Multiple physical signs detection and decision support system for hospitalized older adults.
Health monitoring systems have rapidly evolved during the past two decades and have the potential to change the way healthcare is currently delivered. Smart monitoring systems automate patient monitoring tasks and thereby improve patient workflow management. Moreover, expert systems have the potential to assist clinicians and improve their performance by accurately executing repetitive tasks, to which humans are ill-suited. Clinicians working in hospital wards are responsible for conducting a multitude of tasks which require constant vigilance, and thus the need for a smart decision support system has arisen. In particular, wireless patient monitoring systems are emerging as a low cost, reliable and accurate means of healthcare delivery. Vital signs monitoring systems are rapidly becoming part of today's healthcare delivery. The paradigm has shifted from traditional and manual recording to computer-based electronic records and, further, to handheld devices as versatile and innovative healthcare monitoring systems. The current study focuses on interpreting multiple physical signs and early warning for hospitalized older adults so that severe consequences can be minimized. Data from a total of 30 patients have been collated in New Zealand hospitals under local and national ethics approvals. The system records blood pressure, heart rate (pulse), oxygen saturation (SpO2), ear temperature and blood glucose levels from hospitalized patients and transfers this information to a web-based software application for remote monitoring and further interpretation. Ultimately, this system is aimed to achieve a high level of agreement with clinicians' interpretation when assessing specific physical signs such as bradycardia, tachycardia, hypertension, hypotension, hypoxaemia, fever and hypothermia to generate early warnings. The performance of the vital signs interpretation system was validated through off-line as well as real-time tests with a high level of agreement between the system and physician.
Department of Electrical and Electronic Engineering, School of Engineering Auckland University of Technology, Private Bag 92006, Auckland 1142, New Zealand.
Mirza Mansoor
MM
Baig
Hamid
H
GholamHosseini
Martin J
MJ
Connolly
D000368:Aged; D000369:Aged, 80 and over; D020000:Decision Support Systems, Clinical; D005260:Female; D006760:Hospitalization; D006801:Humans; D008297:Male; D008991:Monitoring, Physiologic / Q000295:instrumentation / Q000379:methods*; D055986:Vital Signs; D059015:Wireless Technology