A Virtual Ward for Home Hemodialysis Patients – A Pilot Trial
Bibliographic record
Abstract
BACKGROUND: Patients with end-stage renal disease (ESRD) have a high rate of hospitalization and are prone to care gaps that may occur during the transition from hospital to home. The virtual ward (VW) is an innovative model that provides short-term transitional care to patients upon hospital discharge. The VW may be an effective intervention to address care gaps. OBJECTIVES: The primary objective of the pilot study was to assess the feasibility and practicality of implementing the Home Dialysis VW (HDVW) on a broader scale. DESIGN: The HDVW Pilot Study enrolled home hemodialysis patients following one of four inclusion criteria: 1. Discharge from hospital, 2. Completion of an in-hospital medical procedure, 3. Prescription of an antibiotic, 4. Completion of home hemodialysis training. Patients were followed in the HDVW for 14 days and during this time were assessed serially with a clinician-led telephone interview for one of three transitional care gaps: 1. Requirement for change in hemodialysis prescription, 2. Requirement for coordination of follow-up care, 3. Requirement for medication change. SETTING: The study was conducted in Toronto, Ontario, Canada at a quaternary care academic teaching hospital from 2012-2013. PATIENTS: This study included 52 HDVW admissions among 35 patients selected from the existing home hemodialysis program. MEASUREMENTS: The primary outcome was the identification of the number of care gaps at each HDVW admission. Secondary outcomes included the identification of potential predictors of care gaps and description of clinical adverse events following HDVW admission (readmissions, emergency department visits, unplanned visits to the home hemodialysis in-center). RESULTS: The implementation and execution of the HDVW Pilot Study proved to be technically feasible and practical. A care gap was identified in 35 (67 %) of the HDVW admissions. In total, the cohort experienced 85 care gaps. There were no baseline demographic characteristics predictive of experiencing a care gap. In the total cohort observed for 2912 patient days, there were 9 readmissions, 13 visits to the emergency department, and 7 unplanned visits to the home hemodialysis in-center unit. LIMITATIONS: The results of this study are limited by the small study size and single-center experience. CONCLUSION: The implementation of a virtual ward for home hemodialysis patients is practical, feasible and identifies many care gaps which have the potential to result in subsequent adverse events. A larger, multi-center prospective clinical trial is justified to identify if the HDVW can prevent adverse events among home dialysis patients.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".