Computerised provider order entry and residency education in an academic medical centre
Bibliographic record
Abstract
CONTEXT: Many academic medical centres (AMCs) have introduced institutional policies, changed processes of care and implemented new technologies to improve health care quality. The impact of such changes on medical education has received little attention. We examine the impact of computerised provider order entry (CPOE) on the educational experiences of medical trainees who work and train in AMCs. METHODS: We conducted semi-structured interviews of postgraduate trainees and attending physicians in internal medicine at five AMCs (two with CPOE, three without CPOE). Trainees routinely rotate from CPOE to non-CPOE AMCs, whereas some attending physicians work at both types of AMC and are therefore well positioned to reflect on differences between CPOE and non-CPOE learning environments. Data collection and analysis used grounded theory methods. We sampled purposively until we achieved theoretical saturation. RESULTS: Our study included 11 residents and six attending physicians. Computerised provider order entry had both positive and negative impacts on five aspects of postgraduate training: (i) learning (better for medication interactions and availability of learning resources; worse for learning medication doses); (ii) teaching (more medication information available to enhance case discussions; fewer face-to-face teaching opportunities); (iii) feedback (improved ability to observe medication ordering behaviours to inform feedback; less provision of direct feedback); (iv) clinical supervision (facilitates efficient and safe supervision from a distance; may impede trainee independence), and (v) trainee assessment (increased opportunity to assess clinical decision-making and organisational skills). CONCLUSIONS: We identify five key educational themes that are positively and negatively impacted by CPOE. These themes form a conceptual framework that could be applied to define the educational impact of other health care quality and patient safety practices. This will help educators to identify educational opportunities and protect the safety of the training experience of residents in AMCs.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".