Effects of an integrated clinical information system on medication safety in a multi-hospital setting
Bibliographic record
Abstract
PURPOSE: The implementation of vendor-based integrated clinical information technology was studied, and its effect on medication errors throughout the medication-use process in a health care system was evaluated. METHODS: The integrated systems selected for implementation included computerized physician order entry, pharmacy and laboratory information systems, clinical decision-support systems (CDSSs), electronic drug dispensing systems (EDDSs), and a bar-code point-of-care medication administration system. The primary endpoint was the reduction in related medication errors. Secondary endpoints included the reductions in medication order turnaround time and EDDS override transactions. RESULTS: Integrated clinical information system technology was implemented in a multihospital health care system with a phased-in approach. A positive effect of this integration on medication errors throughout the medication-use process was demonstrated. Most prescribing errors decreased significantly in the selected categories monitored, specifically drug allergy detection, excessive dosing, and incomplete or unclear orders. Pharmacists were also twice as likely to identify dosages requiring adjustment for renal insufficiency when the integrated technology was in place and more than six times as likely for drug levels outside of the therapeutic range. A positive effect on medication administration safety was also demonstrated: 73 administration-related errors were intercepted through electronic bar-code scanning for every 100,000 doses charted. CONCLUSION: Integration of clinical information system technology decreased selected types of medication errors throughout the medication-use process in a health care system and improved therapeutic drug monitoring in patients with renal insufficiency and in patients receiving drugs with narrow therapeutic ranges through the use of CDSS alerts.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".