A Telehealth and Telepsychiatry Economic Cost Analysis Framework: Scoping Review
Bibliographic record
Abstract
Introduction: Despite a good evidence base for telepsychiatry (TP), economic cost analyses are infrequent and vary in quality. Methods: A scoping review was conducted based on the research question, “From the perspective of an economic cost analysis for telehealth and telepsychiatry, what are the most meaningful ways to ensure a study/intervention improved clinical care, provided value to participants, had population level impact, and is sustainable?” The search in seven databases focused on keywords in four concept areas: (1) economic cost analysis, (2) evaluation, (3) telehealth and telepsychiatry, and (4) quantifiable health status outcomes. The authors reviewed the full-text articles based on the inclusion (Medical Subject Headings [MeSH] of the keywords) and exclusion criteria. Results: Of a total of 2,585 potential references, a total of 99 articles met the inclusion criteria. The evaluation of telehealth and TP has focused on access, quality, patient outcomes, feasibility, effectiveness, outcomes, and cost. Cost-effectiveness, cost–benefit, and other analytic models are more common with telehealth than TP studies, and these studies show favorable clinical, quality of life, and economic impact. A standard framework for economic cost analysis should include: an economist for planning, implementation, and evaluation; a tool kit or guideline; comprehensive analysis (e.g., cost-effectiveness or cost–benefit) with an incremental cost-effectiveness ratio; measures for health, quality of life, and utility outcomes for populations; methods to convert outcomes into economic benefits (e.g., monetary, quality of adjusted life year); broad perspective (e.g., societal perspective); sensitivity analysis for uncertainty in modeling; and adjustments for differential timing (e.g., discounting and future costs). Conclusions: Technology assessment and economic cost analysis—such as effectiveness and implementation science approaches—contribute to clinical, training, research, and other organizational missions. More research is needed with a framework that enables comparisons across studies and meta-analyses.
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How this classification was reachedexpand
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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".