Payment Systems for Telemedicine: Comparative Study (Preprint)
Bibliographic record
Abstract
BACKGROUND Many researchers have addressed the lack of reimbursement for telemedicine as one of the most important barriers to telemedicine adoption. However, little is known on how telemedicine should be implemented in reimbursement policy, how it must be financed, and what the right incentives are for an effective and efficient telemedicine use. OBJECTIVE To help future researchers to provide reimbursement policy recommendations, and to facilitate reimbursement decision-making, this paper analyzed and compared the telemedicine payment models of ten countries. METHODS A convenience sample was created of Western countries inside and outside Europe that already reimburse to some extent telemedicine. Ten countries met this criterion: Australia, Belgium, Denmark, France, Germany, Luxembourg, the Netherlands, Canada (Ontario province), Switzerland, and the United Kingdom. The study was based on the countries’ official physician fee schedules, listing all reimbursed medical services performed by physicians, including telemedicine. Based on the fee schedules, a comparative analysis of the payment models of telemedicine was conducted. RESULTS Televisits are reimbursed in all countries, which is not the case for telemonitoring and tele-expertise services. Telemonitoring is often restricted for patients with implanted cardiac devices. Telemedicine services are mainly paid fee-for-service, except for the telemonitoring of patients with implanted cardiac devices, which is paid through an episodic payment system in Australia. Payment parity exists across televisits and visits in person in France, Luxembourg, the Netherlands, and Switzerland, meaning that an equal fee is given for both services. CONCLUSIONS Our findings show that fees for telemedicine are lacking, especially for telemonitoring and tele-expertise. As telemedicine might enlarge disparities in healthcare access, policymakers should consider payment parity across televisits and face-to-face visits, and across telephone and video visits. Furthermore, an episodic physician payment system complemented with bonuses for quality outcomes, should be considered by policymakers for telemonitoring as it might capture the specificities of telemonitoring better than a fee-for-service system. Future research is needed on payment models, including research linking cost-effectiveness analyses with analyses on payment models, to allow profound reimbursement recommendations and a faster decision-making process for the reimbursement of telemedicine.
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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 | 0.009 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.001 |
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".