PD45 Paying For Digital Health: What Evidence Is Needed?
Bibliographic record
Abstract
Introduction Digital transformation has been promoted by the World Health Organization (WHO), Food and Drug Administration (FDA) and the European Commission (EC) to help improve health outcomes. To ensure sustainability, digital health interventions (DHI) require funding by payers. Evidence-informed decision and policy making requires an assessment of the impact on relevant outcomes vs current healthcare practice. Various national and international organizations are involved in creating or guiding the development of standards for the evidence required for digital technologies. Methods We undertook an intensive individual investigation of the websites of leading payer and health technology assessment (HTA) bodies in France, UK, Germany, Belgium, Austria, Finland, Canada, Australia, and the USA to identify new frameworks and any updated information. As the objective focused on evaluation frameworks which were used across DHIs by a particular payer to support pricing and reimbursement decisions, we excluded individual case studies where DHIs had been assessed, regulatory frameworks for approval of DHIs and frameworks which assessed feasibility or applicability of a DHI since these were not directly influencing the decision for funding. Results We found six frameworks which directly address digital health interventions for the purposes of pricing and reimbursement: NICE Evidence Standards, FinCCHTA, MSAC, Germany BfArM, Belgium RIZIV and France HAS. The context for the framework and the requirements were compared on parameters including those normally found in HTA and for criteria related to digital technologies. The parameters included varied considerably across the frameworks as did the level of evidence expected to be available for the assessment. In some cases, these related to the level of risk or impact of the intended DHI. Conclusions While DHIs are increasingly used in health, HTA is struggling to adapt to assess these technologies. Due to the multidisciplinary nature of digital health (combination of health care and technology), and the speed and rate of change of innovations in this area, an approach based upon the risk assessment posed by the technology seems reasonable. In this way the level of effort can be tailored to those interventions which seek to influence care or predict outcomes rather than those which are tailored to increased awareness of the patient about their condition.
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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 | 0.136 | 0.428 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".