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Telehomecare Reduces ER Use and Hospitalizations at William Osler Health System

2015· article· en· W313289042 on OpenAlexaffabout
Sandra Mierdel, Kirk Owen

Bibliographic record

VenueStudies in health technology and informatics · 2015
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsWilliam Osler Health System
Fundersnot available
KeywordsEmergency departmentMedicineTelemedicineEmergency medicineTelehealthHealth careMedical emergencyNursing

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.655
Threshold uncertainty score0.520

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.092
GPT teacher head0.400
Teacher spread0.308 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations15
Published2015
Admission routes2
Has abstractyes

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