Intelligent Emergency Department: Validation of Sociometers to Study Workload

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• 作者：Denny Yu ; Renaldo C. Blocker ; Mustafa Y. Sir…
• 关键词：Sensor technology ; Information and communication technology (ICT) ; Clinical engineering learning lab ; Emergency department ; Intelligent healthcare
• 刊名：Journal of Medical Systems
• 出版年：2016
• 出版时间：March 2016
• 年：2016
• 卷：40
• 期：3
• 全文大小：1,613 KB
• 参考文献：1.Derlet, R. W., Richards, J. R., and Kravitz, R. L., Frequent overcrowding in US emergency departments. Acad. Emerg. Med. 8(2):151鈥?55, 2001.CrossRef PubMed
  2.Welch, S. J., Jones, S. S., and Allen, T., Mapping the 24-hour emergency department cycle to improve patient flow. Jt Comm. J. Qual. Patient Saf. 33(5):247鈥?55, 2007.PubMed
  3.Pines, J. M., et al., The effect of emergency department crowding on patient satisfaction for admitted patients. Acad. Emerg. Med. 15(9):825鈥?31, 2008.CrossRef PubMed
  4.Bragard, I., G. Dupuis, and R. Fleet, Quality of work life, burnout, and stress in emergency department physicians: a qualitative review. Eur. J. Emergency Med : Off. J. Eur. Soc. Emergency Med., 2014.
  5.Hunsaker, S., et al., Factors That Influence the Development of Compassion Fatigue, Burnout, and Compassion Satisfaction in Emergency Department Nurses. J. Nurs Scholarsh, 2015.
  6.Plsek, P. E., and Greenhalgh, T., Complexity science: The challenge of complexity in health care. BMJ: Br. Med. J. 323(7313):625, 2001.CrossRef
  7.Sanders, M. S. and E. J. McCormick, Human factors in engineering and design: McGRAW-HILL book company, 1987.
  8.Gawande, A. A., et al., Analysis of errors reported by surgeons at three teaching hospitals. Surgery 133:614鈥?21, 2003.CrossRef PubMed
  9.Commission, J., Sentinel event data鈥攔oot causes by event type (2004鈥?013). 2013, 2014.
  10.Lingard, L., et al., Communication failures in the operating room: an observational classification of recurrent types and effects. Qual. Saf. Health Care 13(5):330鈥?34, 2004.PubMedCentral CrossRef PubMed
  11.Yao, W., Chu, C.-H., and Li, Z., The adoption and implementation of RFID technologies in healthcare: A literature review. J. Med. Syst. 36(6):3507鈥?525, 2012.CrossRef PubMed
  12.脴stbye, T., et al., Evaluation of an infrared/radiofrequency equipment-tracking system in a tertiary care hospital. J. Med. Syst. 27(4):367鈥?80, 2003.CrossRef PubMed
  13.Chowdhury, B. and R. Khosla. RFID-based Hospital Real-time Patient Management System. in Computer and Information Science, 2007. ICIS 2007. 6th IEEE/ACIS International Conference on. 2007.
  14.Darwish, A., and Hassanien, A. E., Wearable and implantable wireless sensor network solutions for healthcare monitoring. Sensors 11(6):5561鈥?595, 2011.PubMedCentral CrossRef PubMed
  15.Paksuniemi, M., et al. Wireless sensor and data transmission needs and technologies for patient monitoring in the operating room and intensive care unit. in Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005. 27th Annual International Conference of the. 2005.
  16.Alemdar, H., and Ersoy, C., Wireless sensor networks for healthcare: A survey. Comput. Netw. 54(15):2688鈥?710, 2010.CrossRef
  17.Corchado, J. M., et al., Using heterogeneous wireless sensor networks in a telemonitoring system for healthcare. IEEE Trans. Inf. Technol. Biomed 14(2):234鈥?40, 2010.CrossRef PubMed
  18.Yang, H., and Robert, F., Wireless body sensor networks for health-monitoring applications. Physiol. Meas. 29(11):R27, 2008.CrossRef
  19.Tunca, C., et al., Multimodal wireless sensor network-based ambient assisted living in real homes with multiple residents. Sensors 14(6):9692鈥?719, 2014.PubMedCentral CrossRef PubMed
