A methodology pathway to develop a Computer Drug Safety program in Primary health care setting based on clinical audit
Bibliographic record
Abstract
Objective: Medications management is an area in Primary health care (PHC) and General Practice (GP) setting where decision making is very important. Computer Decision Support program have been developed to help primary physicians in their decisions and have proved effective in improving the process of care and promising in economic issues.Methods: In order to create a Computer Drug Safety (CDS) program for managing oral anticoagulant therapy for use in PHC and GP setting with developed Information Technology (IT) System and established electronic Health Records (eHRs), we used clinical audit (a real-life practice analysis) as the methodology framework. We assumed that this method would enable a proposed CDS program to cope with clinical complexity of GP patients taking oral anticoagulants and also suggest this method as the operative framework for Quality of Care (QC) improvement and practice research.Results: By using clinical audit, we were able to identify the list of elements necessary for building up a feasible CDS program for a long-term oral anticoagulant therapy surveillance, for use in PHC and GP setting. According to this list of elements, we were able to create a paper based concept (a schemata) for this program development. This CDS program would not be a simple drug-dose calculator, but a comprehensive software support system integrated within the existing IT work applications.Conclusions: The main benefits, expected from this proposed CDS program, include: learning from work experience, oral anticoagulant QC improvement, better patients compliance to long-term treatment with the drug warfarin, practice performance follow up and practice research.
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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 | 0.016 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".