Cost-effectiveness of Anticipatory and Preventive multidisciplinary Team Care for complex patients
Bibliographic record
Abstract
OBJECTIVE T o evaluate the cost-effectiveness of Anticipatory and Preventive Team Care (APTCare). DESIGN Analysis of data dra wn from a randomized controlled trial. SETTING A family health network in a rural area near Otta wa, Ont. PARTICIPANTS Patients 50 years of age or older at risk of experiencing adverse health outcomes. Analysis of cost-effectiveness was performed for a subsample of participants with at least 1 of the chronic diseases used in the quality of care (QOC) measure (74 intervention and 78 control patients). INTERVENTIONS At-risk patients were randomly assigned to receive usual care from their family physicians or APTCare from a collaborative team. MAIN OUTCOME MEASURES Cost-effectiveness and the net benefit to society of the APTCare intervention. RESULTS Costs not directly associated with delivery of the intervention were similar in the 2 arms: $9121 and $9222 for the APTCare and control arms, respectively. Costs directly associated with the program were $3802 per patient for a total cost per patient of $12 923 and $9222, respectively ( P = .033). A 1% impro vement in QOC was estimated to cost $407 per patient. Analysis of the net benefit to society in absolute dollars found a breakeven threshold of $750 when statistical significance was required. This implies that society must place a value of at least $750 on a 1% improvement in QOC in order for the intervention to be socially worthwhile. By any of the metrics used, the APTCare intervention was not cost-effective, at least not in a population for which baseline QOC was high. CONCLUSION Although our calculations suggest that the APTCare intervention was not cost-effective , our results need the following caveats. The costs of such a newly introduced intervention are bound to be higher than those for an established, up-and-running program. Furthermore, it is possible that some benefits of the secondary preventive measures were not captured in this limited 12- to 18-month study or were simply not measured.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".