Electronic medical record system at an opioid agonist treatment programme: study design, pre‐implementation results and post‐implementation trends
Bibliographic record
Abstract
RATIONALE: Electronic medical record (EMR) systems are commonly included in health care reform discussions. However, their embrace by the health care community has been slow. METHODS: At Addiction Research and Treatment Corporation, an outpatient opioid agonist treatment programme that also provides primary medical care, HIV medical care and case management, substance abuse counselling and vocational services, we studied the implementation of an EMR in the domains of quality, productivity, satisfaction, risk management and financial performance utilizing a prospective pre- and post-implementation study design. RESULTS: This report details the research approach, pre-implementation findings for all five domains, analysis of the pre-implementation findings and some preliminary post-implementation results in the domains of quality and risk management. For quality, there was a highly statistically significant improvement in timely performance of annual medical assessments (P < 0.001) and annual multidiscipline assessments (P < 0.0001). For risk management, the number of events was not sufficient to perform valid statistical analysis. CONCLUSIONS: The preliminary findings in the domain of quality are very promising. Should the findings in the other domains prove to be positive, then the impetus to implement EMR in similar health care facilities will be advanced.
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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 | 0.022 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".