Overlaying multiple sources of data to identify bottlenecks in clinical workflow

• Published in: Journal of biomedical informatics 2019-01, Vol.100, p.100004-100004, Article 100004
• Main Authors: Vankipuram, Akshay, Patel, Vimla L., Traub, Stephen, Shortliffe, Edward H.
• Format: Article
• Language: English
• Subjects: Clinical workflow; Data analytics; EHR data logs; Emergency department; Quality improvement; RFID
• ISSN: 1532-0464; 1532-0480
• DOI: 10.1016/j.yjbinx.2019.100004

[Display omitted] •Present a data-driven approach to clinical workflow redesign.•Collected data from three disparate data sources: RFID, EHR logs, observations and shadowing.•Detail iterative approach to workflow efficiency improvement.•Correlated EHR usage data to location tracking data to better understand cognitive burdens of physicians.•Methods developed can be used to improve workflow over time or across technological or process changes. The pursuit of increased efficiency and quality of clinical care based on the analysis of workflow has seen the introduction of several modern technologies into medical environments. Electronic health records (EHRs) remain central to analysis of workflow, owing to their wide-ranging impact on clinical processes. The two most common interventions to facilitate EHR-related workflow analysis are automated location tracking using sensor-based technologies and EHR usage data logs. However, to maximize the potential of these technologies, and especially to facilitate workflow redesign, it is necessary to overlay these quantitative findings on the contextual data from qualitative methods such as ethnography. Such a complementary approach promises to yield more precise measures of clinical workflow that provide insights into how redesign could address inefficiencies. In this paper, we categorize clinical workflow in the Emergency Department (ED) into three types (perceived, real and ideal) to create a structured approach to workflow redesign using the available data. We use diverse data sources: sensor-based location tracking through Radio-Frequency Identification (RFID), summary EHR usage data logs, and data from physician interviews augmented by direct observations (through clinician shadowing). Our goal is to discover inefficiencies and bottlenecks that can be addressed to achieve a more ideal workflow state relative to its real and perceived state. We thereby seek to demonstrate a novel data-driven approach toward iterative workflow redesign that generalizes for use in a variety of settings. We also propose types of targeted support or adjustments to offset some of the inefficiencies we noted.