title: Intelligent Emergency Department: Validation of Sociometers to Study Workload.
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.
authors.affiliation: Department of Health Sciences Research, Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, USA.|Clinical Engineering Learning Lab, Mayo Clinic, Rochester, MN, USA.
authors.forename: Denny
authors.initials: D
authors.lastname: Yu
authors.affiliation: Department of Health Sciences Research, Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, USA.|Clinical Engineering Learning Lab, Mayo Clinic, Rochester, MN, USA.
authors.forename: Renaldo C
authors.initials: RC
authors.lastname: Blocker
authors.affiliation: Department of Health Sciences Research, Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, USA.|Clinical Engineering Learning Lab, Mayo Clinic, Rochester, MN, USA.
authors.forename: Mustafa Y
authors.initials: MY
authors.lastname: Sir
authors.affiliation: Department of Health Sciences Research, Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, USA.|Clinical Engineering Learning Lab, Mayo Clinic, Rochester, MN, USA.
authors.forename: M Susan
authors.initials: MS
authors.lastname: Hallbeck
authors.affiliation: Department of Emergency Medicine, Mayo Clinic, Rochester, MN, USA.|Clinical Engineering Learning Lab, Mayo Clinic, Rochester, MN, USA.
authors.forename: Thomas R
authors.initials: TR
authors.lastname: Hellmich
authors.affiliation: Department of Health Sciences Research, Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, USA.
authors.forename: Tara
authors.initials: T
authors.lastname: Cohen
authors.affiliation: Department of Emergency Medicine, Mayo Clinic, Rochester, MN, USA.|Clinical Engineering Learning Lab, Mayo Clinic, Rochester, MN, USA.
authors.forename: David M
authors.initials: DM
authors.lastname: Nestler
authors.affiliation: Department of Health Sciences Research, Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, USA. pasupathyk@mayo.edu.|Clinical Engineering Learning Lab, Mayo Clinic, Rochester, MN, USA. pasupathyk@mayo.edu.
authors.forename: Kalyan S
authors.initials: KS
authors.lastname: Pasupathy
mesh_terms: D017598:Efficiency, Organizational; D004636:Emergency Service, Hospital / Q000458:organization & administration* / Q000592:standards; D006801:Humans; D011787:Quality of Health Care / Q000458:organization & administration*; D058998:Remote Sensing Technology / Q000295:instrumentation / Q000379:methods*; D015203:Reproducibility of Results; D016526:Workload
keywords: Clinical engineering learning lab; Emergency department; Information and communication technology (ICT); Intelligent healthcare; Sensor technology