Using Electronic Health Records to Mitigate Workplace Burnout Among Clinicians During the COVID-19 Pandemic: Field Study in Iran
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic spread worldwide in 2020. Notably, in the countries dealing with massive casualties, clinicians have worked in new conditions characterized by a heavy workload and a high risk of being infected. The issue of clinician burnout during the pandemic has attracted considerable attention in health care research. Electronic health records (EHRs) provide health care workers with several features to meet a health system's clinical needs. OBJECTIVE: We aim to examine how the use of EHR features affects the burnout of clinicians working in hospitals that have special wards for confirmed COVID-19 cases. METHODS: Using an online survey, we collected data from 368 physicians, physician assistants, and nurses working in six hospitals that have implemented EHRs in the city of Tehran in Iran. We used logistic regression to assess the association between burnout and awareness of EHR features, EHR system usability, concerns about COVID-19, technology solutions, hospital technology interventions, hospital preparedness, and professional efficacy adjusted for demographic and practice characteristics. RESULTS: The primary outcome of our study was self-reported burnout during the COVID-19 pandemic. Of the 368 respondents, 36% (n=134) reported having at least one symptom of burnout. Participants indicated that the leading cause of EHR-related stress is inadequate training for using technology (n=159, 43%), followed by having less face-to-face time with patients (n=140, 38%). Positive perceptions about the EHR's ease of use were associated with lower odds of burnout symptoms. More interventions, such as clear communication of regulations; transparency in policies, expectations, and goals regarding the use of technology in the clinical workflow; and hospital preparedness to cope with the challenges of the pandemic, were associated with lower odds of burnout. CONCLUSIONS: The use of EHR applications, hospital pandemic preparation programs, and transparent technology-related policies and procedures throughout the epidemic can be substantial mitigators of technology-based stress and clinician burnout. Hospitals will then be better positioned to devise or modify technology-related policies and procedures to support physicians' and nurses' well-being during the COVID-19 pandemic. Training programs, transparency in communications of regulations, and developing a clear channel for informing clinicians of changes in policies may help reduce burnout symptoms among physicians and nurses during a pandemic. Providing easily accessible mentorship through teleconsultation and 24-hour available information technology support may also help to mitigate the odds of burnout.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".