Considerations for Improving the Portability of Electronic Health Record-Based Phenotype Algorithms.
AMIA Annu Symp Proc
; 2019: 755-764, 2019.
Article
en En
| MEDLINE
| ID: mdl-32308871
ABSTRACT
With the increased adoption of electronic health records, data collected for routine clinical care is used for health outcomes and population sciences research, including the identification of phenotypes. In recent years, research networks, such as eMERGE, OHDSI and PCORnet, have been able to increase statistical power and population diversity by combining patient cohorts. These networks share phenotype algorithms that are executed at each participating site. Here we observe experiences with phenotype algorithm portability across seven research networks and propose a generalizable framework for phenotype algorithm portability. Several strategies exist to increase the portability of phenotype algorithms, reducing the implementation effort needed by each site. These include using a common data model, standardized representation of the phenotype algorithm logic, and technical solutions to facilitate federated execution of queries. Portability is achieved by tradeoffs across three domains Data, Authoring and Implementation, and multiple approaches were observed in representing portable phenotype algorithms. Our proposed framework will help guide future research in operationalizing phenotype algorithm portability at scale.
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Algoritmos
/
Registros Electrónicos de Salud
Tipo de estudio:
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
AMIA Annu Symp Proc
Asunto de la revista:
INFORMATICA MEDICA
Año:
2019
Tipo del documento:
Article
País de afiliación:
Israel