Making sense: Sensor-based investigation of clinician activities in complex critical care environments

  • Authors:

  • Thomas Kannampallil;Zhe Li;Min Zhang;Trevor Cohen;David J. Robinson;Amy Franklin;Jiajie Zhang;Vimla L. Patel

  • Affiliations:

  • Center for Cognitive Informatics and Decision Making, School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, United States;Center for Cognitive Informatics and Decision Making, School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, United States;Center for Cognitive Informatics and Decision Making, School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, United States;Center for Cognitive Informatics and Decision Making, School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, United States;Center for Cognitive Informatics and Decision Making, School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, United States;Center for Cognitive Informatics and Decision Making, School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, United States;Center for Cognitive Informatics and Decision Making, School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, United States;Center for Cognitive Informatics and Decision Making, School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, United States

  • Venue:
  • Journal of Biomedical Informatics
  • Year:
  • 2011

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Abstract

In many respects, the critical care workplace resembles a paradigmatic complex system: on account of the dynamic and interactive nature of collaborative clinical work, these settings are characterized by non-linear, inter-dependent and emergent activities. Developing a comprehensive understanding of the work activities in critical care settings enables the development of streamlined work practices, better clinician workflow and most importantly, helps in the avoidance of and recovery from potential errors. Sensor-based technology provides a flexible and viable way to complement human observations by providing a mechanism to capture the nuances of certain activities with greater precision and timing. In this paper, we use sensor-based technology to capture the movement and interactions of clinicians in the Trauma Center of an Emergency Department (ED). Remarkable consistency was found between sensor data and human observations in terms of clinician locations and interactions. With this validation and greater precision with sensors, ED environment was characterized in terms of (a) the degree of randomness or entropy in the environment, (b) the movement patterns of clinicians, (c) interactions with other clinicians and finally, (d) patterns of collaborative organization with team aggregation and dispersion. Based on our results, we propose three opportunities for the use of sensor technologies in critical care settings: as a mechanism for real-time monitoring and analysis for ED activities, education and training of clinicians, and perhaps most importantly, investigating the root-causes, origins and progression of errors in the ED. Lessons learned and the challenges encountered in designing and implementing the sensor technology sensor data are discussed.