The movement of people and machines in and around the Emergency Department (ED) is dynamic and can change in a moment's notice.
It's understandably hard to anticipate patient counts for the day and model staff headcount accordingly. TAGNOS’ ED Patient Flow Solution uses the AI application of machine learning combined with real-time locating data to make sense of people and data in constant motion, and provides unified workflows, communication and forecasting to help smooth the unpredictable.
360 degree communication between clinical and ancillary care teams reduce wait times and improve outcomes
Assets available where and when you need them
Mobile decision making improves ED bed turnover and throughput
Predictions help reduce staff overtime and left-without-being-seen (LWBS) issues
Slow, manual communication causes wasted time and delays
Manually entered historical times creates opportunities for inaccurate scheduling and staffing levels
Hunting equipment in-the-moment creates delays in care delivery
Key insights oftentimes not shared with appropriate staff members
Emergency Department Census Prediction
TAGNOS' Al application of Machine Learning provides smart hospitals with the ability to predict census in your ED for the day. We utilize a powerful combination of historical data collection and what's happening real-time to modify future patient census and adjust at least four times a day so staffing levels can be fine-tuned to meet the demands for the upcoming shift; limiting over-time and reducing the risk of under-staffing in your Emergency Department.
Using RTLS (real-time location system) technologies, TAGNOS' solution provides room level tracking the moment the patient receives an RFID wristband -typically at Check-in/Registration. Keep track of the following KPIs from registration to discharge, including:
Bed, Room and Nurse Turnover
Patient Wait Times
Average Length of Stay (LOS)
Patient & Staff Interactions such as Arrival-to-Registration, Room-to-MD
Critical Illness Insights - Stroke, STEMI and Sepsis time to treatment metrics