Patient Experience of Virtual Hospital Care Provided by a Multidisciplinary Team: Protocol for a Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: Virtual hospitals are defined as the delivery of hospital-level health care via digital services such as videoconferencing technologies, digital platforms, and remote monitoring. The purpose is to leverage virtual technologies to deliver accessible patient-centered care, improve the overall health of communities, and implement improvements driven by real-time access to patient data. Patients and their caregivers have increasingly favored these alternative and complementary service delivery models alongside traditional in-person care. However, this is still a complex issue, and the current literature points to a variety of challenges that need to be overcome to provide optimal models of care. OBJECTIVE: This study aims to identify unique challenges to virtual hospitals providing multidisciplinary care for patients at home, and co-design recommendations to improve the patient experience. METHODS: This research is a mixed methods exploratory case study of a virtual hospital in Sydney, New South Wales, Australia. The methods include (1) document analysis: this will be used to identify the current formal processes that govern the virtual hospital; (2) secondary analysis of data: a detailed investigation of existing data collected by the virtual hospital, for example, patient-reported experience measures, patient-reported outcome measures, and other available data; (3) observations of current practices; (4) semistructured interviews; (5) co-designed focus groups; (6) economic analysis; (7) comparative case study; and finally, (8) a triangulation analysis. To synthesize these findings within a unified analytical framework, all data will be subjected to open or axial coding following the Strauss framework, allowing the convergence of themes across codes and methods. RESULTS: As of April 2025, observations and initial documentation analysis have begun. Multidisciplinary team meetings and clinician shadowing have commenced, along with analysis of Standards of Practice documents. CONCLUSIONS: This protocol outlines a mixed methods case study on a new virtual hospital located in Sydney, New South Wales, Australia. We anticipate our results to provide a comprehensive understanding of patient experience through a range of quantitative and qualitative research activities. We will achieve this by identifying challenges for patients, carers, and health care workers, and documenting informal solutions discovered through our research activities. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/72729.
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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 | 0.089 | 0.062 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.042 | 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".