Understanding Economic Decision-Making in Digital Therapeutics Development: Qualitative Approach
Bibliographic record
Abstract
BACKGROUND: Digital therapeutics (DTx) represent a transformative shift in health care delivery, offering software-driven, evidence-based therapeutic interventions. Despite their potential, adoption remains low across health care systems, partly due to insufficient economic evidence. Significant knowledge gaps persist regarding stakeholders' approaches to economic decisions in DTx development, with prior studies also indicating limited consideration of economic factors in early DTx development stages, particularly from researchers. OBJECTIVE: This study investigates how researchers approach decision-making regarding factors that influence the economic impact of DTx during technological development and clinical validation phases, examining the underlying mechanisms and contextual conditions that shape these processes. METHODS: Using a critical realism philosophical stance, 17 semistructured interviews were conducted with researchers involved in DTx development, including research engineers (n=5), health systems and social science researchers (n=6), clinician-researchers (n=4), and practitioner-researchers (n=2). The research approach combined deductive and inductive coding, followed by abductive and retroductive inference processes to identify generative mechanisms underlying observed decision-making patterns. Qualitative system dynamics modeling was applied to visualize causal loop relationships through triangulated data sources. RESULTS: Three interrelated generative mechanisms were identified that shape researchers' decision-making regarding economic considerations: (1) the professional norms, operating through reinforcing loops that systematically prioritize clinical validation while marginalizing economic considerations; (2) the researcher experience, revealing how professional training and limited economic literacy create cognitive biases that obscure economic factors; and (3) the DTx adoption uncertainties, demonstrating how implementation concerns influence development decisions through both reinforcing and balancing feedback loop dynamics. These mechanisms explain why, despite growing recognition of the importance of economic evidence, economic considerations remain peripheral in researchers' decision frameworks. CONCLUSIONS: This study reveals complex interactions between institutional structures, intrapersonal factors, and implementation uncertainties that systematically deprioritize economic considerations in DTx development. The identified mechanisms provide valuable intervention points for strengthening the development process toward a more comprehensive assessment of clinical, technical, and economic value throughout the DTx lifecycle to ultimately enhance their adoption in health care systems.
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 | 0.082 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".