Using Virtual Models of Care for Chronic Disease Management in Outpatient Services: A Systematic Review of Quality of Care Outcomes
Bibliographic record
Abstract
Background: The use of virtual care (VC) among individuals with chronic disease is well-documented, yet evidence on quality of care outcomes, such as frequency of subsequent hospitalizations, emergency department (ED) visits, and mortality, is fragmented. This systematic review aimed to synthesize evidence of quality of care outcomes, namely subsequent outpatient encounters, hospital admissions, ED visits, and mortality, associated with VC among outpatients with chronic diseases. Methods : A search strategy was developed and applied to six electronic databases (Embase, MEDLINE, the Cochrane Library, PsycINFO, Web of Science, and CINAHL) for articles published between January 1, 2013 and July 6, 2024. Eligible studies included synchronous VC (e.g., live, video, or audio based) between a patient and health care provider. A narrative synthesis compared VC with in-person care, considering types of outpatient care, specialty, VC components, follow-up duration, and outcomes. Results : After reviewing 5,679 abstracts, 24 articles were included. Studies were predominantly from the United States ( n = 11), followed by Australia ( n = 3) and Canada ( n = 2). The follow-up durations ranged from 2 weeks to 2 years, with 14 studies having follow-up durations of 6 months or less. Studies predominantly reported no difference or lower rates of hospital admissions ( n = 18/20), ED visits ( n = 11/12), and mortality ( n = 12/14) among outpatients who used VC compared with those who had in-person visits. Half of the studies ( n = 3/6) reported more subsequent outpatient encounters for patients using VC for the initial outpatient encounter compared with those who had in-person visits. Conclusion : The review indicated that outpatient VC is associated with fewer or no different volume of hospital admissions or ED visits among people with chronic conditions but may be associated with an increased number of outpatient follow-up visits. Robust research at scale that considers the volume of VC consumed and associations with outcomes over longer follow-up periods is required.
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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 | 0.017 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".