Pilot-testing an adverse drug event reporting form prior to its implementation in an electronic health record
Bibliographic record
Abstract
BACKGROUND: Adverse drug events (ADEs), harmful unintended consequences of medication use, are a leading cause of hospital admissions, yet are rarely documented in a structured format between care providers. We describe pilot-testing structured ADE documentation fields prior to integration into an electronic medical record (EMR). METHODS: We completed a qualitative study at two Canadian hospitals. Using data derived from a systematic review of the literature, we developed screen mock-ups for an ADE reporting platform, iteratively revised in participatory workshops with diverse end-user groups. We designed a paper-based form reflecting the data elements contained in the mock-ups. We distributed them to a convenience sample of clinical pharmacists, and completed ethnographic workplace observations while the forms were used. We reviewed completed forms, collected feedback from pharmacists using semi-structured interviews, and coded the data in NVivo for themes related to the ADE form. RESULTS: We completed 25 h of clinical observations, and 24 ADEs were documented. Pharmacists perceived the form as simple and clear, with sufficient detail to capture ADEs. They identified fields for omission, and others requiring more detail. Pharmacists encountered barriers to documenting ADEs including uncertainty about what constituted a reportable ADE, inability to complete patient follow-up, the need for inter-professional communication to rule out alternative diagnoses, and concern about creating a permanent record. CONCLUSION: Paper-based pilot-testing allowed planning for important modifications in an ADE documentation form prior to implementation in an EMR. While paper-based piloting is rarely reported prior to EMR implementations, it can inform design and enhance functionality. Piloting with other groups of care providers and in different healthcare settings will likely lead to further revisions prior to broader implementations.
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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 | 0.091 | 0.188 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".