Organizers: Vaishnavi Kannan, DuWayne Willett
Designing effective Clinical Decision Support (CDS) tools in an Electronic Health Record (EHR) can prove challenging, due to complex real-world scenarios and newly-discovered requirements. As such, deploying new CDS EHR tools shares much in common with new product development, where “agile” principles and practices consistently prove more effective than traditional project management.
Typical agile principles and practices can thus prove helpful on CDS projects, including time-boxed “sprints” and lightweight requirements gathering with User Stories and acceptance criteria. Modeling CDS behavior removes ambiguity and promotes shared understanding of desired behavior, but risks analysis paralysis: an Agile Modeling approach can foster effective rapid-cycle CDS design and optimization. The agile practice of automated testing for test-driven design and regression testing can be applied to CDS development in EHRs using open-source tools. Ongoing monitoring of CDS behavior once released to production can identify anomalies and prompt rapid-cycle redesign to further enhance CDS effectiveness. The workshop participant will learn about these topics in interactive didactic sessions, with time for practicing the techniques taught.
- Explain benefits of employing agile principles and practices during new product development; evaluate how CDS development for a local practice environment shares characteristics with new product
- Iteratively refine CDS requirements with requestors using User Stories and acceptance criteria; understand how sizing with relative Story Points improves forecasting of scheduled delivery into
- Identify which models/diagrams prove most helpful during CDS design, and practice applying them to a realistic CDS design and development
- Recognize the value of automated testing in iterative development projects, and practice writing CDS “business rules” testable using open-source automated testing software.
- Design an interactive CDS usage graph/report from EHR-captured data, for monitoring CDS tool behavior following release to production, to help detect anomalies and/or opportunities for further tool refinement.
List of Speakers
Vaishnavi Kannan, MS, University of Texas Southwestern Medical Center,
Agile development and agile modeling for clinical decision support in electronic health records.
Vaishnavi Kannan is a clinical decision support specialist and data scientist in the applied clinical informatics program at UT Southwestern Medical Center, in Dallas, TX. In this role, she led development of the Modified Early Warning System (MEWS) to detect incipient inpatient clinical deterioration, and multiple other clinical decision support features in the electronic health record (EHR). She also served as technical lead on a project to develop 40 specialty patient registries in 9 months using agile techniques. This project was recognized by Healthcare Informatics as the first place recipient of their 2016 Innovator Awards.
Duwayne L. Willett, MD, MS, University of Texas Southwestern Medical Center,
Agile test-driven development of clinical decision support tools, and monitoring of clinician interactions for iterative enhancements.
Dr. Willett is the Chief Medical Informatics Officer for the University of Texas Southwestern Health System, and a Professor of Internal Medicine / Cardiology there. As CMIO, Dr. Willett oversees the design and implementation of clinical systems at UT Southwestern, with the goals of enhancing clinical quality, provider efficiency and patient experience. He also guides design of the health analytics lifecycle, from data capture in the electronic health record through efficient extraction into the enterprise data warehouse. He currently serves as Director of the Informatics core for UT Southwestern’s Center for Translational Medicine, with work focusing on applied clinical informatics. Dr. Willett holds Master’s degrees in Information Systems and in Healthcare Management, and is board-certified in Cardiovascular Disease and in Clinical Informatics.