Lessons from billed telepsychiatry in Australia during the COVID-19 pandemic: rapid adaptation to increase specialist psychiatric care
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVE: To summarise and comment upon research regarding the service delivery impact of the introduction of COVID-19 pandemic Medicare Benefits Schedule (MBS) psychiatrist telehealth services in Australia in 2020-2021. Type of program or service: Privately-billed, MBS-reimbursed, face-to-face and telehealth consultations with a specialist psychiatrist during the first year of the COVID-19 pandemic. METHODS: This paper draws on analyses of previously published papers. MBS-item-consultation data were extracted for video, telephone and face-to-face consultations with a psychiatrist for April-September 2020 in Victoria, and compared to face-to-face consultations in the same period of 2019 and for all of Australia. We also extracted MBS-item-consultation data for all of Australia from April 2020-April 2021, and compared this to face-to-face consultations for April 2018-April 2019. RESULTS: Although face-to-face consultations with psychiatrists waned following nationwide lockdowns, the introduction of MBS billing items for video and telephone telehealth meant that overall consultations were 13% higher in April 2020-April 2021, compared to the pre-pandemic year prior. A lockdown restricted to Victoria was associated with a 19% increase in consultations from April-September 2020, compared to the corresponding period in 2019. LESSONS LEARNT: Telehealth has been an integral component of Australia's relatively successful mental health response to COVID-19. The public availability of MBS data makes it possible to accurately assess change in psychiatric practice. The Australian Federal Government subsidises MBS telepsychiatry care by a patient rebate per consultation, illustrating that government-subsidised services can rapidly provide additional care. Rapid and substantial provision of telepsychiatry in Australia indicates that it may be a useful substitute or adjunct to face-to-face care during future pandemics and natural disasters.
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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 | 0.016 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it