Can hand-held computers improve adherence to guidelines? A (Palm) Pilot study of family doctors in British Columbia.
Bibliographic record
Abstract
OBJECTIVE: To examine whether Palm Prevention, a free software tool for Palm OS personal digital assistants (PDAs) that provides quick access to preventive guidelines in a patient-specific manner at the point of care, improved adherence to five preventive measures in primary care. DESIGN: Prospective intervention pilot study. SETTING: Vancouver, BC, and surrounding area. PARTICIPANTS: Eight general practitioners. INTERVENTIONS: Each physician used Palm Prevention for five preventive measures during routine preventive health visits with 10 patients (n = 80). Charts of consenting patients were reviewed for documentation of recommended maneuvers. MAIN OUTCOME MEASURES: Rates of adherence to five evidence-based guidelines selected from the Canadian and American task forces on preventive care and incorporated into Palm Prevention. RESULTS: Intervention and control physicians were similar in their familiarity with and use of PDAs, and they recruited similar patients for the study. Intervention and control groups had similar rates of screening for hypertension. Intervention improved adherence to the remaining four guidelines: cervical cancer screening increased 22% (only absolute increases are reported); hyperlipidemia screening increased 30%; colorectal cancer screening increased 27%; and prophylaxis with acetylsalicylic acid in high-risk patients increased 38%. Participants were surveyed after the study; all reported that they found the software helpful and would continue using Palm Prevention. Usage statistics showed that study participants used the tool outside the trial: users entered between 28 and 68 unique patients into the program during the 2-month intervention. CONCLUSION: This pilot study suggests PDAs are useful in improving preventive care and facilitating translation of knowledge into practice. This was particularly apparent with newer guidelines.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".