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Area of Science:

  • Healthcare Operations Research
  • Clinical Informatics
  • Human Factors Engineering

Background:

  • Analyzing clinical workflow is challenging, especially in dynamic environments, often relying on qualitative methods.
  • Technological interventions like automated location tracking and electronic health records (EHR) are emerging to supplement qualitative analyses with quantifiable metrics.

Purpose of the Study:

  • To present a cohesive framework integrating analytic techniques to complement traditional observations for a deeper understanding of clinical workflow.
  • To enhance the quality, safety, and efficiency of patient care through quantitative workflow metrics.

Main Methods:

  • Developed a framework with three modules: transformation, analysis, and visualization of location-tracking data.
  • Applied theoretically-guided techniques to analyze and visualize data from the Emergency Department (ED) setting.
  • Utilized location-tracking data, moving beyond purely qualitative studies to create quantitative workflow metrics.

Main Results:

  • Presented a series of visualizations derived from location-tracking data collected at the Mayo Clinic ED.
  • Demonstrated a method for deriving quantitative workflow metrics using modern location-tracking technology.
  • The framework aims to provide a deeper understanding of clinical workflow dynamics.

Conclusions:

  • The proposed framework offers a novel approach to analyzing clinical workflow by integrating quantitative data from location tracking.
  • This method has the potential to significantly enhance the quality, safety, and efficiency of care in healthcare settings.
  • While demonstrated in an ED, the methods are expected to generalize to other clinical environments with further validation.