Virtual models of chronic disease management: lessons from the experiences of virtual care during the COVID-19 response
Bibliographic record
Abstract
Objective This study examined Gold Coast staff and patient experiences with the rapid expansion of a virtual model of chronic disease management during the COVID-19 pandemic. Methods The study undertook a survey of enrolled patients (n=24) and focus groups with clinical and administrative staff (n=44) delivering chronic disease programs at Gold Coast Health in Queensland. The study also examined routinely collected activity data for the chronic disease programs before COVID (January-February 2020) and for the first 3 months of the COVID-19 response (March-May 2020). Results Chronic disease programs continued to provide similar numbers of appointments over the COVID-19 response period, but there was a marked increase in the proportion of appointments that were delivered virtually, either by telephone or video conference. Most patients were satisfied with their virtual care experiences and felt that their health care needs were met. Conclusions The COVID-19 response provided an opportunity to learn and further develop models of virtual care. Staff and patients were generally supportive of continuing to include virtual appointments in the future. Ongoing concerns were predominantly around the support available to patients and staff to ensure they are trained and equipped to manage the technology and new mode of communicating. What is known about the topic? Emerging evidence suggests that virtual models of health care delivery, such as telephone and video consultations and remote patient monitoring, can be safe and cost-effective alternatives to traditional face-to-face chronic disease management programs. Virtual care is associated with equal or improved clinical outcomes, as well as efficiency improvements, such as reduced failure to attend rates. What does this paper add? The increasing burden of chronic disease across Australia, as well as the need to minimise the risk of vulnerable patient groups attending in-hospital appointments where it is safe and appropriate to do so, means that expanding the delivery of virtual chronic disease management will become increasingly necessary. The results of this study provide an opportunity to learn from a rapid rollout of virtual care for these staff and patient groups and will help inform advances in this area. What are the implications for practitioners? Existing evidence, demographic pressures and the COVID-19 pandemic response all point to virtual care as a viable and safe alternative to traditional models of chronic disease management. The lessons presented here provide more detailed guidance on the support that staff and patients require to ensure virtual care is a seamless and safe alternative or adjunct to traditional chronic disease management programs.
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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 | 0.013 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.011 | 0.014 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".