Changes in outpatient care patterns and subsequent outcomes during the COVID-19 pandemic: A retrospective cohort analysis from a single payer healthcare system
Bibliographic record
Abstract
ABSTRACT Background There have been rapid shifts in outpatient care models during the COVID-19 pandemic but the impact of these changes on patient outcomes are uncertain. We designed this study to examine ambulatory outpatient visit patterns and outcomes between March 1, 2019 to February 29, 2020 (pre-pandemic) and from March 1, 2020 to February 28, 2021 (pandemic). Methods We conducted a population-based retrospective cohort study of all 3.8 million adults in the Canadian province of Alberta, which has a single payer healthcare system, using linked administrative data. We examined all outpatient physician encounters (virtual or in-person) and outcomes (emergency department visits, hospitalizations, or deaths) in the next 30- and 90-days. Results Although in-person outpatient visits declined by 38.9% in the year after March 1, 2020 (10,142,184 vs. 16,592,599), the increase in virtual visits (7,152,147; 41.4% of total) meant that total outpatient encounters increased by 4.1% in the first year of the pandemic. Outpatient care and prescribing patterns remained stable for adults with ambulatory-care sensitive conditions (ACSC): 97.2% saw a primary care physician (median 6 visits), 59.0% had at least one specialist visit, and 98.5% were prescribed medications (median 9) in the year prior to the pandemic compared to 96.6% (median 3 in-person and 2 virtual visits), 62.6%, and 98.6% (median 8 medications) during the first year of the pandemic. In the first year of the pandemic, virtual outpatient visits were associated with less subsequent healthcare encounters than in-person ambulatory visits, particularly for patients with ACSC (9.2% vs. 10.4%, aOR 0.89 [95% confidence interval 0.87-0.92] at 30 days and 26.9% vs. 29.3%, aOR 0.93 [0.92-0.95] at 90 days). Conclusions The shifts in outpatient care patterns caused by the COVID-19 pandemic did not disrupt prescribing or follow-up for patients with ACSC and did not worsen post-visit outcomes. Funding None Registration None KEY MESSAGES What is already known on this topic There have been rapid shifts in outpatient care models during the COVID-19 pandemic but outcomes are uncertain. What this study adds Total outpatient encounters increased by 4% in the first year of the pandemic due to a rapid increase in virtual visits (which made up 41% of all outpatient encounters). Prescribing patterns and frequency of follow-up were similar in the first year after onset of the pandemic in adults with ambulatory-care sensitive conditions. Compared to in-person visits, virtual outpatient visits were associated with less subsequent healthcare encounters, particularly for patients with ambulatory-care sensitive conditions (11% less at 30 days and 7% less at 90 days). How this study might affect research, practice or policy Our data provides reassurance that the shifts in outpatient care patterns caused by the COVID-19 pandemic did not negatively impact follow-up, prescribing, or outcomes for patients with ACSC. Further research is needed to define which patients and which conditions are most suitable for virtual outpatient visits and, as with all outpatient care, the optimal frequency of such visits.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".