A Patient-Centered Framework for Measuring the Economic Value of the Clinical Benefits of Digital Health Apps: Theoretical Modeling
Bibliographic record
Abstract
BACKGROUND: As digital health tools such as smartphone apps evolve and enter clinical use, questions regarding their value must be addressed. Although there are scarce generalizable data on the value of health apps given their nascency and diverse use cases, it is possible to estimate the economic value of the clinical improvement they bring to patients using a quality-adjusted life-year (QALY)-based approach and generalized values from existing literature. OBJECTIVE: This paper aims to provide a patient-centered framework for assessing the economic value of the clinical benefits delivered by digital health apps. METHODS: We proposed a model based upon 5 levers: country-specific monetary value of a QALY, QALYs lost due to the condition, engagement rate of app users, average effect size of the app's health impact, and duration of the app's impact before remission. RESULTS: Using 2 digital health apps from the United States and United Kingdom as examples, we explored how this model could generate country-specific estimates of the economic value of the clinical benefits of health apps. CONCLUSIONS: This new framework can help drive research priorities for digital health by elucidating the factors that influence the economic value.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".