Comparison of Healthcare Utilization Between Telemedicine and Standard Care: A Propensity-Score Matched Cohort Study Among Individuals With Chronic Psychotic Disorders in Ontario, Canada
Bibliographic record
Abstract
Background: Telemedicine adoption has grown significantly due to the coronavirus of 2019 pandemic; however, it remains unclear what the impact of widespread telemedicine use is on healthcare utilization among individuals with psychosis. Objectives: To investigate the impact of telemedicine use on changes in healthcare utilization among patients with chronic psychotic disorders (CPDs). Study Design: We conducted a population-based, retrospective propensity-matched cohort study using healthcare administrative data in Ontario, Canada. Patients were included if they had at least one ambulatory visit between March 14, 2020 and September 30, 2020 and a CPD diagnosis any time before March 14, 2020. Telemedicine users (2+ virtual visits after March 14, 2020) were propensity score-matched 1:1 with standard care users (minimum of 1 in-person or virtual ambulatory visit and maximum of 1 virtual visit after March 14, 2020) based on several baseline characteristics. Monthly use of various healthcare services was compared between the two groups from 12 months before to 3 months after their index in-person or virtual ambulatory visit after March 14, 2020 using generalized estimating equations (eg, hospitalizations, emergency department [ED] visits, and outpatient physician visits). The slope of change over the study period (ie, rate ratio) as well as a ratio of slopes, were calculated for both telemedicine and standard care groups for each outcome. Study Results: A total of 18 333 pairs of telemedicine and standard care patients were identified after matching (60.8% male, mean [SD] age 45.4 [16.3] years). There was a significantly greater decline across time in the telemedicine group compared to the standard care group for ED visits due to any psychiatric conditions (ratio of slopes for telemedicine vs standard care (95% CI), 0.98 (0.98 to 0.99)). However, declines in primary care visit rates (ratio of slopes for telemedicine vs standard care (1.01 (1.01 to 1.02)), mental health outpatient visits with primary care (1.03 (1.03 to 1.04)), and all-cause outpatient visits with primary care (1.01 (1.01 to 1.02)), were steeper among the standard care group than telemedicine group. Conclusions: Overall, patients with CPDs appeared to benefit from telemedicine as evidenced by increased outpatient healthcare utilization and reductions in ED visits due to psychiatric conditions. This suggests that telemedicine may have allowed this patient group to have better access and continuity of care during the initial waves of the pandemic.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".