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 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 | 0.057 | 0.160 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.041 | 0.040 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.008 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".