Quality of electronic records documenting adverse drug reactions within a hospital setting: identification of discrepancies and information completeness.
Bibliographic record
Abstract
AIM: Incomplete and incorrect documentation of adverse drug reactions (ADRs) can restrict prescribing choices resulting in suboptimal pharmaceutical care. This study aimed to examine the quality of information held within electronic systems in a hospital setting, to determine the preciseness of ADR documentation, and identify discrepancies where multiple electronic systems are utilised. METHOD: Over a four-week period, consecutive patients admitted to the general medical ward at the study hospital had their electronic profiles reviewed. Patient demographic information (de-identified), ADR history and discrepancies between information sources (as recorded in all electronic systems utilised at initial prescribing) were recorded and analysed. RESULTS: Over the four-week period, 332 patient profiles were reviewed, and over 1,200 alerts were identified and analysed (including duplicates of ADR reactions). Of these patients, 151 (45.5%) had at least one documented allergy or intolerance which generated 585 reactions, relating to 526 unique events. A further 151 (45.5%) were classified as having no known (drug) allergies or intolerances; however, 20 (15%) of these patients did have at least one allergy documented in at least one other electronic system. The remaining 30 (9%) patients were classified as having an unknown allergy status and of those nine had allergies documented in at least one other electronic system. Further, most systems contained information duplication, which had not been addressed during the admission process. CONCLUSION: ADR information was both imprecise and inaccurate, as multiple discrepancies between ADR information recorded in different electronic patient management systems were found to exist. Information sharing between systems needs to be prioritised in order to allow full, accurate and complete ADR information to be collected, stored and utilised; both to reduce current inadequacies and to allow optimal pharmaceutical care.
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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 | 0.002 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".