Elucidating system‐level interdependence in electronic health record data: What are the ramifications for trainee assessment?
Bibliographic record
Abstract
CONTEXT: The electronic health record (EHR) has been identified as a potential site for gathering data about trainees' clinical performance, but these data are not collected or organised for this purpose. Therefore, a careful and rigorous approach is required to explore how EHR data could be meaningfully used for assessment purposes. The purpose of this study was to identify EHR performance metrics that represent both the independent and interdependent clinical performance of emergency medicine (EM) trainees and explore how they might be meaningfully used for assessment and feedback. METHODS: Using constructivist grounded theory, we conducted 21 semi-structured interviews with EM faculty members and residents. Participants were asked to identify the clinical actions of trainees that would be valuable for assessment and feedback and describe how those activities are represented in the EHR. Data collection and analysis, which consisted of three stages of coding, occurred iteratively. RESULTS: When faculty members and trainees in EM were asked to reflect on the usefulness of using EHR performance metrics for resident assessment and feedback they expressed both widespread support for the idea in principle and hesitation that aspects of clinical performance captured in the data would not be representative of residents' individual performance, but would rather reflect their interdependence with other team members and the systems in which they work. We highlight three categorisations of system-level interdependence - medical directives, technological systems and organisational systems - identified by our participants, and discuss strategies participants employed to navigate these forms of interdependence within the health care system. CONCLUSIONS: System-level interdependence shapes physicians' performances, and yet, this impact is rarely corrected for or noted within clinical performance data. Educators have a responsibility to recognise system-level interdependence when teaching and consider system-level interdependence when assessing the performance of trainees in order to most effectively and fairly utilise the EHR as a source of assessment data.
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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 | 0.209 | 0.460 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.015 | 0.026 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| 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".