Connecting People With Multimorbidity to Interprofessional Teams Using Telemedicine
Bibliographic record
Abstract
PURPOSE: Most models for managing chronic disease focus on single diseases. Managing patients with multimorbidity is an increasing challenge in family medicine. We evaluated the feasibility of a novel approach to caring for patients with multimorbidity, performing a case study of TIP-Telemedicine IMPACT (Interprofessional Model of Practice for Aging and Complex Treatments) Plus-a 1-time interprofessional consultation with primary care physicians (PCPs) and their patients in Toronto, Canada. METHODS: We assessed feasibility of the TIP model from the number of referrals from PCPs and emergency departments in Toronto, Canada; the intervention cost; and the satisfaction of patients, PCPs, and team members with the new model. One patient and PCP story highlights the model's impact. We also performed thematic analysis of written feedback. RESULTS: A total of 76 patients were referred from 53 PCPs and 4 emergency departments, and 65 PCPs participated in TIP. All 74 patient survey respondents indicated TIP improved their access to interdisciplinary resources, and 97% reported feeling hopeful their conditions would improve as a result. Of 21 PCP survey respondents, 100% reported they would use TIP again, and 90% reported improved confidence in managing their patient's care. Of 87 team member survey respondents, 97% rated TIP as effective. Qualitative findings indicated benefits to both patients and health professionals. The cost was about 22% less than that of a 1-day hospital admission through the emergency department (C$854 vs C$1,088). CONCLUSIONS: TIP is a feasible intervention in multiple primary care settings that gives patients an active role in their health management, supported by their team. The model effectively addresses the needs of the most complex patients and their PCPs.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".