Wireless Sensor Network Technology for Clinical Monitoring

Wireless Sensor Network Technology for Clinical Monitoring

Department of Computer Science and Engineering, School of Engineering, Washington University
Department of Medicine, School of Medicine, Washington University
BJC HealthCare
Institute of Clinical and Translational Sciences, Washington University

Team

Faculty: Tom Bailey, Yixin Chen, Marin Kollef, Chenyang Lu

PhD Students: Rahav Dor, Bo Li

Alumni: Chris Brooks, Octav Chipara, Greg Hackmann, Yi Mao, Catalin Roman, Zhicheng Yang

Collaborator: Roger Chamberlain

Real-time patient monitoring will enable timely prediction of clinical deterioration of non-ICU inpatients. A recent clinical study has shown that an automated electronic scoring system integrated with electronic medical records can reduce the delay in recognizing clinical deterioration and activating rapid response teams. To provide timely care of patients with deteriorating conditions, it is essential to update existing electronic medical records with real-time sensor data continuously collected from patients. To meet this challenge we are developing and evaluating novel wireless sensor network technologies for real-time patient monitoring. Each wireless sensor used in this study consists of an embedded computer for on-board processing, a radio interface for wireless communication, and a pulse oximeter for collecting heart rate (HR) and oxygen saturation (SpO2). Wireless sensors attached to patients will continuously collect sensor data and transmit them to an electronic medical record system over a wireless network.

The project is organized in three phases: (1) development and evaluation of wireless network protocols for reliable data collection on an existing wireless sensor network testbed at the Department of Computer Science & Engineering (CSE); (2) integration and evaluation of pulse oximeters with wireless sensor networks on the CSE testbed; (3) trials with patients in a clinic at the BJC HeathCare.

The wireless sensor network technology we are developing has three novel features: (1) new network protocols that guarantee reliable data collection from (mobile or stationary) patients; (2) an open and flexible software platform that allows medical experts to plug in different data processing algorithms and sensors; (3) seamless integration of real-time sensor data into electronic medical record systems.

News Coverage

Wireless Sensors Relay Medical Insight to Patients and Caregivers, IEEE Signal Processing Magazine, 29(3): 8-12, May 2012.

Washington University Researchers Seek to Bring Mobility to ICU Patients, RFID Journal, August 2011.

Mesh Network Monitors Patients in Virtual ICU, British Journal of Healthcare Computing, August 2011.

Hospital Tests Wireless Patient Monitoring, UPI, August 2011.

Clinical Warning System Could Change Healthcare, Nurse.com, August 2011.

St. Louis Hospital Tests Wireless System That Monitors Vital Signs, iHealthBeat, Augist 2011.

Wireless Network in Hospital Monitors Vital Signs, Washington University News Release, August 2011.

Papers

T. Bailey, Y. Chen, Y. Mao, C. Lu, G. Hackmann, S.T. Micek, K. Heard, K. Faulkner, M.H. Kollef, A Trial of a Real-Time Alert for Clinical Deterioration in Patients Hospitalized on General Medical Wards, Journal of Hospital Medicine, accepted.

R. Dor, G. Hackmann, Z. Yang, C. Lu, Y. Chen, M. Kollef and T.C. Bailey, Experiences with an End-To-End Wireless Clinical Monitoring System, Conference on Wireless Health (WH'12), October 2012.

Y. Mao, W. Chen, Y. Chen, C. Lu, M. Kollef and T.C. Bailey, An Integrated Data Mining Approach to Real-time Clinical Monitoring and Deterioration Warning, ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'12), August 2012.

Y. Mao, Y. Chen, G. Hackmann, M. Chen, C. Lu, M. Kollef and T.C. Bailey, Early Deterioration Warning for Hospitalized Patients by Mining Clinical Data, International Journal of Knowledge Discovery in Bioinformatics, 2(3):1-20, 2012. (Extended version of the BioDM'11 paper.)

Y. Mao, Y. Chen, G. Hackmann, M. Chen, C. Lu, M. Kollef and T.C. Bailey, Medical Data Mining for Early Deterioration Warning in General Hospital Wards, ICDM Workshop on Biological Data Mining and its Applications in Healthcare (BioDM'11), December 2011.

G. Hackmann, M. Chen, O. Chipara, C. Lu, Y. Chen, M. Kollef and T.C. Bailey, Toward a Two-Tier Clinical Warning System for Hospitalized Patients, American Medical Informatics Association Annual Symposium (AMIA'11), October 2011.

O. Chipara, C. Lu, T.C. Bailey and G.-C. Roman, Reliable Clinical Monitoring using Wireless Sensor Networks: Experience in a Step-down Hospital Unit, ACM Conference on Embedded Networked Sensor Systems (SenSys'10), November 2010.

J. Ko, C. Lu, M.B. Srivastava, J.A. Stankovic, A. Terzis and M. Welsh, Wireless Sensor Networks for Healthcare, Proceedings of IEEE, 98(11): 1947 - 1960, November 2010.

O. Chipara, G.Hackmann, C. Lu, W. Smart and G.-C. Roman, Practical Modeling and Prediction of Radio Coverage in Indoor Sensor Networks, ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN'10), April 2010.

O. Chipara, C. Brooks, S. Bhattacharya, C. Lu, R.D. Chamberlain, G.-C. Roman, and T.C. Bailey, Reliable Real-time Clinical Monitoring Using Sensor Network Technology, American Medical Informatics Association (AMIA) Annual Symposium, November 2009.

Talks

Toward a Two-Tier Clinical Warning System for Hospitalized Patients, American Medical Informatics Association Annual Symposium (AMIA'11), October 2011.

Reliable Clinical Monitoring using Wireless Sensor Networks: Experience in a Step-down Hospital Unit, ACM Conference on Embedded Networked Sensor Systems (SenSys'10), November 2010.

Toward Wireless Clinical Monitoring in General Hospital Units, PRECISE Seminar, University of Pennsylvania, October 2010.

Acknowledgement
The project described was supported by Award Number UL1RR024992 from the National Center For Research Resources and BJH Foundation. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Center for Research Resources or the National Institutes of Health.