MétaCan
Menu
Back to cohort
Record W2469503922 · doi:10.3233/978-1-61499-505-0-23

Scaling Up the Use of Remote Patient Monitoring in Canada

2015· article· en· W2469503922 on OpenAlexaffabout
Bobby Gheorghiu, Fraser Ratchford

Bibliographic record

VenueStudies in health technology and informatics · 2015
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsCanada Health Infoway
Fundersnot available
KeywordsImplementationCOPDPulmonary diseaseMedical emergencyMedicineHeart failureEmergency departmentHealthcare systemInvestment (military)BusinessHealth careIntensive care medicineNursingComputer sciencePolitical scienceCardiology

Abstract

fetched live from OpenAlex

Evidence supporting the use of remote patient monitoring (RPM) as a cost-effective means of keeping patients from being re-admitted to hospitals or making repeated emergency department visits is growing, especially for the treatment of chronic obstructive pulmonary disease (COPD) and congestive heart failure (CHF). A recent study funded by Canada Health Infoway, titled Connecting Patients with Providers: A Pan-Canadian Study on Remote Patient Monitoring, aimed to assess the current state of RPM solutions; to examine the evidence for patient and health system benefits achieved both in Canada and internationally; and to determine the critical success factors needed to support further investment and scaling-up of RPM solutions across the Canadian health care system. Break-even analysis of four different implementations reviewed in this study demonstrated that RPM programs can be viable and sustainable for large and small jurisdictions; however, more evidence is needed with regards to a number of potential applications for RPM beyond the management of COPD and CHF.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.919
Threshold uncertainty score0.588

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.022
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.004
Science and technology studies0.0040.001
Scholarly communication0.0030.001
Open science0.0020.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.216
GPT teacher head0.455
Teacher spread0.239 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations10
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueStudies in health technology and informaticsSame topicMobile Health and mHealth ApplicationsFrench-language works237,207