Reimbursement of Apps for Mental Health: Findings From Interviews
Bibliographic record
Abstract
BACKGROUND: Although apps and other digital and mobile health tools are helping improve the mental health of Americans, they are currently being reimbursed through a varied range of means, and most are not being reimbursed by payers at all. OBJECTIVE: The aim of this study was to shed light on the state of app reimbursement. We documented ways in which apps can be reimbursed and surveyed stakeholders to understand current reimbursement practices. METHODS: Individuals from over a dozen stakeholder organizations in the domains of digital behavioral and mental health, care delivery, and managed care were interviewed. A review of Current Procedural Terminology (CPT) and Healthcare Common Procedure Coding System (HCSPCS) codes was conducted to determine potential means for reimbursement. RESULTS: Interviews and the review of codes revealed that potential channels for app reimbursement include direct payments by employers, providers, patients, and insurers. Insurers are additionally paying for apps using channels originally designed for devices, drugs, and laboratory tests, as well as via value-based payments and CPT and HCSPCS codes. In many cases, it is only possible to meet the requirements of a CPT or HCSPCS code if an app is used in conjunction with human time and services. CONCLUSIONS: Currently, many apps face significant barriers to reimbursement. CPT codes are not a viable means of providing compensation for the use of all apps, particularly those involving little physician work. In some cases, apps have sought clearance from the US Food and Drug Administration for prescription use as digital therapeutics, a reimbursement mechanism with as yet unproven sustainability. There is a need for simpler, more robust reimbursement mechanisms to cover stand-alone app-based treatments.
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 | 0.018 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".