Training physicians and residents for the use of Electronic Health Records—A comparative case study between two hospitals
Bibliographic record
Abstract
BACKGROUND/PURPOSE: Despite widespread use of Electronic Health Records (EHR), the promise of benefits has not been clearly realised due, in part, to inadequate physician training. Training for EHR use is a highly complex intervention that occurs in a dynamic socio-technical health system. The purpose of this study was to describe and critically assess the interplay between educational activities and organisational factors that influenced EHR training and implementation across two different hospitals. METHODS: Based in a socio-technical framework, a comparative qualitative case study was undertaken as well suited to real-world processes. Semi-structured interviews were completed (n = 43), representing administrative leaders, staff physicians, residents and EHR trainers from two Canadian academic hospitals. Thematic analysis was employed for analysis. RESULTS: Similar findings were noted at both hospitals despite different implementation strategies. Despite mandatory training, physicians described limited transferability of training to the workplace. Factors contributing to this included standardised vendor modules (lacking specificity for their clinical context); variable EHR trainer expertise; limited post-launch training; and insufficient preparation for changes to workflow. They described learning while caring for patients and using workarounds. Strong emotional responses were described, including anger, frustration, anxiety and fear of harming patients. CONCLUSIONS: Training physicians for effective EHR utilisation requires organisational culture transformation as EHRs impacts all aspects of clinical workflows. Analytic thinking to consider workflows, ongoing post-launch training and the recognition of the interdependency of multiple factors are critical to preparing physicians to provide effective clinical care, and potentially reducing burnout. A list of key considerations is provided for educational leaders.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".