  20.Choudhury, T. and A. Pentland. The sociometer: A wearable device for understanding human networks. In: CSCW鈥?2 Workshop: Ad hoc Communications and Collaboration in Ubiquitous Computing Environments. 2002.
  21.Pentland, A., The new science of building great teams. Harv. Bus. Rev. 90(4):60鈥?9, 2012.
  22.Olgu铆n, D.O., P.A. Gloor, and A. Pentland. Wearable sensors for pervasive healthcare management. In: Pervasive Computing Technologies for Healthcare, 2009. PervasiveHealth 2009. 3rd International Conference on. IEEE. 2009.
  23.Onnela, J.-P., et al., Using sociometers to quantify social interaction patterns. Sci. Rep. 4. 2014.
  24.Yu, D., et al., Wearable sociometers in chaotic simulated environments. J. Med. Devices 9(2):020946鈥?20946, 2015.
  25.Lingard, L., et al., Team communications in the operating room: Talk patterns, sites of tension, and implications for novices. Acad. Med. 77(3):232鈥?37, 2002.CrossRef PubMed
  26.Jason, A., et al., Defining patient experience. Patient Experience J. 1(1):7鈥?9, 2014.
  27.Beach, M. C., et al., Do patients treated with dignity report higher satisfaction, adherence, and receipt of preventive care? Ann. Family Med 3(4):331鈥?38, 2005.CrossRef
  28.Ferrer, A., Multivariate Statistical Process Control Based on Principal Component Analysis (MSPC-PCA): Some reflections and a case study in an autobody assembly process. Qual. Eng. 19(4):311鈥?25, 2007.CrossRef
  29.Ferrer, A., Latent structures-based multivariate statistical process control: A paradigm shift. Qual. Eng. 26(1):72鈥?1, 2014.CrossRef
  30.Montgomery, D.C., Introduction to statistical quality control. 7th ed2013, Hoboken, NJ: Wiley. xiv, 754 p.
  31.Kourti, T., Application of latent variable methods to process control and multivariate statistical process control in industry. Int. J. Adap Control. Signal Process 19(4):213鈥?46, 2005.CrossRef
  32.Kourti, T. and J.F. MacGregor, Multivariate SPC methods for process and product monitoring. Journal of Quality Technology, 28(4). 1996.
  33.Mitchell, T. M., Machine Learning. McGraw-Hill, Inc, New York, 1997.
  34.Rokach, L. and O. Maimon, Data Mining with Decision Trees: Theory and Applications. Series in Machine Perception and Artifical Intelligence2014: World Scientific Publishing Company Pte Limited.
  35.Medina, B., A., and Pasupathy, K. Uncovering complex relationships in system dynamics modeling: Exploring the use of CHAID and CART. Paper presented at the system dynamics society international conference: Boston, MA, 2007.
  36.Pasupathy, K. S., and Barker, L. M. Impact of fatigue on performance in registered nurses: Data mining and implications for practice. Journal for Healthcare Quality, 34(5):22鈥?0, 2012.
  37.Brown, G., et al., Health Informatics: A systems perspective. Chicago, IL: Health Administration Press, 2012.
  38.Parhizi, S., et al., Mining the relationships between psychosocial factors and fatigue dimensions among registered nurses. International Journal of Industrial Ergonomics, 43(1):82鈥?0, 2013.
  
• 作者单位：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)
  
  1. Department of Health Sciences Research, Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, USA
  3. Clinical Engineering Learning Lab, Mayo Clinic, Rochester, MN, USA
  2. Department of Emergency Medicine, Mayo Clinic, Rochester, MN, USA
  
• 刊物类别：Mathematics and Statistics
• 刊物主题：Statistics
  Statistics for Life Sciences, Medicine and Health Sciences
  Health Informatics and Administration
  
• 出版者：Springer Netherlands
• ISSN：1573-689X

文摘

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掳, 60掳, and 180掳 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鈥?4 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