A cost analysis comparing telepsychiatry to in-person psychiatric outreach and patient travel reimbursement in Northern Ontario communities
Bibliographic record
Abstract
INTRODUCTION: Residents of Northern Ontario have limited access to local psychiatric care. To address this, three program models exist: (1) telepsychiatry; (2) psychiatrists traveling to underserved areas; and (3) reimbursing patients for travel to a psychiatrist. Evidence shows that telepsychiatry has comparable outcomes to in-person consultations. The objective of this study was to determine the cost difference between programs. METHODS: A cost-minimization analysis estimating cost per visit from a public healthcare payer economic costing perspective was conducted. Data on fixed and variable costs were obtained. Evidence-based assumptions were made where relevant. Base-case scenarios and a break-even analysis were completed, as well as deterministic and probabilistic sensitivity analyses, to explore the effects of parameter variability on program costs. RESULTS: Costs per visit were lowest in telepsychiatry (CAD$360) followed by traveling physicians (CAD$558) and patient reimbursement (CAD$620). Among the 100,000 Monte Carlo simulations, results showed telepsychiatry was the least costly program in 71.2% of the simulations, while the reimbursement and outreach programs were least costly in 15.1% and 13.7% of simulations, respectively. The break-even analysis found telepsychiatry was the least costly program after an annual patient visit threshold of approximately 76 visits (compared to traveling psychiatrists) and 126 visits (compared to reimbursed patients). DISCUSSION: Our analyses support telepsychiatry as the least costly program. These results have important implications for program planning, including the prioritization of telepsychiatry, increased integration of telepsychiatry with other modalities of outreach psychiatry, and limiting use of the patient remuneration program to where medically necessary, to reduce overall cost.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".