Changes in General Practice use and costs with COVID-19 and telehealth initiatives
Bibliographic record
Abstract
Abstract Background In response to the COVID-19 pandemic, general practice (GP) in Australia underwent a rapid transition, including the rollout of population-wide telehealth, with uncertain impacts on GP use and costs. Objective To describe how use and costs of GP services in Australia changed in 2020—following the pandemic and introduction of telehealth—compared to 2019, and how this varied across population subgroups. Method Data for ∼19M individuals from Census 2016 were linked to Medicare data for 2019-2020 through the Multi-Agency Data Integration Project. We used regression models to compare age-sex-adjusted GP use and out-of-pocket cost (OPC) over time, overall and by sociodemographic characteristics. Results The number of people who visited a GP in Q2-Q4 of 2020 decreased by 4% compared to Q2-Q4 of 2019. The mean number of face-to-face GP services per quarter declined, while telehealth services increased, with overall use of GP services in Q4 2020 similar to or higher than Q4 2019. The proportion of total GP services by telehealth stabilised at ∼25% in Q4 2020. However, individuals aged 3-14 or ≥70 years and those with limited English proficiency used fewer GP services in 2020 compared to 2019, with a lower proportion by telehealth. Mean OPC-per-service was lower across all subgroups in 2020 compared to 2019. Discussion Introduction of widespread telehealth largely maintained use of GP services during the pandemic and minimised OPCs, but not for all population subgroups. This may indicate technological, social or other barriers in these populations, as well as pandemic-related changes in healthcare use. HOW THIS FITS IN In response to the COVID-19 pandemic, major telehealth initiatives were implemented to ensure access to primary healthcare while minimising disease transmission. Using routinely collected, whole-of-population data from Australia, we show that the introduction of telehealth during the pandemic largely maintained use of GP services while minimising costs. However, compared to pre-pandemic levels, GP use was lower among individuals aged 3-14 or ≥70 years and those not proficient in English, although these groups also saw the greatest reduction in out-of-pocket cost per service. As telehealth initiatives are integrated into standard GP care, it is vital to ensure telehealth is designed and funded to support these groups and the ongoing financial viability of practices.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".