Behavioral Health Order Sets in a Hybrid Information Environment
Bibliographic record
Abstract
INTRODUCTION: The Centre for Addiction and Mental Health (CAMH) is a 500 bed freestanding psychiatric hospital in Canada. We are in the process of preparing for an integrated commercial clinical information system, which will have computerized physician order entry (CPOE) functionality. METHODS: As a preparation for CPOE, we developed inpatient order sets (OSs). Development teams from individual clinical programs created and sent their OSs to an OS Working Group for initial endorsement, and then to Pharmacy & Therapeutics and Medical Advisory committees subsequent approvals. RESULTS: In twelve months we created and introduced 22 behavioral health OSs across eight clinical programs in our hybrid information system with an excellent adoption rate (>97%) by clinicians. DISCUSSION: The development and implementation temporarily contributed to a multifactorial flow problem in the emergency department (ED), which was addressed by substantially simplifying the General Admission via the ED OS. Also, as the OSs were developed and sent for approval the project identified areas where local clinical practice can improve. Our electronic-paper hybrid set of clinical systems was a major factor impacting the effort.
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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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".