Research Area #1

Clinical Informatics

This research area has two focuses with an overarching goal of streamlining clinical workflow and equipping clinicians with well-designed clinical decision support (CDS) tools to improve clinician experience and care delivery and outcomes. In the first focus (1a), clinical workflow analysis (CWA) is conducted to understand processes and bottlenecks. The workflow data come from semi-structured interviews, time and motion studies, electronic health record timestamps, and real-time locating systems. The workflow patterns will correlate with clinicians' work efficiency, job satisfaction, and burnout. The findings can inform quality improvement initiatives. In the second focus (1b), CDS tools are developed following three phases. First, machine learning models are trained and validated to predict patient outcomes retrospectively. Second, the workflow and usability of the CDS tool are explored through design research methods. Third, the CDS is implemented and its effectiveness and impact on health outcomes are evaluated in a quasi-experimental study. Both focuses involve data-driven interactive dashboards and visual analytics to facilitate the information consumption and knowledge discovery.

Selected Publications (Annotations: *Corresponding author, Wu mentee, iEqual Contribution)

  • Wu DTY*, Barrick L, Ozkaynak M, Blondon K, Zheng K. Special Section on Workflow Automation: Principles for Designing and Developing a Workflow Monitoring Tool to Enable and Enhance Clinical Workflow Automation. Appl Clin Inform. In Press.

  • Liu L*, Wu DTY, Spooner SA, Ni Y. Development and Evaluation of an Automated Approach to Detect Weight Abnormalities in Pediatric Weight Charts. AMIA Annu Symp Proc. 2021 Nov. In Press.

  • Wu DTY, Chen AT, Manning JD, Levy-Fix G, Backonja U, Borland D, Caban JJ, Dowding DW, Hochheiser H, Kaganj V, Kandaswamy S, Kumar M, Nunez A, Pan E, Gotz D*. Evaluating Visual Analytics for Health Informatics Applications: A Systematic Review from the AMIA VIS Working Group Task Force on Evaluation. J Am Med Inform Assoc. 2019 Apr. PMID: 30840080.