Potential benefit of electronic pharmacy claims data to prevent medication history errors and resultant inpatient order errors
Bibliographic record
Abstract
OBJECTIVE: We sought to assess the potential of a widely available source of electronic medication data to prevent medication history errors and resultant inpatient order errors. METHODS: We used admission medication history (AMH) data from a recent clinical trial that identified 1017 AMH errors and 419 resultant inpatient order errors among 194 hospital admissions of predominantly older adult patients on complex medication regimens. Among the subset of patients for whom we could access current Surescripts electronic pharmacy claims data (SEPCD), two pharmacists independently assessed error severity and our main outcome, which was whether SEPCD (1) was unrelated to the medication error; (2) probably would not have prevented the error; (3) might have prevented the error; or (4) probably would have prevented the error. RESULTS: Seventy patients had both AMH errors and current, accessible SEPCD. SEPCD probably would have prevented 110 (35%) of 315 AMH errors and 46 (31%) of 147 resultant inpatient order errors. When we excluded the least severe medication errors, SEPCD probably would have prevented 99 (47%) of 209 AMH errors and 37 (61%) of 61 resultant inpatient order errors. SEPCD probably would have prevented at least one AMH error in 42 (60%) of 70 patients. CONCLUSION: When current SEPCD was available for older adult patients on complex medication regimens, it had substantial potential to prevent AMH errors and resultant inpatient order errors, with greater potential to prevent more severe errors. Further study is needed to measure the benefit of SEPCD in actual use at hospital admission.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".