Telehomecare Reduces ER Use and Hospitalizations at William Osler Health System
Bibliographic record
Abstract
The Ontario Telemedicine Network's Telehomecare initiative brings together specially trained clinicians and technology to coach patients with COPD and/or heart failure to monitor vital signs and manage their health at home. The objective of the study was to evaluate pre- and post-enrollment and post discharge data captured by Telehomecare host William Osler Health System (WOHS). Results demonstrate a 46% reduction in emergency department use and a 53% reduction in hospitalizations post-enrollment compared to pre-enrollment. Average length of stay (LOS) dropped by 25% of a day compared to pre-enrollment. In addition, six months after Telehomecare discharge, inpatient admissions and emergency department visits continued to decline, by 65% and 57% respectively, compared to pre-enrollment. While average LOS increased between pre-enrollment and post-discharge, the reduction in acute inpatient episodes created a net reduction in accumulated inpatient days of 563.16 days (63% reduction). The WOHS Telehomecare results strongly support the positive influence of the program on health system utilization and the development of effective long-term self-management skills. Next steps could include reviewing, more closely, the reasons for hospital utilization and undertaking a cost-benefit analysis to support further expansion of the program to address other chronic illness and care needs.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".