UCSF-BASIC uses DataRobot and Operating Room Data to Predict the Outcomes of Patients with Traumatic Spinal Cord Injuries
Customer Company Size
Large Corporate
Region
- America
Country
- United States
Product
- DataRobot
Tech Stack
- Predictive Analytics
- Automated Feature Discovery
Implementation Scale
- Departmental Deployment
Impact Metrics
- Productivity Improvements
- Customer Satisfaction
Technology Category
- Analytics & Modeling - Predictive Analytics
Applicable Industries
- Healthcare & Hospitals
Applicable Functions
- Product Research & Development
Use Cases
- Predictive Maintenance
- Remote Patient Monitoring
Services
- Data Science Services
About The Customer
The customer in this case study is the University of California, San Francisco's Transforming Research and Clinical Knowledge in Spinal Cord Injury (TRACK-SCI) team. This interdisciplinary group of researchers and clinicians is part of the Brain and Spinal Cord Injury Center at UCSF. Their mission is to produce high-quality research that examines current clinical practices and improve care through their collaboration with clinicians. They are particularly interested in how blood pressure management during operating procedures affects a patient’s likelihood to recover from a spinal cord injury.
The Challenge
The University of California, San Francisco's Transforming Research and Clinical Knowledge in Spinal Cord Injury (TRACK-SCI) team is dedicated to improving patient care for individuals with traumatic spinal cord injuries. Each year, there are 17,000 cases of spinal cord injury (SCI) in the United States, often resulting in permanent challenges such as paralysis and sensory dysfunction. The estimated lifetime costs for each individual patient can range from just over $1 million to nearly $5 million. Acute clinical decisions made throughout SCI patient care, such as during surgery and ICU management, are critical for setting a patient up for recovery. However, clinicians lack guidance developed through data-driven research. One area of particular interest to the TRACK-SCI team is how blood pressure management during operating procedures affects a patient’s likelihood to recover.
The Solution
The TRACK-SCI team partnered with DataRobot to tackle this research challenge and improve the guidelines for blood pressure management in the operating room for SCI patients. Using patient data that included the nature and location of the injury, along with time-series readings of blood pressure and heart rate taken every five minutes during surgery, the TRACK-SCI team used Automated Feature Discovery in DataRobot to discover novel new features and built predictive models to determine if a patient would improve. Using predictive analytics from DataRobot, the TRACK-SCI team performed a rigorous examination of the impact of many variables on outcomes for patients with SCI. One novel insight was that time spent in high blood pressure regimes has a significant impact on a patient’s likelihood to improve.
Operational Impact
Quantitative Benefit
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