ArticleJournal of managed care & specialty pharmacy2024
Considerations for US coverage and reimbursement: Which digital health products to evaluate? Findings from a modified Delphi study.
Article in Journal of managed care & specialty pharmacy, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Health System- and Payer-Level Decision-Making Processes Influencing the Adoption and Sustainability of Patient-Facing Digital Health Tools: Qualitative Study.Journal of medical Internet research · 2026Article
- Integrating digital therapeutics for patients with mental illness into clinical practice: perspectives for US healthcare professionals.Frontiers in public health · 2026Review
- Artificial intelligence in obesity management: clinical evidence, translational gaps, and implementation priorities-a structured narrative review.Frontiers in endocrinology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe rapid growth of digital health tools, including digital applications, wearables, sensors, diagnostics, digital therapeutics (DTx), and prescription DTx, offers new ways to treat patients and close gaps in care. Payers need transparent, credible, and efficient processes to differentiate products for potential reimbursement from the larger universe of digital health products.
objectiveTo identify areas of agreement, disagreement, and rationale for payers to determine which digital health products should be evaluated for formulary consideration and to develop generalizable criteria for health care decision-makers developing policies and approaches for digital health products.
methodsExperts from the Academy of Managed Care Pharmacy DTx Advisory Group Payer Evaluation subcommittee rated whether a pharmacy and therapeutics committee, health technology assessment group, or an innovation center within a health plan or pharmacy benefit manager should consider 14 hypothetical products for potential formulary coverage. Using a 4-step modified Delphi approach, experts rated whether it was appropriate for a payer to evaluate each product on a scale of 1 (strongly disagree) to 9 (strongly agree). Quantitative agreement was assessed using terciles of responses, medians, and the distribution of appropriateness scores. The corresponding discussions are summarized to identify generalizable criteria for payers to consider as they develop approaches to determine which digital health products to evaluate.
resultsAmong the 14 hypothetical products, 4 achieved quantitative agreement that payers
conclusionsThe rapid availability of digital health and DTx options can be daunting for health care decision-makers when determining which products to evaluate. These generalizable criteria can help payers develop a more efficient process.
Indexed as
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.