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Intelligent Emergency Department: Validation of Sociometers to Study Workload

  • Denny Yu
  • Renaldo C. Blocker
  • Mustafa Y. Sir
  • M. Susan Hallbeck
  • Thomas R. Hellmich
  • Tara Cohen
  • David M. Nestler
  • Kalyan S. PasupathyEmail author
Mobile Systems
Part of the following topical collections:
  1. Smart Living in Healthcare and Innovations

Abstract

Sociometers are wearable sensors that continuously measure body movements, interactions, and speech. The purpose of this study is to test sociometers in a smart environment in a live clinical setting, to assess their reliability in capturing and quantifying data. The long-term goal of this work is to create an intelligent emergency department that captures real-time human interactions using sociometers to sense current system dynamics, predict future state, and continuously learn to enable the highest levels of emergency care delivery. Ten actors wore the devices during five simulated scenarios in the emergency care wards at a large non-profit medical institution. For each scenario, actors recited prewritten or structured dialogue while independent variables, e.g., distance, angle, obstructions, speech behavior, were independently controlled. Data streams from the sociometers were compared to gold standard video and audio data captured by two ward and hallway cameras. Sociometers distinguished body movement differences in mean angular velocity between individuals sitting, standing, walking intermittently, and walking continuously. Face-to-face (F2F) interactions were not detected when individuals were offset by 30°, 60°, and 180° angles. Under ideal F2F conditions, interactions were detected 50 % of the time (4/8 actor pairs). Proximity between individuals was detected for 13/15 actor pairs. Devices underestimated the mean duration of speech by 30–44 s, but were effective at distinguishing the dominant speaker. The results inform engineers to refine sociometers and provide health system researchers a tool for quantifying the dynamics and behaviors in complex and unpredictable healthcare environments such as emergency care.

Keywords

Sensor technology Information and communication technology (ICT) Clinical engineering learning lab Emergency department Intelligent healthcare 

Notes

Acknowledgments

The authors would like to thank all the volunteer actors that participated in this study and Kelly Herbst and Kyle Koenig for their administrative and technical support. This work is funded in part by the Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery.

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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Denny Yu
    • 1
    • 3
  • Renaldo C. Blocker
    • 1
    • 3
  • Mustafa Y. Sir
    • 1
    • 3
  • M. Susan Hallbeck
    • 1
    • 3
  • Thomas R. Hellmich
    • 2
    • 3
  • Tara Cohen
    • 1
  • David M. Nestler
    • 2
    • 3
  • Kalyan S. Pasupathy
    • 1
    • 3
    Email author
  1. 1.Department of Health Sciences Research, Kern Center for the Science of Health Care DeliveryMayo ClinicRochesterUSA
  2. 2.Department of Emergency MedicineMayo ClinicRochesterUSA
  3. 3.Clinical Engineering Learning LabMayo ClinicRochesterUSA

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