DataRobot Case Studies UCSF-BASIC uses DataRobot and Operating Room Data to Predict the Outcomes of Patients with Traumatic Spinal Cord Injuries

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	UCSF-BASIC uses DataRobot and Operating Room Data to Predict the Outcomes of Patients with Traumatic Spinal Cord Injuries DataRobot

UCSF-BASIC uses DataRobot and Operating Room Data to Predict the Outcomes of Patients with Traumatic Spinal Cord Injuries

DataRobot

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
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. Read More
About 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. Read More
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. Read More Log in to view content
Contents

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

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.

About 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.

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.

Impact #1	The TRACK-SCI team was able to use DataRobot to discover novel new features and build predictive models to determine if a patient would improve.
Impact #2	The team performed a rigorous examination of the impact of many variables on outcomes for patients with SCI.
Impact #3	One novel insight was that time spent in high blood pressure regimes has a significant impact on a patient’s likelihood to improve.

Impact #1

The TRACK-SCI team was able to use DataRobot to discover novel new features and build predictive models to determine if a patient would improve.

Impact #2

The team performed a rigorous examination of the impact of many variables on outcomes for patients with SCI.

Impact #3

One novel insight was that time spent in high blood pressure regimes has a significant impact on a patient’s likelihood to improve.

Benefit #1	The use of DataRobot's predictive analytics allowed the team to gain novel insights into the impact of blood pressure management on patient recovery.
Benefit #2	The insights gained will be used to develop guidelines for patient care, potentially improving outcomes for future SCI patients.

Benefit #1

The use of DataRobot's predictive analytics allowed the team to gain novel insights into the impact of blood pressure management on patient recovery.

Benefit #2

The insights gained will be used to develop guidelines for patient care, potentially improving outcomes for future SCI patients.

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Overview

UCSF-BASIC uses DataRobot and Operating Room Data to Predict the Outcomes of Patients with Traumatic Spinal Cord Injuries

Operational Impact

Quantitative Benefit