A group concept mapping study of stakeholder perspectives on digital therapeutics economic value drivers
Bibliographic record
Abstract
BackgroundDigital therapeutics (DTx), software as a medical device, present a promising avenue for addressing the increasing burden of a range of conditions. In a context of budgetary constraints and an enduring need for optimal resource allocation, their implementation and adoption are contingent upon their capacity to provide economic value, which remains a research gap.MethodsGroup concept mapping, a participatory and mixed method was used to delve into a comprehensive identification and understanding of the factors that are conceptualized and perceived to impact the economic value of DTx. It involved semi-structured interviews, group discussions and web-based tasks with healthcare professionals, researchers, industry and public sector representatives. Participants sorted factors by similarity (n=37), rated their importance in generating economic value and consideration in practice (n=62). Multivariate methods included nonmetric multidimensional scaling analysis and agglomerative hierarchical cluster analysis to estimate conceptual data into visual representations.Findings59 factors influencing the economic value of DTx were identified and grouped into eight clusters. There was broad agreement among stakeholders on the significance of the DTx Impact on Patient Outcomes and DTx Implementation as the most influential clusters affecting the economic value of DTx. The DTx Associated Costs and DTx Monetization Models clusters were reported as not receiving potentially sufficient consideration throughout the DTx development lifecycle, particularly among researchers. Consequently, a conceptual framework of priority clusters and factors potentially driving DTx economic value is proposed.InterpretationThe results illustrate how stakeholders conceptualize, perceive, and consider factors impacting DTx economic value throughout its lifecycle. They reveal the complex interplay of multiple stakeholders and factors influencing DTx economic value, highlighting areas requiring prioritization at each stage.
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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 | 0.028 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".