Outpatient visit trends for internal medicine ambulatory care sensitive conditions after the COVID-19 pandemic: a time-series analysis
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic led to a dramatic shift in the delivery of outpatient medicine with reduced in-person visits and a transition to predominantly virtual visits. We sought to understand trends in visit patterns for ambulatory care sensitive conditions (ACSCs) commonly seen in internal medicine clinics. METHODS: We included adult outpatients seen for an ACSC between March 15th, 2017 and March 14th, 2021 at a single-centre in Ontario, Canada. Monthly visits were assessed by visit type (new consultation, follow-up), diagnosis, and clinic. Time series analyses compared visit volumes pre- and post-pandemic. Proportion of virtual visits were compared before and during the pandemic. Patient and visit factors were compared between in-person and virtual visits. RESULTS: 8274 patients with 34,021 visits were included. Monthly visits increased by 15% during the pandemic (p < 0.0001). New consultations decreased by 10% (p = 0.0053) but follow-up visits increased by 21% (p < 0.0001). Monthly heart failure visits increased by 43% (p < 0.0001) whereas atrial fibrillation visits decreased. Pre- pandemic, < 1% of visits were virtual compared to 82% during the pandemic (p < 0.0001). Less than half of heart failure visits were virtual whereas > 95% of diabetes visits were virtual. CONCLUSIONS: We found a significant increase in overall visits to internal medicine clinics driven by increased volumes of follow-up visits, which more than offset decreased new consultations. There was variability in visit trends and uptake of virtual care by visit diagnosis, which may indicate challenges with delivery of virtual care for certain conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".