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     <dc:title xml:lang="fr">Méthodes digitales au service du recrutement des patients dans les essais cliniques: besoins actuels, solutions apportées et perspectives d’amélioration</dc:title>
     <dcterms:alternative xml:lang="en">Digital methods enhancing patient recruitment in clinical trials: current need, solutions and perspectives of improvement</dcterms:alternative>
     <dc:subject xml:lang="fr">Recherche clinique</dc:subject><dc:subject xml:lang="fr">Essai clinique</dc:subject><dc:subject xml:lang="fr">Recrutement</dc:subject><dc:subject xml:lang="fr">Patient</dc:subject><dc:subject xml:lang="fr">Intelligence artificielle</dc:subject><dc:subject xml:lang="fr">Digital</dc:subject><dc:subject xml:lang="fr">Start-up</dc:subject><dc:subject xml:lang="fr">Investigateur</dc:subject><dc:subject xml:lang="fr">CRO</dc:subject>
     <dc:subject xml:lang="en">Clinical Research</dc:subject><dc:subject xml:lang="en">Clinical trial</dc:subject><dc:subject xml:lang="en">Recruitment</dc:subject><dc:subject xml:lang="en">Patient</dc:subject><dc:subject xml:lang="en">Artificial intelligence</dc:subject><dc:subject xml:lang="en">Digital</dc:subject><dc:subject xml:lang="en">Start-up</dc:subject><dc:subject xml:lang="en">Investigator</dc:subject><dc:subject xml:lang="en">CRO</dc:subject><tef:sujetRameau><tef:vedetteRameauNomCommun>
						<tef:elementdEntree autoriteSource="Sudoc" autoriteExterne="027607690">Malades</tef:elementdEntree><tef:subdivision autoriteSource="Sudoc" type="subdivisionDeSujet" autoriteExterne="027798887">Recrutement</tef:subdivision>
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						<tef:elementdEntree autoriteSource="Sudoc" autoriteExterne="027238989">Médecine clinique</tef:elementdEntree>
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						<tef:elementdEntree autoriteSource="Sudoc" autoriteExterne="027234541">Intelligence artificielle </tef:elementdEntree>
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     <dcterms:abstract xml:lang="fr">La recherche clinique est dans une phase de transformation profonde. Les changements s’inscrivent dans le contexte de digitalisation avec l’arrivée de l’intelligence artificielle, et profitent à l’industrie pharmaceutique qui fait face à des problèmes grandissants. La complexification de la réglementation et de la logistique des essais cliniques a mené à un nouvel enjeu : recruter le patient idéal pour une étude clinique donnée. Cette étape clé est devenue la plus redoutée par les professionnels de la recherche clinique, puisqu’elle génère des délais considérables dans les études cliniques, qui retardent la mise sur le marché de nouveaux médicaments. Les moyens développés pour améliorer le recrutement des patients se multiplient, et de nouvelles entreprises voient le jour.  Deux études ont été conduites afin de mettre en lien les solutions apportées par les startups développant des outils digitaux avec les attentes des CROs. L’analyse des données recueillies, et la mise en commun des différentes stratégies, a permis de souligner les avantages et inconvénients de chaque solution, et de proposer des perspectives d’amélioration.</dcterms:abstract>
     <dcterms:abstract xml:lang="en">Clinical research is in a phase of deep transformation. These changes are taking place against a backdrop of digitalization, with the arrival of artificial intelligence, and are benefiting the pharmaceutical industry, which is facing growing challenges. The increasing complexity of clinical trial regulations and logistics has led to a new challenge: recruiting the ideal patient for a given clinical study. This key step has become the most dreaded by clinical research professionals, since it generates considerable delays in clinical studies, which in turn delay the marketing of new drugs. The means developed to improve patient recruitment are multiplying, and new companies are springing up. Two studies have been carried out to compare the solutions offered by startups developing digital tools with the expectations of CROs. By analyzing the data collected and pooling the different strategies, we were able to highlight the advantages and disadvantages of each solution, and suggest ways forward.</dcterms:abstract>
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