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Record W4321384850 · doi:10.2196/44006

A Medicoeconomic Evaluation of a Telehealth Platform for Elective Outpatient Surgeries: Protocol for a Randomized Controlled Trial

2023· article· en· W4321384850 on OpenAlexaffvenueabout
Florian Robin, Maxim Roy, Alexandre Kuftedjian, Laurelie Perret, Frédéric Lavoie, Alexandre Castonguay, Marie‐Pascale Pomey, Cedrick Zaouter, Guy Paré

Bibliographic record

VenueJMIR Research Protocols · 2023
Typearticle
Languageen
FieldMedicine
TopicNausea and vomiting management
Canadian institutionsHEC MontréalUniversité de MontréalCentre Hospitalier de l’Université de Montréal
Fundersnot available
KeywordsTelecareMedicineRandomized controlled trialTelehealthPerioperativeTelemedicineProtocol (science)AmbulatoryOutpatient surgeryMedical emergencyPhysical therapyHealth careSurgeryAlternative medicine

Abstract

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BACKGROUND: The number of elective outpatient surgeries in Canada has increased markedly in the last 10 years. However, unanticipated cancellations on the day of surgery and adverse postoperative events are frequent. Modern technologies have been shown to be of great help in the medical field in improving patient care. Thus, it is likely that dedicated technologies could also significantly improve surgical outpatients' pathways. Therefore, the department of anesthesiology at the University of Montreal Medical Center, in collaboration with LeoMed, a telemedicine platform, has developed a telehealth solution to offer more efficient perioperative support and follow-up for patients undergoing ambulatory surgery. OBJECTIVE: The objective is to evaluate the medicoeconomic benefit of a dedicated perioperative telehealth platform for patients undergoing day surgery. Our hypothesis is that this dedicated telecare solution will allow more efficient patient care, which will reduce all types of medical costs related to day surgery pathways. METHODS: This study is a single-center, single-blinded, 2-group randomized controlled trial. One thousand patients aged over 18 years with internet access who are scheduled to undergo ambulatory surgery will be enrolled and randomized either to follow a perioperative path that includes a patient-tailored perioperative digital app via the LeoMed telecare platform for 1 month or to follow the standard of care, which does not offer personalized digital support. The primary outcome will be to evaluate the cost-effectiveness of the telecare platform, assessing direct costs from factors such as unanticipated cancellations on the day of surgery due to preoperative instructions not being followed, calls to the local health information line, calls to the provincial health information line, emergency department consultations, unplanned readmissions, or medical visits for problems related to the surgical procedure within the first 30 days after the intervention. The secondary outcome will be to evaluate cost utility using a questionnaire assessing quality-adjusted life years. A blinded independent research team will analyze outcomes. All data will be analyzed according to the intention-to-treat principle. A sample size of 500 subjects in each group was calculated to detect a 21% reduction in postoperative complications with a power of 90%. This study has been approved by the ethics board of Centre hospitalier de l'Université de Montréal (University of Montreal Health Centre). No employee of LeoMed was involved in the study conception, and none will be involved in either data collection or analysis. RESULTS: Results of this trial will be useful to determine the economic benefit of a telecare platform specifically developed for surgical outpatient pathways. CONCLUSIONS: We believe that the deployment of a dedicated perioperative telehealth app will lead to better patient care and fewer postoperative complications, which will lower all types of costs related to surgical outpatient care. TRIAL REGISTRATION: ClinicalTrials.gov NCT04948632; https://ClinicalTrials.gov/ct2/show/NCT04948632. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/44006.

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)0.044
metaresearch head score (Gemma)0.013
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.033
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0440.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
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 head0.412
GPT teacher head0.616
Teacher spread0.204 · 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; both teacher heads agree on what is shown here.

Study designRandomized trial
Domainnot available
GenreProtocol

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

Citations4
Published2023
Admission routes3
Has abstractyes

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