Technology-Enabled Workplace Learning Through Rethinking Electronic Health Records to Support Performance Feedback: Protocol for a Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: The health sector collects a plethora of electronic health data via digital technologies, such as electronic health records (EHRs) and electronic medical records (EMRs). The primary use of EHRs includes supporting service delivery, providing data on patient information, and health care operations. Secondary uses of these systems can include quality improvement activities and research, and possibly inform policy. One underexplored secondary use of data from these systems is to enable health care professionals to understand their performance, reflect on their practice, and potentially support enhanced professional and workplace learning. There is growing interest and an increase in policies to focus on motivating the use of this type of data as part of mandatory Continuing Professional Development. Despite this, the design of EHRs is not conducive to the use of these systems for reflective practice, and there are few best practice guides for how to scaffold the use of these data for secondary use. OBJECTIVE: The aim of this project is to determine how EHRs and EMRs can be leveraged to enable formative performance feedback for health care professionals. The primary objective is to explore the use of these systems by health care professionals to further understand the current and possible future use of these records for reflective practice, performance feedback, and workplace learning. METHODS: The project will use a mixed methods design to enable a holistic picture of participant behaviors. Study data are being collected over 3 phases. Phase 1 consists of interviewing health care professionals and clinicians about their experiences with EHRs and EMRs. Phase 2 will involve surveying health care professionals about specific EHR features, and phase 3 will encompass workshopping discussions around EMR functionality and design with key informants. Participants for phases 1 and 2 will be a convenience sample of health care professionals who self-select and volunteer to participate in the study. Participants for phase 3 will consist of policy makers, representatives from peak bodies, technology vendors, health care professionals, and others. Data from phase 1 will be thematically analyzed to identify key features of EMR and EHR design for prioritization in phase 2. Phase 2 survey responses will be descriptively analyzed to understand the most important features in record design to support reflective practice. Phase 3 workshop data will be thematically analyzed to identify design insights for EHRs and EMRs that support professional learning. RESULTS: The project is currently in its interview phase and is expected to publish results in mid-2025. CONCLUSIONS: The project will generate new knowledge on the extent to which data collected by workplace technologies provide health care professionals with formative performance feedback. It will also develop a conceptual design for EHRs that supports health care professional learning, which could be leveraged by developers of these technologies in future implementations. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/66824.
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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 | 0.101 | 0.069 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.056 | 0.018 |
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".