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Journal of biomedical informatics v.79, 2018년, pp.20 - 31   SCI SCIE
본 등재정보는 저널의 등재정보를 참고하여 보여주는 베타서비스로 정확한 논문의 등재여부는 등재기관에 확인하시기 바랍니다.

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

Vankipuram, Akshay (Department of Biomedical Informatics, Arizona State University, Scottsdale, AZ, United States ) ; Traub, Stephen (Mayo Clinic, Phoenix, AZ, United States ) ; Patel, Vimla L. (Department of Biomedical Informatics, Arizona State University, Scottsdale, AZ, United States ) ;
  • 초록  

    Abstract 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. Highlights 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. Graphical abstract [DISPLAY OMISSION]


  • 주제어

    Clinical workflow .   Probabilistic modeling .   Visualization .   Clinical informatics .   Emergency Department.  

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