Lessons from billed telepsychiatry in Australia during the COVID-19 pandemic: rapid adaptation to increase specialist psychiatric care
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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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 | 0.019 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".