Electronic health record associated stress: A survey study of adult congenital heart disease specialists
Bibliographic record
Abstract
BACKGROUND: Physician burnout has many undesirable consequences, including negative impact on patient care delivery and physician career satisfaction. Electronic health records (EHRs) may exacerbate burnout by increasing physician workload. OBJECTIVE: To determine burnout in adult congenital heart disease (ACHD) specialists by assessing stress associated with EHRs. DESIGN: Electronic survey study of ACHD providers. SETTING: Canada and United States. PARTICIPANTS: Three hundred eighty-three ACHD specialists listed on the Adult Congenital Heart Association directory between February and April 2017. OUTCOME MEASURES: Burnout was measured using the Maslach Burnout Inventory (MBI) to understand factors contributing to work life and EHR satisfaction. Chi-square and Wilcoxon Rank Sum tests were used for statistical analysis. RESULTS: Of the 383 invited participants, 110 (28.7%) completed surveys with the majority (n = 88, 80.7%) reporting from an academic medical center. Burnout was defined as high scores on the emotional exhaustion and/or depersonalization MBI subscales. When comparing the 40% (n = 44) that met criteria for burnout with those that did not, there was strong disagreement that a reasonable amount of time is spent on clerical tasks related to direct (P = .0043) or indirect (P = .0004) patient care. There was strong disagreement that EHRs increased efficiency (P = .006) or the patient portal improved patient care (P = .0215). Finally, physicians who met criteria for burnout had lower personal accomplishment scores (P = .0355). CONCLUSIONS: Our results suggest time spent on EHRs creates clerical burden exacerbating ACHD physician burnout. The high levels of emotional exhaustion may decrease quality of ACHD care by directing focus away from physician-patient interaction. Health care systems must develop best practice for EHR design and implementation to optimize patient advocacy and care, and decrease physician burnout.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".