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Deploying Clinical Decision Support for Familial Hypercholesterolemia.
Bangash, Hana; Sutton, Joseph; Gundelach, Justin H; Pencille, Laurie; Makkawy, Ahmed; Elsekaily, Omar; Dikilitas, Ozan; Mir, Ali; Freimuth, Robert; Caraballo, Pedro J; Kullo, Iftikhar J.
Afiliación
  • Bangash H; Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, United States.
  • Sutton J; Department of Information Technology, Mayo Clinic, Rochester, Minnesota, United States.
  • Gundelach JH; Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, United States.
  • Pencille L; Center for Science of HealthCare Delivery, Mayo Clinic, Rochester, Minnesota, United States.
  • Makkawy A; User Experience Research, Saharafox Creative Agency, Rochester, Minnesota, United States.
  • Elsekaily O; Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, United States.
  • Dikilitas O; Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, United States.
  • Mir A; Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, United States.
  • Freimuth R; Department of Digital Health Sciences, Mayo Clinic, Rochester, Minnesota, United States.
  • Caraballo PJ; Department of General Internal Medicine, Mayo Clinic, Rochester, Minnesota, United States.
  • Kullo IJ; Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, United States.
ACI open ; 4(2): e157-e161, 2020 Jul.
Article en En | MEDLINE | ID: mdl-36644330
ABSTRACT

Objective:

Familial hypercholesterolemia (FH), a prevalent genomic disorder that increases risk of coronary heart disease, remains significantly underdiagnosed. Clinical decision support (CDS) tools have the potential to increase FH detection. We describe our experience in the development and implementation of a genomic CDS for FH at a large academic medical center.

Methods:

CDS development and implementation were conducted in four phases (1) development and validation of an algorithm to identify "possible FH"; (2) obtaining approvals from institutional committees to develop the CDS; (3) development of the initial prototype; and (4) use of an implementation science framework to evaluate the CDS.

Results:

The timeline for this work was approximately 4 years; algorithm development and validation occurred from August 2018 to February 2020. During this 4-year period, we engaged with 15 stakeholder groups to build and integrate the CDS, including health care providers who gave feedback at each stage of development. During CDS implementation six main challenges were identified (1) need for multiple institutional committee approvals; (2) need to align the CDS with institutional knowledge resources; (3) need to adapt the CDS to differing workflows; (4) lack of institutional guidelines for CDS implementation; (5) transition to a new institutional electronic health record (EHR) system; and (6) limitations of the EHR related to genomic medicine.

Conclusion:

We identified multiple challenges in different domains while developing CDS for FH and integrating it with the EHR. The lessons learned herein may be helpful in streamlining the development and deployment of CDS to facilitate genomic medicine implementation.
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Texto completo: 1 Base de datos: MEDLINE Tipo de estudio: Guideline / Prognostic_studies Idioma: En Revista: ACI open Año: 2020 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Base de datos: MEDLINE Tipo de estudio: Guideline / Prognostic_studies Idioma: En Revista: ACI open Año: 2020 Tipo del documento: Article País de afiliación: Estados Unidos