Frequency, type and clinical importance of medication history errors at admission to hospital: a systematic review
Bibliographic record
Abstract
BACKGROUND: Over a quarter of hospital prescribing errors are attributable to incomplete medication histories being obtained at the time of admission. We undertook a systematic review of studies describing the frequency, type and clinical importance of medication history errors at hospital admission. METHODS: We searched MEDLINE, EMBASE and CINAHL for articles published from 1966 through April 2005 and bibliographies of papers subsequently retrieved from the search. We reviewed all published studies with quantitative results that compared prescription medication histories obtained by physicians at the time of hospital admission with comprehensive medication histories. Three reviewers independently abstracted data on methodologic features and results. RESULTS: We identified 22 studies involving a total of 3755 patients (range 33-1053, median 104). Errors in prescription medication histories occurred in up to 67% of cases: 10%- 61% had at least 1 omission error (deletion of a drug used before admission), and 13%- 22% had at least 1 commission error (addition of a drug not used before admission); 60%- 67% had at least 1 omission or commission error. Only 5 studies (n = 545 patients) explicitly distinguished between unintentional discrepancies and intentional therapeutic changes through discussions with ordering physicians. These studies found that 27%- 54% of patients had at least 1 medication history error and that 19%- 75% of the discrepancies were unintentional. In 6 of the studies (n = 588 patients), the investigators estimated that 11%-59% of the medication history errors were clinically important. INTERPRETATION: Medication history errors at the time of hospital admission are common and potentially clinically important. Improved physician training, accessible community pharmacy databases and closer teamwork between patients, physicians and pharmacists could reduce the frequency of these errors.
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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 | 0.018 | 0.119 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.020 | 0.024 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".