Shaping User-Centered Health Innovation Through Assessment

Trognon, Arthur; Servais, David; Habibi, Islem; Picard, Robert G.; Lihoreau, Thomas; Pazart, Lionel; Pelayo, Sylvia; Chevallier, Thierry; Ernecq, Kathryn; Garin, Anaïs; Béjean, Mathias; Abraham, Denis

doi:10.5220/0011925800003414

Cited by 2 publications

(4 citation statements)

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“…The French CML Health (CMLH) model 3,15 is a 9-levels reading grid that iteratively breaks down the innovation process into three interconnected axes: needs, technology, and programmatic. It is a direct descendant of the original CML model developed by the American National Aeronautics and Space Administration 16 , which incorporated the last two domains but now includes an additional user-centered axis.…”

Section: Modelmentioning

confidence: 99%

“…It is a direct descendant of the original CML model developed by the American National Aeronautics and Space Administration 16 , which incorporated the last two domains but now includes an additional user-centered axis. Furthermore, it speci es the technology and programmatic domains to adapt them to French and European regulatory requirements in research methodology and data management (Table 1 (Trognon, 2023) The Programmatic axis of the CML Santé France model assesses the programmatic maturity of the project across three areas: project management, regulatory aspects, and nancial aspects (Table 4). The project management axis evaluates the project consortium, from pilot identi cation to development partnership renewal, including the creation of Test Beds, and examines the nature of partnerships formed.…”

Section: Modelmentioning

confidence: 99%

“…Example of the different maturity levels for each sub-area of the "Technological Maturity" domain, adapted from(Trognon, 2023) …”

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confidence: 99%

“…Factorial structure of the French CML Health model, from(Trognon, 2023) The rst axis of the CML Health model (Table2) is derived from the Technology-Readiness Level model17 . It evaluates the development of technological concepts, product management, and ownership through three formalized axes: technological development, data management, and intellectual property.…”

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confidence: 99%

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A computational validation for the Health Concept Maturity Levels Questionnaire

Trognon,

Habibi,

Altakroury

et al. 2023

Preprint

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Background The healthcare market is undergoing rapid transformation, requiring the integration of user needs from the earliest stages of product and service design. Living Labs are emerging as a model for the co-creation and evaluation of user-centered innovations. In this work, we developed a health CML grid and questionnaire to assess the maturity of health concepts. Methods The research process included multiple stages, starting with the creation of the Association Innov'Autonomie - Health Concept Maturity Levels Questionnaire − 178-items (AIA/CMLHQ178), designed to evaluate health concept maturity levels. Speech acts from Health CML expert interventions were then annotated and used as data for our machine learning and deep learning models. We used the CatBoost algorithm in the first experiment to discern individual Health CML factors from speech acts to generate factor probabilities used to feed a neural network trained to take the final decision, to evaluate whether the network could accurately identify the membership factors of Health CML criteria when presented with items from the AIA/CMLHQ178 questionnaire. Results The results of the study indicate that only the models trained with the true factors are able to correctly identify the corresponding factor in the sequentially encoded texts, with the exception of the need domains whose artificial performance was measured according to sensitivity. The general performance of the different CatBoost algorithms used to predict one factor versus the other two showed similar performance. For the questionnaire, the models trained with the real factors also showed better performance in identifying the matching factors compared to the random factors. A marginal difference was observed between the "Need" and "Technology" factors. Conclusion This study provides initial evidence of content validity for the AIA/CMLHQ178, introducing a novel approach to validate psychometric instruments using machine learning and deep learning techniques. However, overlaps between "Programmatic" and "Need" factors indicate a need for improvement in the CML Health model. Future work will focus on enhancing these models and investigating their potential application in other psychometric tools.

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Section: Modelmentioning

confidence: 99%

Section: Modelmentioning

confidence: 99%

“…Example of the different maturity levels for each sub-area of the "Technological Maturity" domain, adapted from(Trognon, 2023) …”

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confidence: 99%

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confidence: 99%

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