR Bravard Marjory A MA Levi Retsef R

Journal of general internal medicine 20220128 15

<h4>Background</h4>Understanding association between factors related to clinical work environment and well-being can inform strategies to improve physicians' work experience.<h4>Objective</h4>To model and quantify what drivers of work composition, team structure, and dynamics are associated with well-being.<h4>Design</h4>Utilizing 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, ...[more]

PMID: 35091916

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Project description:IntroductionElectronic medical record (EMR) systems are used worldwide as repositories for patients' clinical information, providing clinical decision support and increasing visibility of and access to clinical information. While EMR systems facilitate improved healthcare delivery, emerging reports suggest potential detrimental effects on clinician well-being. EMR system implementation influences on nurses' work motivation, engagement, satisfaction and well-being (including burnout) are not well understood, nor have they been examined in relation to contextual factors and mechanisms of action. This paper presents a realist review protocol to examine causal explanations to address the question: How, why and under what circumstances does the implementation of a new hospital EMR system or similar technology impact nurses' work motivation, engagement, satisfaction or well-being?Methods and analysisThe five-step method for realist review will be used to identify causal relationships, how the relationships work, for whom and under what circumstances: (1) defining the review scope; (2) developing initial program theories; (3) searching the evidence; (4) selecting and appraising the evidence; (5) extracting and synthesising the data. Initial program theories were developed using scoping review findings and qualitative data collected from nurses pre-EMR and post-EMR. Five databases will be systematically searched from 1 January 2000 to 31 October 2021 (APA PsycInfo, CINAHL, Embase, IEEE Xplore and MEDLINE Complete), and forward and backward citation searching, grey literature searching and literature recommended by the research team. Search results will be screened by two research team members. Data extracted will assist in refining program theories to develop a conceptual model that synthesises how work motivation, engagement, satisfaction and well-being may influence, or be influenced by, an EMR implementation.Ethics and disseminationThe larger project has previously obtained low-risk ethics approval. The review will be published in a peer-reviewed journal and reported as per RAMESES guidelines.Prospero registration numberCRD42020131875.

Project description:BackgroundHigh expectations have been set for the implementations of health information systems (HIS) in health care. However, nurses have been dissatisfied after implementations of HIS. In particular, poorly functioning electronic health records (EHRs) have been found to induce stress and cognitive workload. Moreover, the need to learn new systems may require considerable effort from nurses. Thus, EHR implementations may have an effect on the well-being of nurses.ObjectiveThis study aimed to examine the associations of EHR-to-EHR implementations and the sufficiency of related training with perceived stress related to information systems (SRIS), time pressure, and cognitive failures among registered nurses. Moreover, we examined the moderating effect of the employment sector (hospital, primary care, social services, and others) on these associations.MethodsThis study was a cross-sectional survey study of 3610 registered Finnish nurses in 2020. EHR implementation was measured by assessing whether the work unit of each respondent had implemented or will implement a new EHR (1) within the last 6 months, (2) within the last 12 months, (3) in the next 12 months, and (4) at no point within the last 12 months or in the forthcoming 12 months. The associations were examined using analyses of covariance adjusted for age, gender, and employment sector.ResultsThe highest levels of SRIS (adjusted mean 4.07, SE 0.05) and time pressure (adjusted mean 4.55, SE 0.06) were observed among those who had experienced an EHR implementation within the last 6 months. The lowest levels of SRIS (adjusted mean 3.26, SE 0.04), time pressure (adjusted mean 4.41, SE 0.05), and cognitive failures (adjusted mean 1.84, SE 0.02) were observed among those who did not experience any completed or forthcoming implementations within 12 months. Nurses who perceived that they had received sufficient implementation-related training experienced less SRIS (F1=153.40, P<.001), time pressure (F1=80.95, P<.001), and cognitive failures (F1=34.96, P<.001) than those who had received insufficient training. Recent implementations and insufficient training were especially strongly associated with high levels of SRIS in hospitals.ConclusionsEHR implementations and insufficient training related to these implementations may endanger the well-being of nurses and even lead to errors. Thus, it is extremely important for organizations to offer comprehensive training before, during, and after implementations. Moreover, easy-to-use systems that allow transition periods, a re-engineering approach, and user involvement may be beneficial to nurses in the implementation process. Training and other improvements would be especially important in hospitals.

Dataset Information

Understanding Physician Work and Well-being Through Social Network Modeling Using Electronic Health Record Data: a Cohort Study.

Background

Objective

Design

Participants

Main measures

Key results

Conclusions

Publications

Understanding Physician Work and Well-being Through Social Network Modeling Using Electronic Health Record Data: a Cohort Study.

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