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CC BY 4.0 · ACI open 2020; 04(02): e157-e161
DOI: 10.1055/s-0040-1721489
Original Article

Deploying Clinical Decision Support for Familial Hypercholesterolemia

Hana Bangash
1   Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, United States
,
Joseph Sutton
2   Department of Information Technology, Mayo Clinic, Rochester, Minnesota, United States
,
Justin H. Gundelach
1   Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, United States
,
Laurie Pencille
3   Center for Science of HealthCare Delivery, Mayo Clinic, Rochester, Minnesota, United States
,
Ahmed Makkawy
4   User Experience Research, Saharafox Creative Agency, Rochester, Minnesota, United States
,
Omar Elsekaily
1   Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, United States
,
Ozan Dikilitas
1   Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, United States
,
Ali Mir
1   Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, United States
,
Robert Freimuth
5   Department of Digital Health Sciences, Mayo Clinic, Rochester, Minnesota, United States
,
Pedro J. Caraballo
6   Department of General Internal Medicine, Mayo Clinic, Rochester, Minnesota, United States
,
Iftikhar J. Kullo
1   Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, United States
› Institutsangaben Funding This study was funded by National Heart, Lung, and Blood Institute, grants: R01 HL135879 and K24 HL137010.
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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.

Keywords

implementation and deployment - facilitators and barriers - testing and evaluation - clinical decision support - precision medicine - workflows and human interactions - human–computer interaction - interfaces and usability


Publikationsverlauf

Eingereicht: 13. März 2020

Angenommen: 04. November 2020

Artikel online veröffentlicht:
31. Dezember 2020

© 2020. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/)

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 

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