Using Computable Phenotypes in Point-of-Care Clinical Trial Recruitment.

clinical trials learning healthcare system microservices phenomics

Journal

Studies in health technology and informatics
ISSN: 1879-8365
Titre abrégé: Stud Health Technol Inform
Pays: Netherlands
ID NLM: 9214582

Informations de publication

Date de publication:
27 May 2021
Historique:
entrez: 27 5 2021
pubmed: 28 5 2021
medline: 1 6 2021
Statut: ppublish

Résumé

A key challenge in point-of-care clinical trial recruitment is to autonomously identify eligible patients on presentation. Similarly, the aim of computable phenotyping is to identify those individuals within a population that exhibit a certain condition. This synergy creates an opportunity to leverage phenotypes in identifying eligible patients for clinical trials. To investigate the feasibility of this approach, we use the Transform clinical trial platform and replace its archetype-based eligibility criteria mechanism with a computable phenotype execution microservice. Utilising a phenotype for acute otitis media with discharge (AOMd) created with the Phenoflow platform, we compare the performance of Transform with and without the use of phenotype-based eligibility criteria when recruiting AOMd patients. The parameters of the trial simulated are based on those of the REST clinical trial, conducted in UK primary care.

Identifiants

pubmed: 34042638
pii: SHTI210233
doi: 10.3233/SHTI210233
doi:

Types de publication

Journal Article

Langues

eng

Pagination

560-564

Auteurs

Martin Chapman (M)

King's College London, London, United Kingdom.

Jesús Domínguez (J)

King's College London, London, United Kingdom.

Elliot Fairweather (E)

King's College London, London, United Kingdom.

Brendan C Delaney (BC)

Imperial College, London, United Kingdom.

Vasa Curcin (V)

King's College London, London, United Kingdom.

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Classifications MeSH