Comparison of the out-of-pocket costs of Medicare-funded telepsychiatry and face-to-face consultations: A descriptive study
Bibliographic record
Abstract
OBJECTIVE: Telepsychiatry items in the Australian Medicare Benefits Schedule (MBS) were expanded following the COVID-19 pandemic. However, their out-of-pocket costs have not been examined. We describe and compare patient out-of-pocket payments for face-to-face and telepsychiatry (videoconferencing and telephone) MBS items for outpatient psychiatric services to understand the differential out-of-pocket cost burden for patients across these modalities. METHODS: out-of-pocket cost information was obtained from the Medical Costs Finder website, which extracted data from Services Australia's Medicare claims data in 2021-2022. Cost information for corresponding face-to-face, video, and telephone MBS items for outpatient psychiatric services was compared, including (1) Median specialist fees; (2) Median out-of-pocket payments; (3) Medicare reimbursement amounts; and (4) Proportions of patients subject to out-of-pocket fees. RESULTS: Medicare reimbursements are identical for all comparable face-to-face and telepsychiatry items. Specialist fees for comparable items varied across face-to-face to telehealth options, with resulting differences in out-of-pocket costs. For video items, higher proportions of patients were not bulk-billed, with greater out-of-pocket costs than face-to-face items. However, the opposite was true for telephone items compared with face-to-face items. CONCLUSIONS: Initial cost analyses of MBS telepsychiatry items indicate that telephone consultations incur the lowest out-of-pocket costs, followed by face-to-face and video consultations.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".