Unlocking the potential of digital therapeutics: The need for consistent and granular inclusion in drug compendia for managed care
Bibliographic record
Abstract
The field of digital therapeutics (DTx), software programs that prevent, manage, and treat medical conditions, continues to grow. DTx offers new treatment options and has the potential to close gaps in care caused by unmet patient needs, provider shortages, or socioeconomic or geographical disparities. However, the field of DTx has not seen steady adoption owing to barriers, particularly related to coverage, payer acceptance of the category, provider use, and integration within existing health care delivery tools. One challenge for payers to effectively evaluate and cover DTx products is ensuring that consistent data elements are listed for these products in traditional drug compendia databases. Managed care organizations will need similar information about DTx product features as are available for traditional medications to inform coverage and reimbursement decisions. The Academy of Managed Care Pharmacy DTx Advisory Group developed and distributed a request for information to the 5 top drug compendia companies to assess how compendia products incorporate DTx and prescription DTx. This article summarizes how DTx are listed within different compendia products and offers insights on future data needs to adequately inform payers. As the DTx sector grows and consumer demand rises, compendia listing services will need to evolve to accommodate these new therapies and treatment modalities and facilitate patient access and efficient claims processing. Recommendations for how compendia companies can support managed care in these efforts are outlined.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".