Impact of vendor computerized physician order entry on patients with renal impairment in community hospitals
Bibliographic record
Abstract
BACKGROUND: Adverse drug events (ADEs) are common among hospitalized patients with renal impairment. OBJECTIVE: To determine whether computerized physician order entry (CPOE) systems with clinical decision support capabilities reduce the frequency of renally related ADEs in hospitals. DESIGN, SETTING, AND PATIENTS: Quasi-experimental study of 1590 adult patients with renal impairment who were admitted to 5 community hospitals in Massachusetts from January 2005 to September 2010, preimplementation and postimplementation of CPOE. INTERVENTION: Varying levels of clinical decision support, ranging from basic CPOE only (sites 4 and 5), rudimentary clinical decision support (sites 1 and 2), and advanced clinical decision support (site 3). MEASUREMENTS: Primary outcome was the rate of preventable ADEs from nephrotoxic and/or renally cleared medications. Similarly, secondary outcomes were the rates of overall ADEs and potential ADEs. KEY RESULTS: There was a 45% decrease in the rate of preventable ADEs following implementation (8.0/100 vs 4.4/100 admissions; P < 0.01), and the impact was related to the level of decision support. Basic CPOE was not associated with any significant benefit (4.6/100 vs 4.3/100 admissions; P = 0.87). There was a nonsignificant decrease in preventable ADEs with rudimentary clinical decision support (9.1/100 vs 6.4/100 admissions; P = 0.22). However, substantial reduction was seen with advanced clinical decision support (12.4/100 vs 0/100 admissions; P = 0.01). Despite these benefits, a significant increase in potential ADEs was found for all systems (55.5/100 vs 136.8/100 admissions; P < 0.01). CONCLUSION: Vendor-developed CPOE with advanced clinical decision support can reduce the occurrence of preventable ADEs but may be associated with an increase in potential ADEs.
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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 | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".