Telemedicine Public Reimbursement Models for National and Subnational Jurisdictions: Scoping Review
Bibliographic record
Abstract
Background: Telemedicine has transformed health care delivery, offering improved access, efficiency, and potentially cost-effectiveness. However, wide-scale implementation is challenged due to multiple factors. Among these, reimbursements were reported to influence the scalability and sustainability of telemedicine. Objective: This study aimed to examine current payment models and reimbursement coverage for telemedicine in any national and subnational jurisdictions to inform the development of reimbursement policy. Methods: We conducted a scoping review using Arksey and O'Malley's 6-stage method, including sources that discussed telemedicine payment methods reimbursed by public payers. To supplement the limited results, particularly from low- and middle-income countries in Asia, we conducted 5 stakeholder interviews with telemedicine providers or those with experience in telemedicine reimbursement models who added insights for India, Nepal, and Taiwan. Data were synthesized narratively. Results: We included 31 of 14,522 records screened. Most (n=22, 71%) records were published after 2020, were research studies (n=26, 84%), and discussed reimbursement in the United States (n=24, 77%). We categorized reimbursement coverage as the purpose of telemedicine, health conditions, patients' nonhealth conditions, service providers, interaction participants, interaction modes, and technology used. Payment methods varied widely and included fee-for-service, capitation, bundled payment, and value-based models. Varying telemedicine reimbursement models adopted by countries reflect health service and care objectives along with health system characteristics. Payment mechanisms were linked to telemedicine services or broader health care delivery, with each presenting unique advantages. Conclusions: Workable telemedicine reimbursement is a critical enabling factor in expanding health care access by incentivizing provider participation, ensuring financial sustainability, promoting equity in access, and aligning telemedicine with broader health goals. This review provides a starting point for countries in designing a telemedicine reimbursement model specific to population needs and health system capacity. Policy makers are encouraged to leverage these insights in adapting telemedicine reimbursement to their context.
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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 | 0.039 | 0.156 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.027 | 0.026 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".