The Emergency Care Systems Lab engages in projects ranging from developing predictive analytics and clinical decision support tools to conducting community-engaged research and quality improvement initiatives. We partner with local and national organizations to ensure our innovations are grounded in real-world applications and provide measurable impacts in emergency care.
Some of our projects include:
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Multi-site Trial to Improve Dementia Care in Emergency Department (ED-LEAD)
The study involves an 80-site pragmatic clinical trial testing three different interventions to improve emergency care for patients living with dementia who are discharged home. One of the three interventions is the community paramedic-led transition intervention (CPTI) previously developed and tested here by Dr. Manish N. Shah and his research team.
The overall budget for the Emergency Departments LEading the Transformation of Alzheimer’s and Dementia care (ED-LEAD) study is $55 million over five years. This investment appropriately reflects the urgency of improving emergency care for the nearly seven million people in the U.S. living with dementia. Most importantly, the ED-LEAD study’s ambitious and attainable objectives provide significant potential to guide improved emergency care practices for patients with dementia.
Human Factors and Simulation to Inform East Madison Hospital Redesign
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Multi-site Implementation of Clinical Decision Support to Prevent Future Falls (R18)
This project focuses on reducing the risk of falls in older adults following emergency department (ED) visits, a leading cause of injury in this population. Leveraging advanced technology, the project implements an innovative Clinical Decision Support (CDS) system that integrates with existing electronic health records (EHR) to identify older adults at high risk of future falls.
The CDS automatically scans EHR data during an ED visit, analyzing variables like patient history, demographics, and health indicators to assess fall risk. If the system detects a high risk of falling within the next six months, it triggers an alert for the ED clinician. The alert is paired with a recommendation to refer the patient to a specialized, multidisciplinary falls prevention clinic. This process is seamless, minimizing additional workload for ED physicians and staff.
The system’s adaptive design allows for real-time adjustment based on clinic availability, ensuring that high-risk patients are always prioritized for referral. Currently, this approach is being tested at three UW Health emergency departments. By optimizing referral rates and improving preventive care coordination, the project aims to demonstrate the CDS system’s effectiveness in reducing future fall-related injuries and promoting broader adoption across health care settings.
Predicting Surges in the Emergency Department Census at East Madison Hospital
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Informatics to Improve Prehospital Care
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Surveillance and Responsive Training (WIRED-RT)
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Human Factors to Improve Older Adult Care Transitions from the Emergency Department (Patient Safety Learning Lab)
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Human Factors and Predictive Analytics to Improve Patient Flow
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