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.

OBJECTIVE: HL7 SMART on FHIR apps have the potential to improve healthcare delivery and EHR usability, but providers must be aware of the apps and use them for these potential benefits to be realized. The HL7 CDS Hooks standard was developed in part for this purpose. The objective of this study was to determine if contextually relevant CDS Hooks prompts can increase utilization of a SMART on FHIR medical reference app (MDCalc for EHR). MATERIALS AND METHODS: We conducted a 7-month, provider-randomized trial with 70 providers in a single emergency department. The intervention was a collection of CDS Hooks prompts suggesting the use of 6 medical calculators in a SMART on FHIR medical reference app. The primary outcome was the percentage of provider-patient interactions in which the app was used to view a recommended calculator. Secondary outcomes were app usage stratified by individual calculators. RESULTS: Intervention group providers viewed a study calculator in the app in 6.0% of interactions compared to 2.6% in the control group (odds ratio = 2.45, 95% CI, 1.2-5.2, P value .02), an increase of 130%. App use was significantly greater for 2 of 6 calculators. DISCUSSION AND CONCLUSION: Contextually relevant CDS Hooks prompts led to a significant increase in SMART on FHIR app utilization. This demonstrates the potential of using CDS Hooks to guide appropriate use of SMART on FHIR apps and was a primary motivation for the development of the standard. Future research may evaluate potential impacts on clinical care decisions and outcomes.

OBJECTIVE: The study sought to evaluate a novel electronic health record (EHR) add-on application for chronic disease management that uses an integrated display to decrease user cognitive load, improve efficiency, and support clinical decision making. MATERIALS AND METHODS: We designed a chronic disease management application using the technology framework known as SMART on FHIR (Substitutable Medical Applications and Reusable Technologies on Fast Healthcare Interoperability Resources). We used mixed methods to obtain user feedback on a prototype to support ambulatory providers managing chronic obstructive pulmonary disease. Each participant managed 2 patient scenarios using the regular EHR with and without access to our prototype in block-randomized order. The primary outcome was the percentage of expert-recommended ideal care tasks completed. Timing, keyboard and mouse use, and participant surveys were also collected. User experiences were captured using a retrospective think-aloud interview analyzed by concept coding. RESULTS: With our prototype, the 13 participants completed more recommended care (81% vs 48%; P < .001) and recommended tasks per minute (0.8 vs 0.6; P = .03) over longer sessions (7.0 minutes vs 5.4 minutes; P = .006). Keystrokes per task were lower with the prototype (6 vs 18; P < .001). Qualitative themes elicited included the desire for reliable presentation of information which matches participants' mental models of disease and for intuitive navigation in order to decrease cognitive load. DISCUSSION: Participants completed more recommended care by taking more time when using our prototype. Interviews identified a tension between using the inefficient but familiar EHR vs learning to use our novel prototype. Concept coding of user feedback generated actionable insights. CONCLUSIONS: Mixed methods can support the design and evaluation of SMART on FHIR EHR add-on applications by enhancing understanding of the user experience.