Electronic medical record-related burnout in healthcare providers: a scoping review of outcomes and interventions
Bibliographic record
Abstract
OBJECTIVE: Healthcare provider (HCP) burnout is on the rise with electronic medical record (EMR) use being cited as a factor, particularly with the rise of the COVID-19 pandemic. Burnout in HCPs is associated with negative patient outcomes, and, therefore, it is crucial to understand and address each factor that affects HCP burnout. This study aims to (a) assess the relationship between EMR use and burnout and (b) explore interventions to reduce EMR-related burnout. METHODS: We searched MEDLINE (Ovid), CINAHL and SCOPUS on 29 July 2021. We selected all studies in English from any publication year and country that discussed burnout in HCPs (physicians, nurse practitioners and registered nurses) related to EMR use. Studies must have reported a quantitative relationship to be included. Studies that implemented an intervention to address this burnout were also included. All titles and abstracts were screened by two reviewers, and all full-text articles were reviewed by two reviewers. Any conflicts were addressed with a third reviewer and resolved through discussion. Quality of evidence of all included articles was assessed using the Quality Rating Scheme for Studies and Other Evidence. FINDINGS: The search identified 563 citations with 416 citations remaining after duplicate removal. A review of abstracts led to 59 studies available for full-text assessment, resulting in 25 studies included in the scoping review. Commonly identified associations between EMR-related burnout in HCPs included: message and alert load, time spent on EMRs, organisational support, EMR functionality and usability and general use of EMRs. Two articles employed team-based interventions to improve burnout symptoms without significant improvement in burnout scores. CONCLUSIONS AND RELEVANCE: Current literature supports an association between EMR use and provider burnout. Very limited evidence exists for burnout-reducing interventions that address factors such as time spent on EMRs, organisational support or EMR design.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.037 | 0.140 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.031 | 0.030 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".