Computerized Physician Order Entry with Clinical Decision Support in the Long‐Term Care Setting: Insights from the Baycrest Centre for Geriatric Care
Bibliographic record
Abstract
Although computerized physician order entry (CPOE) has been successfully implemented in many acute care hospitals, few descriptions of its use in the long-term care (LTC) setting are available. This report describes the experiences of one LTC facility in developing and implementing a CPOE system with clinical decision support (CDS). Even when a facility has the necessary resources and "institutional will," many challenges are associated with the implementation of this application. The system was designed to meet the needs of healthcare providers in the LTC setting, in particular by informing prescribing decisions, reducing the frequency of prescribing and monitoring errors, and reducing adverse drug event rates. Based on experience adopting this technology early, 10 insights are offered that it is hoped will assist others who are considering the implementation of CPOE systems with CDS in the LTC setting.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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".