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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.103 | 0.224 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.028 | 0.032 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.009 | 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".