Thirteen percent of individuals have an elevated risk for familial breast / colorectal cancer (Scheuner, 2010) and most are unaware of their risk. National Comprehensive Cancer Network (NCCN) guidelines recommend risk-based genetic testing for these individuals, but the guidelines are not expressed in a computationally shareable form. EHR vendor-provided tools offer proprietary solutions, and as a consequence, are not optimally scalable.
GARDE is a population clinical decision support (CDS) platform based on Fast Healthcare Interoperability Resources (FHIR) and CDS Hooks standards to support interoperability and logic sharing beyond single vendor solutions. GARDE is live at University of Utah Health (UHealth) and New York University Langone Health (NYU). The team is currently exploring deployment options at Intermountain Healthcare and other health systems.
GARDE screens and identifies patients who meet National Comprehensive Cancer Network (NCCN) criteria for genetic evaluation of familial cancer risk based on their family history in the EHR using both structured data and natural language processing of free-text data. Patients identified by GARDE are imported into an EHR's population health management (PHM) dashboard (e.g., Epic's Healthy Planet module) where genetic counseling staff review individual cases, select, and send bulk outreach messages to patients via chatbot and/or through the patient portal. Additionally, the GARDE-populated dashboard facilitates communicating care decisions with the patient's primary care provider to ensure care is coordinated.
GARDE was made possible through a research grant funded by the Informatics Technology for Cancer Research (ITCR) program of the US National Cancer Institute (NCI), and was devised by extending existing standards and leveraging existing EHR resources. CDS Hooks is a synchronous and individual patient-level CDS standard that was extended to support asynchronous population-level CDS via Web services. HL7 FHIR interfaces were devised to query and extract data on patient populations from existing enterprise data warehouse schemas. Additionally, generic EHR service patterns were devised to read/write patient data through existing proprietary EHR data services to populate the PHM dashboard.
The chatbot outreach interface was made possible through a grant funded by an NCI U01 grant to Broadening the Reach, Impact, and Delivery of Genetic Services (BRIDGE) referenced below.
Scalable Population CDS Platform Architecture
Population Coordinator - the central application workflow controller and interoperable data pipeline responsible for reading patient data from the EHR, performing population-based CDS operations to identify cohorts, and communicating cohorts/results to a PHM system.
OpenCDS – an open source CDS Hooks-compliant server responsible for computing patient eligibility for PHM cohorts.
EHR Patient Data – an electronic health record/patient data provider used for retrieving data required by the CDS algorithm; typically, an enterprise data warehouse (EDW) or web service API preferably based on FHIR standards.
EHR PHM Tools – tools for loading and managing patient populations, including tracking targeted patients and performing patient outreach. In the figure above, the Population Registry tools store patient populations. Manage Populations tools are used for navigating the registry to review patient statuses, and for performing outreach functions.
Chatbot Services – an automated and interactive patient chatbot that provides education through outreach services. It is not shown in Figure 1 but is a component of the outreach process within the Manage Populations bubble.
Utah Biomedical Informatics Team
Claude Nanjo, MPH, MAAS – Senior Clinical Informaticist
David Shields – Data Warehouse Architect III
Douglas Martin, MD – Senior Clinical Informaticist
Emerson Borsato, PhD – Research Associate
Guilherme Del Fiol, MD, PhD – Co-PI, Vice-Chair of Research
Jianlin Shi, PhD – Natural Language Processing engineer
Kensaku Kawamoto, MD, PhD – Project Co-PI, Associate CMIO (UHealth), Director of ReImagine EHR
Phillip Warner, MS – Senior Software Design Engineer
Richard Bradshaw, PhD, MS – Senior Clinical Informaticist
Huntsman Cancer Institute
Kim Kaphingst, PhD, Communications
Wendy Kohlmann, MS, Genetic Counselor
Kadyn Kimball, Study Coordinator
Scott Narus, PhD, Medical Informatics Director
Pallavi Ranade-Kharkar, MS, PhD, FAMIA, Director of Research Informatics and Precision Health Systems
NYU Langone – Perlmutter Cancer Center
Ophira Ginsburg, MD, Principal Investigator
Rachelle Chambers, MS, CGC, Genetic Counselor
Priscilla Chan, Genetic Counseling Assistant
Rachel Monahan, Senior Project Coordinator
Erica Lieberman, PhD, ANP, WHNP, Postdoctoral Fellow
Devin Mann, MD, MS, Senior Director of Informatics and Innovation
Javier Gonzalez, Technical Lead
Shane Loomis, Epic Care Analyst
Melody Goodman, PhD, Associate Dean of Research
NCI Data Science Webinar (https://datascience.cancer.gov/news-events/events/identifying-and-managing-patients-who-meet-evidence-based-criteria-genetic)
NCI Cancer Moonshot Webinar (https://datascience.cancer.gov/news-events/events/standards-based-informatics-infrastructure-identify-and-manage-patients-eligible)
Clinical Knowledge Summary Web service (https://github.com/gdelfiol/ClinicalKnowledgeSummary/KnowledgeSummaryService)
Clinical Knowledge Summary app (https://github.com/gdelfiol/ClinicalKnowledgeSummary/CKSapplication)
Bradshaw RL, Kawamoto K, Kaphingst KA, Kohlmann WK, Hess R, Flynn MC, Nanjo CJ, Warner PB, Shi J, Morgan K, Kimball K, Ranade-Kharkar P, Ginsburg O, Goodman M, Chambers R, Mann D, Narus SP, Gonzalez J, Loomis S, Chan P, Monahan R, Borsato EP, Shields DE, Martin DK, Kessler CM, Del Fiol G. GARDE: a standards-based clinical decision support platform for identifying population health management cohorts. J Am Med Inform Assoc. 2022 Feb 28:ocac028. doi: 10.1093/jamia/ocac028. Epub ahead of print. PMID: 35224632.
Del Fiol G, Kohlmann W, Bradshaw RL, Weir CR, Flynn M, Hess R, et al. Standards-Based Clinical Decision Support Platform to Manage Patients Who Meet Guideline-Based Criteria for Genetic Evaluation of Familial Cancer. JCO Clin Cancer Inform. 2020;4:1-9.
Taber P, Ghani P, Schiffman J, Kohlmann W, Hess R, Chidambaram V, Kawamoto K, Waller R, Borbolla D, Del Fiol G. Physicians’ strategies for using family history data: having the data is not the same as using the data. JAMIA Open. 2020;3(3):378-385.
Kaphingst K, Kohlmann W, Chambers RL, Kawamoto K, Del Fiol G, Buys S, Ginsburg O. Comparing models of delivery for cancer genetics services among patients receiving primary care who meet criteria for genetic evaluation in two healthcare systems: BRIDGE randomized controlled trial. BMC Health Services Research. 2021 Jun 2;21(1):542.