Impact of a Novel Multi-Specialist Telemedicine Consultation Program Model of Care for Homebound Older Adults
Bibliographic record
Abstract
BACKGROUND: In 2015, a centralised Multi-Specialist Telemedicine (TM) Consultation Program was established to improve access to specialist care and enhance continuity of care for homebound older adults in Toronto, Canada. Community-dwelling patients were referred to the program by their primary care providers (PCP), treating specialists, and inpatient physicians for specialist-led post hospital discharge follow-up care. A clinical nurse specialist (CNS) thereafter collaborated with hospital-based consulting specialists, utilizing videoconferencing technology to facilitate consultations and follow-up visits for homebound patients METHODS: We conducted a retrospective observational study of the overall intervention including patient characteristics and the number/type of consultations provided by analyzing the clinical charts of each enrolled patient. Satisfaction surveys were conducted after five years of program implementation with patients, family caregivers and healthcare providers utilizing paper-based questionnaires administered by members of our research team. Data were analyzed using summary statistics. RESULTS: From April 2015 to March 2020, this program supported a total of 216 homebound patients, with an average age of 84 years, and an average Charlson Comorbidity Index (CCI) of 3.36. Patients received a total of 1,003 consultation and follow-up visits from 42 specialist care providers representing 22 unique clinical specialties. 59 (27%) patients and family caregivers and 22 specialist and primary care providers voluntarily completed satisfaction surveys. 100% of surveyed patients and caregivers reported being very satisfied with the program and 86% of physicians reported that the program enhanced their delivery of patient care. CONCLUSIONS: Our TM program had a high participation rate and showed promising results in improving the delivery of patient care by centrally facilitating multi-specialist consultations and maintaining continuity of care for homebound older adults in an urban setting, which may have potentially reduced future ED visits. The program also received high satisfaction rates among providers, patients, and caregivers, indicating a positive response to its implementation.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".