Automating Workflows in Stanford’s Brain Stimulation Lab
Company Size
1,000+
Region
- America
Country
- United States
Product
- Flywheel
- Flywheel Gears
- Flywheel SDK
Tech Stack
- Data Management
- Workflow Automation
- Cloud Computing
Implementation Scale
- Departmental Deployment
Impact Metrics
- Innovation Output
- Productivity Improvements
Technology Category
- Infrastructure as a Service (IaaS) - Cloud Computing
- Platform as a Service (PaaS) - Data Management Platforms
Applicable Industries
- Healthcare & Hospitals
Applicable Functions
- Product Research & Development
Use Cases
- Predictive Maintenance
Services
- Cloud Planning, Design & Implementation Services
- Data Science Services
About The Customer
Stanford Medicine’s Brain Stimulation Lab is a research facility dedicated to finding solutions for treatment-resistant depression. The lab is studying the use of Repetitive Transcranial Magnetic Stimulation (rTMS), a therapy that involves activating or inhibiting the brain directly with electromagnetic fields. The lab is led by Dr. Nolan Williams and includes researchers like Dr. Azeezat Azeez, a postdoctoral scholar with Stanford Psychiatry and Behavioral Sciences. The lab collaborates with the Stanford Center for Cognitive and Neurobiological Imaging (CNI) to obtain its scans. The lab's work has been published in high-impact journals and has attracted mainstream media attention from outlets like CBS and NPR.
The Challenge
Stanford Medicine’s Brain Stimulation Lab is working on solutions for treatment-resistant depression, a condition that affects 5% of adults worldwide. The lab is studying the use of Repetitive Transcranial Magnetic Stimulation (rTMS), a therapy that involves activating or inhibiting the brain directly with electromagnetic fields. The lab's work is growing, and so is their need for smart data management. The lab originally used the Flywheel platform to store raw and reconstructed data and applied its basic tools for reconstruction and quality control. However, when they wanted to perform analysis, researchers were still downloading data to a static lab PC. This process was time-consuming and made it difficult to track data provenance.
The Solution
To improve the lab’s workflow, Dr. Azeez began leveraging more of Flywheel’s capabilities. She dedicated time to building custom Flywheel Gears—containerized algorithms that are run and managed within the platform. Flywheel provides an ever-expanding library of ready-to-use Gears, for everything from routine data processing tasks like metadata extraction and classification, to more complicated processes like independent component analysis and structural segmentation. Users also have the ability to develop their own custom Gears. By better utilizing existing Flywheel Gears and developing her own, Dr. Azeez and her collaborators have been able to dramatically streamline their workflows. Flywheel also provides an SDK (software development kit) library, a powerful suite of tools and utilities that allows users to programmatically interface with the platform. Using these tools, Dr. Azeez has daisy-chained Gears together so that once a patient’s baseline scan is received, the platform automatically runs Gears in sequence for reconstruction, quality control, fMRIprep pre-processing, and the lab’s custom targeting algorithm.
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.
