Unlocking Precision Medicine: Streamlining Data Management for Multi-Site Traumatic Brain Injury Research
Company Size
1,000+
Region
- America
Country
- United States
Product
- Flywheel
Tech Stack
- Cloud-based platform
- Automated processing pipelines
- Machine learning pipelines
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Innovation Output
- Productivity Improvements
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
About The 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.
The 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.
The 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.
Operational Impact
Quantitative Benefit
Case Study missing?
Start adding your own!
Register with your work email and create a new case study profile for your business.
Related Case Studies.
Case Study
Hospital Inventory Management
The hospital supply chain team is responsible for ensuring that the right medical supplies are readily available to clinicians when and where needed, and to do so in the most efficient manner possible. However, many of the systems and processes in use at the cancer center for supply chain management were not best suited to support these goals. Barcoding technology, a commonly used method for inventory management of medical supplies, is labor intensive, time consuming, does not provide real-time visibility into inventory levels and can be prone to error. Consequently, the lack of accurate and real-time visibility into inventory levels across multiple supply rooms in multiple hospital facilities creates additional inefficiency in the system causing over-ordering, hoarding, and wasted supplies. Other sources of waste and cost were also identified as candidates for improvement. Existing systems and processes did not provide adequate security for high-cost inventory within the hospital, which was another driver of cost. A lack of visibility into expiration dates for supplies resulted in supplies being wasted due to past expiry dates. Storage of supplies was also a key consideration given the location of the cancer center’s facilities in a dense urban setting, where space is always at a premium. In order to address the challenges outlined above, the hospital sought a solution that would provide real-time inventory information with high levels of accuracy, reduce the level of manual effort required and enable data driven decision making to ensure that the right supplies were readily available to clinicians in the right location at the right time.
Case Study
Gas Pipeline Monitoring System for Hospitals
This system integrator focuses on providing centralized gas pipeline monitoring systems for hospitals. The service they provide makes it possible for hospitals to reduce both maintenance and labor costs. Since hospitals may not have an existing network suitable for this type of system, GPRS communication provides an easy and ready-to-use solution for remote, distributed monitoring systems System Requirements - GPRS communication - Seamless connection with SCADA software - Simple, front-end control capability - Expandable I/O channels - Combine AI, DI, and DO channels
Case Study
Driving Digital Transformations for Vitro Diagnostic Medical Devices
Diagnostic devices play a vital role in helping to improve healthcare delivery. In fact, an estimated 60 percent of the world’s medical decisions are made with support from in vitrodiagnostics (IVD) solutions, such as those provided by Roche Diagnostics, an industry leader. As the demand for medical diagnostic services grows rapidly in hospitals and clinics across China, so does the market for IVD solutions. In addition, the typically high cost of these diagnostic devices means that comprehensive post-sales services are needed. Wanteed to improve three portions of thr IVD:1. Remotely monitor and manage IVD devices as fixed assets.2. Optimizing device availability with predictive maintenance.3. Recommending the best IVD solution for a customer’s needs.
Case Study
HaemoCloud Global Blood Management System
1) Deliver a connected digital product system to protect and increase the differentiated value of Haemonetics blood and plasma solutions. 2) Improve patient outcomes by increasing the efficiency of blood supply flows. 3) Navigate and satisfy a complex web of global regulatory compliance requirements. 4) Reduce costly and labor-intensive maintenance procedures.
Case Study
Harnessing real-time data to give a holistic picture of patient health
Every day, vast quantities of data are collected about patients as they pass through health service organizations—from operational data such as treatment history and medications to physiological data captured by medical devices. The insights hidden within this treasure trove of data can be used to support more personalized treatments, more accurate diagnosis and more advanced preparative care. But since the information is generated faster than most organizations can consume it, unlocking the power of this big data can be a struggle. This type of predictive approach not only improves patient care—it also helps to reduce costs, because in the healthcare industry, prevention is almost always more cost-effective than treatment. However, collecting, analyzing and presenting these data-streams in a way that clinicians can easily understand can pose a significant technical challenge.
