Effect of a multimorbidity intervention on health care utilization and costs in Ontario: randomized controlled trial and propensity-matched analyses
Bibliographic record
Abstract
BACKGROUND: Patients with multimorbidity require coordinated and patient-centred care. Telemedicine IMPACT Plus provides such care for complex patients in Toronto, Ontario. We conducted a randomized controlled trial (RCT) comparing health care utilization and costs at 1-year postintervention for an intervention group and 2 control groups (RCT and propensity matched). METHODS: Data for 82 RCT intervention and 74 RCT control participants were linked with health administrative data. We created a second control group using health administrative data-derived propensity scores to match (1:5) intervention participants with comparators. We evaluated 5 outcomes: acute hospital admissions, emergency department visits, costs of all insured health care, 30-day hospital readmissions and 7-day family physician follow-up after hospital discharge using generalized linear models for RCT controls and generalized estimating equations for propensity-matched controls. RESULTS: There were no significant differences between intervention participants and either control group. For hospital admissions, emergency department visits, costs and readmissions, the relative differences ranged from 1.00 (95% confidence interval [CI] 0.39-2.60) to 1.67 (95% CI 0.82-3.38) with intervention costs at about Can$20 000, RCT controls costs at around Can$15 000 and propensity controls costs at around Can$17 000. There was a higher rate of follow-up with a family physician for the intervention participants compared with the RCT controls (53.13 v. 21.43 per 100 hospital discharges; relative difference 2.48 [95% CI 0.98-6.29]) and propensity-matched controls (49.94 v. 28.21 per 100 hospital discharges; relative difference 1.81 [95% CI 0.99-3.30]). INTERPRETATION: Despite a complex patient-centred intervention, there was no significant improvement in health care utilization or cost. Future research requires larger sample sizes and should include outcomes important to patients and the health care system, and longer follow-up periods. ONTARIO: ClinicalTrials.gov : 104191.
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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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.008 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| 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".