The vulnerabilities of computerized physician order entry systems: a qualitative study
Bibliographic record
Abstract
OBJECTIVE: To test the vulnerabilities of a wide range of computerized physician order entry (CPOE) systems to different types of medication errors, and develop a more comprehensive qualitative understanding of how their design could be improved. MATERIALS AND METHODS: The authors reviewed a random sample of 63,040 medication error reports from the US Pharmacopeia (USP) MEDMARX reporting system where CPOE systems were considered a "contributing factor" to errors and flagged test scenarios that could be tested in current CPOE systems. Testers entered these orders in 13 commercial and homegrown CPOE systems across 16 different sites in the United States and Canada, using both usual practice and where-needed workarounds. Overarching themes relevant to interface design and usability/workflow issues were identified. RESULTS: CPOE systems often failed to detect and prevent important medication errors. Generation of electronic alert warnings varied widely between systems, and depended on a number of factors, including how the order information was entered. Alerts were often confusing, with unrelated warnings appearing on the same screen as those more relevant to the current erroneous entry. Dangerous drug-drug interaction warnings were displayed only after the order was placed rather than at the time of ordering. Testers illustrated various workarounds that allowed them to enter these erroneous orders. DISCUSSION AND CONCLUSION: The authors found high variability in ordering approaches between different CPOE systems, with major deficiencies identified in some systems. It is important that developers reflect on these findings and build in safeguards to ensure safer prescribing for patients.
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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 | 0.057 | 0.135 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".