Healthcare utilization and telemedicine: An evaluation using linked administrative data from Manitoba
Bibliographic record
Abstract
Introduction This research examines utilization patterns for patients using telemedicine compared to those receiving treatment conventionally. Administrative data from 2007–2016 is linked to records from the Manitoba Telehealth programme to conduct a population-level study. Methods Using a high dimensional propensity-weighted regression model, the study compares four utilization measures between telemedicine users and non-users. These include: ambulatory visits (number of visits to any physician), visits to primary care physicians, visits to specialists and the number of hospitalizations. Results Compared to non-users, telemedicine patients showed a higher number of in-person visits. Specifically, using incidence-rate ratios, telemedicine users had 1.32 more ambulatory visits ( p < 0.001; 95% confidence interval, 1.23–1.41), 1.26 more visits to primary care practitioners ( p<0.001; 95% confidence interval, 1.17–1.35), 1.38 more visits to specialists ( p < 0.001; 95% confidence interval, 1.19–1.61) and 1.14 more hospitalizations ( p>0.1; 95% confidence interval, 0.91–1.43). These results were robust to restricted analyses for distance, regions, and chronic conditions. Those patients with frequent virtual encounters with a specialist showed a decrease of the frequency of in-person visits of the same type of care (incidence-rate ratio=0.48, p < 0.001; 95% confidence interval, 0.44–0.54). Discussion Patterns in utilization vary between telemedicine users and non-users, with users showing a higher number of in-person visits than non-users, and only frequent users of telemedicine showing reduced in-person visits. Future investigation linking utilization patterns with patient care outcomes and costs will inform healthcare policy and clinical treatment plans.
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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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".