Development and use of a computer program to detect potentially inappropriate prescribing in older adults residing in Canadian long-term care facilities
Bibliographic record
Abstract
BACKGROUND: Inappropriate prescribing has been estimated to be as high as 40% in long-term care. The purpose of this study was to develop a computer program that identifies potentially inappropriate drug prescriptions and to test its reliability. METHODS: Potentially inappropriate prescriptions were identified based on modified McLeod guidelines. A database from one pharmacy servicing long-term care facilities in Ontario was utilized for this cross-sectional study. Prescription information was available for the 356 long-term care residents and included: the date the prescription was filled, the quantity of drug prescribed and the eight-digit drug identification number. The pharmacy database was linked to the computer-based program for targeting potential inappropriate prescriptions. The computer program's reliability was assessed by comparing its results to a manual search conducted by two independent research assistants. RESULTS: There was complete agreement between the computer and manual abstraction for the total number of potentially inappropriate prescriptions detected. In total, 83 potentially inappropriate prescriptions were identified. Fifty-three residents (14.9%) received at least one potentially inappropriate prescription. Of those, twenty (37.7%) received two potential inappropriate prescriptions and eight (15.1%) received 3 or more potential inappropriate prescriptions. The most common potential inappropriate prescriptions were identified as long-term use of non-steroidal anti-inflammatory agents and tricyclic antidepressants with active metabolites. CONCLUSION: A computer program can accurately and automatically detect inappropriate prescribing in residents of long-term care facilities. This tool may be used to identify potentially inappropriate drug combinations and educate health care professionals.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".