Addressing Safety, Quality, and Cost of Care Through a Telehealth Outpatient Transitional Care Model: Protocol for a Pragmatic Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: People with multimorbidity have complex health care needs, resulting in high health service use, hospital readmission rates, and support needs. To prevent unnecessary hospital readmissions, effective coordination during the transition from hospital to primary care is essential; the transitional care model (TCM) is an effective approach to achieve this. This study will adapt the TCM, focusing on a nurse-led telehealth-based follow-up transition coordination service to enhance continuity between hospital and primary care, aiming to reduce unnecessary hospital readmissions and improve patient transitions. OBJECTIVE: This study aims to assess the impact of a TCM on 3-month readmission rates in people with multimorbidity after discharge in an Australian context. Other objectives include evaluating the rate of re-presentation to hospital and overall length of hospital stay within 1, 6, and 12 months of discharge from the index admission; conducting a cost analysis of the transitional service model of care; evaluating the patient experience with the transition service; assessing patients' symptom burden before and after transitional support service intervention; and evaluating patients' quality of life, self-efficacy, and symptom management before and after intervention. METHODS: The study design is a multicenter, pragmatic randomized controlled trial of patients with multimorbidity; therefore, real-world clinical practices, and operations will be the considerations within the research design elements. A mixed methods approach using quantitative and qualitative data collection methods will be used. The study setting incorporates 2 hospitals, initially commencing at the Queen Elizabeth Hospital (a 355-bed acute and subacute teaching hospital) and then at the Royal Adelaide Hospital (an 880-bed acute care teaching hospital), both located within the Central Adelaide Local Health Network, South Australia. We will include 3 to 6 medical units and wards. The intervention will focus on nurse-led transition assessment and care planning and telehealth transition coordination support for people with multimorbidity for 6 to 10 weeks following hospital discharge. RESULTS: This project received ethics approval (17554) on June 29, 2023, and was registered with the Australian New Zealand Clinical Trials Registry on February 15, 2024 (12624000142538). The study commenced on July 1, 2023; data collection started in February 2024 and was completed on March 31, 2025. Finalized results are expected in March 2026. CONCLUSIONS: The Central Adelaide Local Health Network currently lacks a process to assess or manage readmission risks for people with multimorbidity, despite evidence linking transitional care to reduced rehospitalizations. Our feasibility study highlighted the effectiveness of a transition coordinator role in supporting patients' return to home and community. Progressing this work, an adapted TCM, with telehealth-based follow-up and home and health care support, will enhance continuity between hospital and primary care, aiming to reduce unnecessary readmissions and improve patient transitions. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry (ANZCTR) ACTRN12624000142538; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=383721. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/71847.
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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 | 0.063 | 0.054 |
| Meta-epidemiology (narrow) | 0.008 | 0.004 |
| Meta-epidemiology (broad) | 0.015 | 0.008 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.071 | 0.010 |
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".