Computerized Alerts Can Decrease Ordering of Dopamine Blocking Drugs for Hospitalized Patients with Parkinson’s Disease (S40.008)
Bibliographic record
Abstract
Objective: To determine if alerts in a computerized physician order entry system can decrease the ordering of dopamine blocking medications for hospitalized patients with Parkinson’s disease. Background: Medications that block central dopamine receptors result in worsening of motor symptoms in Parkinson’s disease. Computerized physician order entry systems are capable of producing custom alerts which can potentially decrease prescription of these medications in patients with Parkinson’s disease. Methods: Alerts were created that would fire if either metoclopramide or a neuroleptic (excluding quetiapine or clozapine) were prescribed in a hospitalized patient who also was being prescribed levodopa. Suggestions were provided to use an order set “Antinausea options for older patients” which did not contain dopamine blocking agents or quetiapine if an anti-psychotic was truly necessary. Both alerts advised that prescribing these medications worsen Parkinson’s disease symptoms. A report of actions taken on these alerts was generated to allow for data analysis. Results: Over a one year period these alerts fired in 372 order sessions and in 26.1[percnt] the order for the offending agent was cancelled. For the initial order session in which an alert displayed 32[percnt] (86/269) resulted in the medication being cancelled whereas with subsequent alerts the medication was cancelled in 10.7[percnt] (11/103). Metoclopramide accounted for 47.6[percnt] of the alerts and the order was cancelled in 36.7[percnt]. An alert for a neuroleptic was the trigger in 52.4[percnt] and the order was cancelled in 16.4[percnt]. Conclusions: An alert that displays both the rationale and a suggestion for an alternative can decrease the prescription of central dopamine blocking agents to inpatients with Parkinson’s disease. The alert for metoclopramide had more impact than the alert for neuroleptics. Subsequent alerts for the same medication were less likely to result in the order being cancelled than the initial alert.
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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 | 0.001 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".