Knowing threshold values would have to be set low to avoid "alert fatigue," a major problem in hospitals, they decided to integrate vital signs with data in the patients' electronic medical records in the next-generation sensor network.
"Overall, the prototype trial showed that wireless sensor networks can successfully monitor vital signs to support real-time detection of clinical deterioration in patients," Lu says.
To the future and beyond
The next-generation network is currently being developed and tested at the hospital. In the first tier of this two-tier system, a computer running a machine-learning algorithm calls nursing staff if new clinical data in an electronic medical record indicate a patient enrolled in the trial is at risk. The second-tier warning system, currently under development, will identify clinical deterioration based on both real-time data collected by the wireless sensors and regular clinical data in electronic medical records.
In addition to Bailey, who is the principal investigator for the clinical warning system trial, other faculty members on the team include Lu, Yixin Chen, PhD, associate professor of computer science in the School of Engineering & Applied Science, who developed the machine-learning algorithm; Marin H. Kollef, MD, professor of medicine (pulmonary diseases) and director of the medical intensive care unit; Scott Micek, PharmD, a clinical pharmacist at Barnes-Jewish and a faculty member at St. Louis College of Pharmacy; and Gruia-Catalin Roman, PhD, the former chair of the computer science department whose enthusiasm for distributed sensor networks helped inspire the project.
According to Lu, it won't be long before any patient with a serious medical condition, such as diabetes or asthma, will wear a wireless medical device that will allow them to monitor their own vital signs on a smartphone that will also call relati
|Contact: Diana Lutz|
Washington University in St. Louis