A method for the analysis and visualization of clinical workflow in dynamic environments

• Published in: Journal of biomedical informatics 2018-03, Vol.79, p.20-31
• Main Authors: Vankipuram, Akshay, Traub, Stephen, Patel, Vimla L.
• Format: Article
• Language: English
• Subjects: Algorithms; Arizona; Clinical informatics; Clinical workflow; Computers; Data Collection; Electronic Health Records; Emergency Department; Emergency Medicine - instrumentation; Emergency Service, Hospital; Humans; Medical Informatics - methods; Pattern Recognition, Automated; Physicians; Probabilistic modeling; Probability; Radio Frequency Identification Device; Radio Waves; Reproducibility of Results; Visualization; Workflow
• ISSN: 1532-0464; 1532-0480
• DOI: 10.1016/j.jbi.2018.01.007

[Display omitted] •Clinical workflow analytics framework using Radio-Frequency Identification (RFID).•Used the emergency department at the Mayo Clinic as a case study.•Three phases of data flow: transformation, analysis, and visualization.•Created quantitative techniques to complement traditional qualitative methods.•Goal was to develop generalizable methods for critical care environments. The analysis of clinical workflow offers many challenges, especially in settings characterized by rapid dynamic change. Typically, some combination of approaches drawn from ethnography and grounded theory-based qualitative methods are used to develop relevant metrics. Medical institutions have recently attempted to introduce technological interventions to develop quantifiable quality metrics to supplement existing purely qualitative analyses. These interventions range from automated location tracking to repositories of clinical data (e.g., electronics health record (EHR) data, medical equipment logs). Our goal in this paper is to present a cohesive framework that combines a set of analytic techniques that can potentially complement traditional human observations to derive a deeper understanding of clinical workflow and thereby to enhance the quality, safety, and efficiency of care offered in that environment. We present a series of theoretically-guided techniques to perform analysis and visualization of data developed using location tracking, with illustrations using the Emergency Department (ED) as an example. Our framework is divided into three modules: (i) transformation, (ii) analysis, and (iii) visualization. We describe the methods used in each of these modules, and provide a series of visualizations developed using location-tracking data collected at the Mayo Clinic ED (Phoenix, AZ). Our innovative analytics go beyond qualitative study, and includes user data collected from a relatively modern but increasingly ubiquitous technique of location tracking, with the goal of creating quantitative workflow metrics. Although we believe that the methods we have developed will generalize well to other settings, additional work will be required to demonstrate their broad utility beyond our single study environment.