Last year the Medical University of South Carolina (MUSC) www.musc.edu and Advanced ICU Care (AICU) www.icumedicine.com launched a telemedicine partnership that provides a new model of ICU care to patients in the state.
The partnership was made possible when the state provided a multi-million dollar allocation to MUSC that included telemedicine services. The public private partnership provides access to ICU care via telemedicine to ICUs by physicians and nurses including 24/7 monitoring for the most critical patients.
“Since MUSC’s ICUs are often at full occupancy, we can’t provide timely transfers from community hospitals. With the tele-ICU, we can provide the right care for the most severely ill patients with our 24/7 support available to physicians and nurses treating ICU patients,” said Dee Ford, MD, MUSC Health Tele-ICU Director.
Programs based on the AICU Care model have reduced mortality by 40 percent, increased patient growth by 17 percent, and increased the number of patients moving through the hospital by 25 percent, and this has enabled hospitals to serve more patients without adding beds.
In the State of Georgia, Emory University Hospital www.emoryhealthcare.org using software from IBM www.ibm.com and Excel Medical Electronics (EME) www.excel-medical.com have created the Smart ICU to provide advanced, predictive medical care for critically care patients through real-time streaming analytics. Emory is testing a system that can identify patterns in physiological data and instantly alert clinicians to danger signs in patients.
Professor of Surgery and Anesthesiology Tim Buchman Founding Director for Emory’s Critical Care Center http://emoryhealthcare.org/critical-care is in charge of integrating the ICUs throughout Emory Healthcare. He reports that making the best decision for each patient needs and depends on using “Big Data”.
Harvested information and real-time analytics along with a display of the data, allows nurses and doctors to detect how organs are interacting with each other. Technically, sensors located on patients and on support devices sends both patient and alarm data to multiple locations and to software programs. At this point, the harvesting of big data enables real-time analysis to be possible.