Population health management (PHM) is an important approach to promote wellness and deliver health care to targeted individuals who meet criteria for preventive measures or treatment. A critical component for any PHM program is a data analytics platform that can target those eligible individuals. OBJECTIVE: The aim of this study was to design and implement a scalable standards-based clinical decision support (CDS) approach to identify patient cohorts for PHM and maximize opportunities for multi-site dissemination. MATERIALS AND METHODS: An architecture was established to support bidirectional data exchanges between heterogeneous electronic health record (EHR) data sources, PHM systems, and CDS components. HL7 Fast Healthcare Interoperability Resources and CDS Hooks were used to facilitate interoperability and dissemination. The approach was validated by deploying the platform at multiple sites to identify patients who meet the criteria for genetic evaluation of familial cancer. RESULTS: The Genetic Cancer Risk Detector (GARDE) platform was created and is comprised of four components: (1) an open-source CDS Hooks server for computing patient eligibility for PHM cohorts, (2) an open-source Population Coordinator that processes GARDE requests and communicates results to a PHM system, (3) an EHR Patient Data Repository, and (4) EHR PHM Tools to manage patients and perform outreach functions. Site-specific deployments were performed on onsite virtual machines and cloud-based Amazon Web Services. DISCUSSION: GARDE's component architecture establishes generalizable standards-based methods for computing PHM cohorts. Replicating deployments using one of the established deployment methods requires minimal local customization. Most of the deployment effort was related to obtaining site-specific information technology governance approvals.
Population health management (PHM) is an important approach to promote wellness and deliver health care to targeted individuals who meet criteria for preventive measures or treatment. A critical component for any PHM program is a data analytics platform that can target those eligible individuals. OBJECTIVE: The aim of this study was to design and implement a scalable standards-based clinical decision support (CDS) approach to identify patient cohorts for PHM and maximize opportunities for multi-site dissemination. MATERIALS AND METHODS: An architecture was established to support bidirectional data exchanges between heterogeneous electronic health record (EHR) data sources, PHM systems, and CDS components. HL7 Fast Healthcare Interoperability Resources and CDS Hooks were used to facilitate interoperability and dissemination. The approach was validated by deploying the platform at multiple sites to identify patients who meet the criteria for genetic evaluation of familial cancer. RESULTS: The Genetic Cancer Risk Detector (GARDE) platform was created and is comprised of four components: (1) an open-source CDS Hooks server for computing patient eligibility for PHM cohorts, (2) an open-source Population Coordinator that processes GARDE requests and communicates results to a PHM system, (3) an EHR Patient Data Repository, and (4) EHR PHM Tools to manage patients and perform outreach functions. Site-specific deployments were performed on onsite virtual machines and cloud-based Amazon Web Services. DISCUSSION: GARDE's component architecture establishes generalizable standards-based methods for computing PHM cohorts. Replicating deployments using one of the established deployment methods requires minimal local customization. Most of the deployment effort was related to obtaining site-specific information technology governance approvals.
Authors: Rebecca L Curran; Polina V Kukhareva; Teresa Taft; Charlene R Weir; Thomas J Reese; Claude Nanjo; Salvador Rodriguez-Loya; Douglas K Martin; Phillip B Warner; David E Shields; Michael C Flynn; Jonathan P Boltax; Kensaku Kawamoto Journal: J Am Med Inform Assoc Date: 2020-08-01 Impact factor: 4.497
Authors: Zameer Abedin; Robert Hoerner; Joseph Habboushe; Yi Lu; Kensaku Kawamoto; Phillip B Warner; David E Shields; Rashmee U Shah Journal: Circ Cardiovasc Qual Outcomes Date: 2020-02-06
Authors: Jesualdo Tomás Fernández-Breis; José Alberto Maldonado; Mar Marcos; María del Carmen Legaz-García; David Moner; Joaquín Torres-Sospedra; Angel Esteban-Gil; Begoña Martínez-Salvador; Montserrat Robles Journal: J Am Med Inform Assoc Date: 2013-08-09 Impact factor: 4.497
Authors: Chaitanya Shivade; Preethi Raghavan; Eric Fosler-Lussier; Peter J Embi; Noemie Elhadad; Stephen B Johnson; Albert M Lai Journal: J Am Med Inform Assoc Date: 2013-11-07 Impact factor: 4.497
Authors: James Jones; Daniel Gottlieb; Joshua C Mandel; Vladimir Ignatov; Alyssa Ellis; Wayne Kubick; Kenneth D Mandl Journal: J Am Med Inform Assoc Date: 2021-06-12 Impact factor: 4.497
Authors: Kimberly A Kaphingst; Wendy Kohlmann; Rachelle Lorenz Chambers; Melody S Goodman; Richard Bradshaw; Priscilla A Chan; Daniel Chavez-Yenter; Sarah V Colonna; Whitney F Espinel; Jessica N Everett; Amanda Gammon; Eric R Goldberg; Javier Gonzalez; Kelsi J Hagerty; Rachel Hess; Kelsey Kehoe; Cecilia Kessler; Kadyn E Kimball; Shane Loomis; Tiffany R Martinez; Rachel Monahan; Joshua D Schiffman; Dani Temares; Katie Tobik; David W Wetter; Devin M Mann; Kensaku Kawamoto; Guilherme Del Fiol; Saundra S Buys; Ophira Ginsburg Journal: BMC Health Serv Res Date: 2021-06-02 Impact factor: 2.655
Authors: Guilherme Del Fiol; Wendy Kohlmann; Richard L Bradshaw; Charlene R Weir; Michael Flynn; Rachel Hess; Joshua D Schiffman; Claude Nanjo; Kensaku Kawamoto Journal: JCO Clin Cancer Inform Date: 2020-01