Telehealth-Delivered Program and Accompanying Patients to Enhance the Clinical Condition of Patients Throughout a Liver Transplant: Protocol for a Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: Liver transplantation (LT) is indicated in patients with severe acute or chronic liver failure for which no other therapy is available. With the increasing number of LTs in recent years, liver centers worldwide must manage their patients according to their clinical situation and the expected waiting time for transplantation. The LT clinic at the Centre hospitalier de l'Université de Montréal (CHUM) is developing a new health care model across the entire continuum of pre-, peri-, and posttransplant care that features patient monitoring by an interdisciplinary team, including an accompanying patient; a digital platform to host a clinical plan; a learning program; and data collection from connected objects. OBJECTIVE: This study aims to (1) evaluate the outcomes following the implementation of a patient platform with connected devices and an accompanying patient, (2) identify implementation barriers and facilitators, (3) describe service outcomes in terms of health outcomes and the rates and nature of contact with the accompanying patient, (4) describe patient outcomes, and (5) assess the intervention's cost-effectiveness. METHODS: Six types of participants will be included in the study: (1) patients who received transplants and reached 1 year after transplantation before September 2023 (historical cohort or control group), (2) patients who will receive an LT between December 2023 and November 2024 (prospective cohort/intervention group), (3) relatives of those patients, (4) accompanying patients who have received an LT and are interested in supporting patients who will receive an LT, (5) health care professionals, and (6) decision makers. To describe the study sample and collect data to achieve all the objectives, a series of validated questionnaires, accompanying patient logbooks, transcripts of interviews and focus groups, and clinical indicators will be collected throughout the study. RESULTS: In total, 5 (steering, education, clinical-technological, nurse prescription, and accompanying patient) working committees have been established for the study. Recruitment of patients is expected to start in November 2023. All questionnaires and technological platforms have been prepared, and the clinicians, stakeholders, and accompanying patient personnel have been recruited. CONCLUSIONS: The implementation of this model in the trajectory of LT recipients at the CHUM may allow for better monitoring and health of patients undergoing transplantation, ultimately reducing the average length of hospital stay and promoting better use of medical resources. In the event of positive results, this model could be transposed to all transplant units at the CHUM and across Quebec (potentially affecting 888 patients per year) but could also be applied more widely to the monitoring of patients with other chronic diseases. The lessons learned from this project will be shared with decision makers and will serve as a model for other initiatives involving accompanying patients, connected objects, or digital platforms. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/54440.
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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 | 0.043 | 0.026 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.051 | 0.008 |
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".