Flywheel Case Studies Unlocking Precision Medicine: Streamlining Data Management for Multi-Site Traumatic Brain Injury Research

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	Unlocking Precision Medicine: Streamlining Data Management for Multi-Site Traumatic Brain Injury Research Flywheel

Unlocking Precision Medicine: Streamlining Data Management for Multi-Site Traumatic Brain Injury Research

Flywheel

Technology Category	Analytics & Modeling - Big Data Analytics Platform as a Service (PaaS) - Data Management Platforms
Applicable Industries	Healthcare & Hospitals
Applicable Functions	Product Research & Development
Use Cases	Machine Condition Monitoring Predictive Maintenance
Services	Cloud Planning, Design & Implementation Services Data Science Services
Challenge	Neurologists treating patients with traumatic brain injury (TBI) have long faced a significant challenge: determining which patients with mild or moderate head injuries are at increased future risk of developing neurological problems such as dementia, mood disorders, and Parkinson’s disease, and which are not. Both in classification and outcome assessments, TBI scores are often exclusively symptom-based, and therefore too general to catch some brain injuries and prognoses. To improve the diagnosis, treatment and rehabilitation of patients with TBI, Dr. Geoffrey Manley, Vice Chairman of Neurological Surgery at the University of California, San Francisco, set up the Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACKTBI) study 10 years ago. Today, 19 institutional partners in the TRACK-TBI NETWORK collect more than 3,000 data fields per subject, including outcome measures assessed at four time points post-injury: medical imaging, biospecimen samples, proteome test results and genomic information. Read More
About Customer	The customer in this case study is the Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) study, led by Dr. Geoffrey Manley, Vice Chairman of Neurological Surgery at the University of California, San Francisco. The study was set up 10 years ago with the aim of improving the diagnosis, treatment, and rehabilitation of patients with traumatic brain injury (TBI). The study involves 19 institutional partners in the TRACK-TBI NETWORK, who collectively gather more than 3,000 data fields per subject. These data fields include outcome measures assessed at four time points post-injury, medical imaging, biospecimen samples, proteome test results, and genomic information. The study is designed to create a shared image repository that meets all regulatory requirements, promoting collaboration and acceleration of TBI imaging research. Read More
Solution	The TRACK-TBI leaders engaged Flywheel, a biomedical research data platform, to collaborate on a centralized platform that could aggregate and securely share medical imaging and related data across multiple sites. With Flywheel as their platform, each of the 19 collaborating sites could upload CT and MRI data to a secure, site-specific cloud project where the data was de-identified using Flywheel templates customized for each institution’s unique situation. The configurable patient de-identification capabilities in Flywheel allow researchers to set unique whitelists, blacklists, and other rules to ensure that all patient data is regulatory compliant and ready for research. Flywheel offers tools for high-performance bulk loading and a simple web browser upload with no software installation required. After the data is uploaded, automated processing pipelines ensure that data is quality checked, validated for completeness, and curated for consistency. Once a site’s data has passed a quality control process, it is copied to the main access-controlled TRACK-TBI project area where it is ready for analysis. Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Big Data Analytics

Platform as a Service (PaaS) - Data Management Platforms

Applicable Industries

Healthcare & Hospitals

Applicable Functions

Product Research & Development

Use Cases

Machine Condition Monitoring

Predictive Maintenance

Services

Cloud Planning, Design & Implementation Services

Data Science Services

Challenge

Neurologists treating patients with traumatic brain injury (TBI) have long faced a significant challenge: determining which patients with mild or moderate head injuries are at increased future risk of developing neurological problems such as dementia, mood disorders, and Parkinson’s disease, and which are not. Both in classification and outcome assessments, TBI scores are often exclusively symptom-based, and therefore too general to catch some brain injuries and prognoses. To improve the diagnosis, treatment and rehabilitation of patients with TBI, Dr. Geoffrey Manley, Vice Chairman of Neurological Surgery at the University of California, San Francisco, set up the Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACKTBI) study 10 years ago. Today, 19 institutional partners in the TRACK-TBI NETWORK collect more than 3,000 data fields per subject, including outcome measures assessed at four time points post-injury: medical imaging, biospecimen samples, proteome test results and genomic information.

About Customer

The customer in this case study is the Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) study, led by Dr. Geoffrey Manley, Vice Chairman of Neurological Surgery at the University of California, San Francisco. The study was set up 10 years ago with the aim of improving the diagnosis, treatment, and rehabilitation of patients with traumatic brain injury (TBI). The study involves 19 institutional partners in the TRACK-TBI NETWORK, who collectively gather more than 3,000 data fields per subject. These data fields include outcome measures assessed at four time points post-injury, medical imaging, biospecimen samples, proteome test results, and genomic information. The study is designed to create a shared image repository that meets all regulatory requirements, promoting collaboration and acceleration of TBI imaging research.

Solution

The TRACK-TBI leaders engaged Flywheel, a biomedical research data platform, to collaborate on a centralized platform that could aggregate and securely share medical imaging and related data across multiple sites. With Flywheel as their platform, each of the 19 collaborating sites could upload CT and MRI data to a secure, site-specific cloud project where the data was de-identified using Flywheel templates customized for each institution’s unique situation. The configurable patient de-identification capabilities in Flywheel allow researchers to set unique whitelists, blacklists, and other rules to ensure that all patient data is regulatory compliant and ready for research. Flywheel offers tools for high-performance bulk loading and a simple web browser upload with no software installation required. After the data is uploaded, automated processing pipelines ensure that data is quality checked, validated for completeness, and curated for consistency. Once a site’s data has passed a quality control process, it is copied to the main access-controlled TRACK-TBI project area where it is ready for analysis.

Impact #1	The TRACK-TBI study has now collected and processed large volumes of TBI data on more than 3,000 patients, and has established a multi-modal imaging dataset easily accessible to researchers for precision medicine research.
Impact #2	The study has created a shared image repository meeting all regulatory requirements — essentially an “information commons” to promote collaboration and acceleration of TBI imaging research.
Impact #3	The study has found indications that a technique called neurite orientation dispersion and density imaging (NODDI) may be a more sensitive biomarker for mild TBI than previous methods.

Impact #1

The TRACK-TBI study has now collected and processed large volumes of TBI data on more than 3,000 patients, and has established a multi-modal imaging dataset easily accessible to researchers for precision medicine research.

Impact #2

The study has created a shared image repository meeting all regulatory requirements — essentially an “information commons” to promote collaboration and acceleration of TBI imaging research.

Impact #3

The study has found indications that a technique called neurite orientation dispersion and density imaging (NODDI) may be a more sensitive biomarker for mild TBI than previous methods.

Benefit #1	The study has collected and processed TBI data on more than 3,000 patients.

Benefit #1

The study has collected and processed TBI data on more than 3,000 patients.

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Overview

Unlocking Precision Medicine: Streamlining Data Management for Multi-Site Traumatic Brain Injury Research

Operational Impact

Quantitative Benefit