Casey Overby Taylor, Johns Hopkins School of Medicine “Accelerating Clinical Decision Support for Data-Driven Personal Guidance”
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“Accelerating Clinical Decision Support for Data-Driven Personal Guidance”
Dr. Casey Overby Taylor is Assistant Professor in the Division of General Internal Medicine in Johns Hopkins School of Medicine (SoM) and is a Fellow in the Johns Hopkins Malone Center for Engineering in Healthcare. She has joint appointments in the Division of Health Sciences Informatics in the SoM and the Department of Health Policy and Management in the Johns Hopkins Bloomberg School of Public Health, and secondary appointments in the Biomedical Engineering and Computer Science Departments in the Johns Hopkins Whiting School of Engineering. Prior to her move to Hopkins in 2016, she was Assistant Professor in the University of Maryland Program for Personalized and Genomic Medicine. Dr. Taylor’s research draws from biomedical informatics and the related field of biomedical data science, to address the challenge of how to incorporate technology and digital approaches into clinical research and healthcare practices. She also draws from comparative effectiveness research approaches, including experience with conceptualizing and measuring implementation outcomes, to investigate the use of clinical decision support as a strategy to improve the adoption of clinically actionable guidelines. Dr. Taylor has previously received funding from AHRQ to develop clinical decision support using an implementation model that engages stakeholders and uses open source decision support platforms (R21 HS023390 [Overby]). Factors identified from that work have informed her current work to enable tailored and multifaceted strategies to implement clinical decision support. Feasibility is being studied through local and national collaborations, including the NIH-funded electronic medical records and genomics (eMERGE) Network where Dr. Taylor serves as co-Chair of the Electronic Health Records (EHR) integration workgroup.
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Abstract
“Accelerating Clinical Decision Support for Data-Driven Personal Guidance”
As healthcare practices are rapidly becoming more data-intensive, we are seeing a move from basic, rule-based, clinical decision support guidance to more data-driven and personalized guidance. Indeed, considering the multiple factors when assessing patient risk for poor health outcomes has potential to improve overall understanding of risk when compared to risk assessments accounting for a single factor alone. The success of such approaches to advance healthcare practices, however, will depend on our capacity to deploy clinical decision support in the electronic health record in a timely manner. Despite considerable effort and resources, the adoption of clinically actionable risk assessments remains slow. This talk will explore ways that data science, biomedical informatics, and implementation science research are helping to realize the potential of emerging data-driven methods to direct healthcare practices in ways that are more comprehensive for patients by paving the path to create, approve and deploy clinical decision support within the electronic health record.
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