Patient’s Perceptions of a Centralized Virtual Ward for Remote Patient Monitoring in Primary Care: Qualitative Study
Bibliographic record
Abstract
Background: Remote patient monitoring (RPM) has the potential to reduce in-clinic visits and promote proactive and preventive care for patients with chronic diseases in primary care. However, a decentralized approach to RPM in a primary health care (PHC) setting has not met stakeholders' expectations regarding scalability. This study introduces a centralized virtual ward (CVW)-led RPM, utilizing a multidisciplinary team approach to monitor patients with chronic diseases by clinicians who do not belong to the patients' PHC center. Objective: This study aimed to gain a better understanding of patients' perceptions of CVW-led RPM for managing chronic diseases in a PHC setting. Methods: In-depth interviews were conducted with 22 patients with chronic diseases enrolled at a PHC center in Stockholm, Sweden. The RPM project ran between October 2018 and April 2019 and included a total of 395 patients. Interviews followed a semistructured interview guide and were analyzed using qualitative content analysis. Results: Primary care patients with chronic diseases expressed that their contact with the CVW felt impersonal but at the same time secure and accessible. They noted a lack of coordination and communication between the clinicians of the CVW and their PHC providers. Captured data resulted in 1 overarching theme "Sense of security and accessibility, but impersonal and uncoordinated" based on 5 categories: sense of security, care and self-care, accessibility, quality of care, and communication. Conclusions: Our findings suggest that by addressing patients' needs for new organizational routines for patient-caregiver communication, RPM via centralized virtual wards can better realize the potential of this technology.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".