Can computer-generated evidence-based care suggestions enhance evidence-based management of asthma and chronic obstructive pulmonary disease? A randomized, controlled trial.
Bibliographic record
Abstract
OBJECTIVE: Translation of evidence-based guidelines into clinical practice has been inconsistent. We performed a randomized, controlled trial of guideline-based care suggestions delivered to physicians when writing orders on computer workstations. STUDY SETTING: Inner-city academic general internal medicine practice. STUDY DESIGN: Randomized, controlled trial of 246 physicians (25 percent faculty general internists, 75 percent internal medicine residents) and 20 outpatient pharmacists. We enrolled 706 of their primary care patients with asthma or chronic obstructive pulmonary disease. Care suggestions concerning drugs and monitoring were delivered to a random half of the physicians and pharmacists when writing orders or filling prescriptions using computer workstations. A 2 x 2 factorial randomization of practice sessions and pharmacists resulted in four groups of patients: physician intervention, pharmacist intervention, both interventions, and controls. DATA EXTRACTION/COLLECTION METHODS: Adherence to the guidelines and clinical activity was assessed using patients' electronic medical records. Health-related quality of life, medication adherence, and satisfaction with care were assessed using telephone questionnaires. PRINCIPAL FINDINGS: During their year in the study, patients made an average of five scheduled primary care visits. There were no differences between groups in adherence to the care suggestions, generic or condition-specific quality of life, satisfaction with physicians or pharmacists, medication compliance, emergency department visits, or hospitalizations. Physicians receiving the intervention had significantly higher total health care costs. Physician attitudes toward guidelines were mixed. CONCLUSIONS: Care suggestions shown to physicians and pharmacists on computer workstations had no effect on the delivery or outcomes of care for patients with reactive airways disease.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 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".