Clinical Simulation in the Regulation of Software as a Medical Device: An eDelphi Study
Notice bibliographique
Résumé
BACKGROUND: Accelerated digitalization in the health sector requires the development of appropriate evaluation methods to ensure that digital health technologies (DHTs) are safe and effective. Software as a medical device (SaMD) is a commonly used DHT by clinicians to provide care to patients. Traditional research methods for evaluating health care products, such as randomized clinical trials, may not be suitable for DHTs, such as SaMD. However, evidence to show their safety and efficacy is needed by regulators before they can be used in practice. Clinical simulation can be used by researchers to test SaMD in an agile and low-cost way; yet, there is limited research on criteria to assess the robustness of simulations and, subsequently, their relevance for a regulatory decision. OBJECTIVE: The objective of this study was to gain consensus on the criteria that should be used to assess clinical simulation from a regulatory perspective when it is used to generate evidence for SaMD. METHODS: An eDelphi study approach was chosen to develop a set of criteria to assess clinical simulation when used to evaluate SaMD. Participants were recruited through purposive and snowball sampling based on their experience and knowledge in relevant sectors. They were guided through an initial scoping questionnaire with key themes identified from the literature to obtain a comprehensive list of criteria. Participants voted upon these criteria in 2 Delphi rounds, with criteria being excluded if consensus was not met. Participants were invited to add qualitative comments during rounds and qualitative analysis was performed on the comments gathered during the first round. Consensus was predefined by 2 criteria: if <10% of the panelists deemed the criteria as "not important" or "not important at all" and >60% "important" or "very important." RESULTS: In total, 33 international experts in the digital health field, including academics, regulators, policy makers, and industry representatives, completed both Delphi rounds, and 43 criteria gained consensus from the participants. The research team grouped these criteria into 7 domains-background and context, overall study design, study population, delivery of the simulation, fidelity, software and artificial intelligence, and study analysis. These 7 domains were formulated into the simulation for regulation of SaMD framework. There were key areas of concern identified by participants regarding the framework criteria, such as the importance of how simulation fidelity is achieved and reported and the avoidance of bias throughout all stages. CONCLUSIONS: This study proposes the simulation for regulation of SaMD framework, developed through an eDelphi consensus process, to evaluate clinical simulation when used to assess SaMD. Future research should prioritize the development of safe and effective SaMD, while implementing and refining the framework criteria to adapt to new challenges.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,174 | 0,184 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,002 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,005 | 0,003 |
| Études des sciences et des technologies | 0,009 | 0,011 |
| Communication savante | 0,007 | 0,009 |
| Science ouverte | 0,003 | 0,017 |
| Intégrité de la recherche | 0,005 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».