Improving safety and efficiency in care: multi-stakeholders’ perceptions associated with a peritoneal dialysis virtual care solution
Bibliographic record
Abstract
Lianne Jeffs,1–3 Trevor Jamieson,4,5 Marianne Saragosa,5 Geetha Mukerji,3,5,6 Arsh K Jain,7 Rachel Man,7 Laura Desveaux,3,5 James Shaw,3,5 Payal Agarwal,5,8 Jennifer M Hensel,5,9 Maria Maione,10 Megan Nguyen,5 Nike Onabajo,5 R Sacha Bhatia3,5,11 1Sinai Health System, Toronto, ON M5G 1X5, Canada; 2Li Ka Shing Knowledge Institute, St Michael’s Hospital, Toronto, ON M5G 1WB, Canada; 3Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON M5S 1B2, Canada; 4Department of Medicine, University of Toronto, Division of General Internal Medicine, St Michael’s Hospital, Women’s College Hospital, Toronto, ON M5G 1WB, Canada; 5Institute for Health System Solutions and Virtual Care (WIHV), Women’s College Hospital, Toronto, ON M5S 1B2, Canada; 6Women’s College Hospital, Toronto, ON M5S 1B2, Canada; 7London Health Sciences Centre, London, ON N6A 5W9, Canada; 8Department of Family and Community Medicine, Women’s College Hospital, University of Toronto, Toronto, ON M5S 1B2, Canada; 9Department of Psychiatry, University of Toronto, Women’s College Hospital, Women’s College Research Institute, Women’s College Hospital, Toronto, ON M5S 1B2, Canada; 10St Michael’s Hospital, Toronto, ON M5G 1WB, Canada; 11University Health Network, Toronto, ON M5S 1B2, Canada Background: Although there is a growing body of literature on the outcomes and impacts of remote home management with peritoneal dialysis (PD) patients, less is understood how this virtual care solution impacts the quality and efficiency of the healthcare system care. In this context, a study was undertaken to understand the perceptions of patients and their caregivers, healthcare providers, health system decision makers, and vendors associated with a remote monitoring and tracking solution aimed at enhancing the outcomes and experiences of chronic kidney disease (CKD) patients receiving PD at home. Methods: A qualitative design using semi-structured interviews with 25 stakeholders was used in this study. Narrative data were analyzed by a thematic analysis approach. Results: The following two themes emerged from the data: (1) leveraging data to monitor and intervene to keep patients safe and (2) increasing efficiencies and having control over supplies. Discussion: Our study findings elucidated the ability of patients (and in some cases, caregivers) to monitor and trend their data and order and track directly on-line their dialysis supplies were key to their active participation in managing their CKD and keeping them safe at home. Their active participation and functionality of the virtual care solution also led to enhanced efficiencies (eg, process faster, easier, convenient, time savings) for both patients and healthcare providers. Conclusion: The virtual care solution showed promising signs of a patient-centric approach and may serve as a blueprint for other virtual care solutions for chronic disease management. Keywords: peritoneal dialysis, chronic kidney disease, virtual care, qualitative research, patient centric care
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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 | 0.019 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".