BrainKey.ai is a health tech company that specializes in tracking changes in the brain over time and providing personalized recommendations for nutrition, exercise, and lifestyle changes. The company focuses on analyzing x-rays to track brain changes and retrieve results from hospitals.
The company tracks changes in the brain over time (using MRI scans or by analyzing Genetic data).
Challenges
– Data Management: managing sensitive patient data, including x-rays and medical history, in compliance with HIPAA regulations was crucial.
– Integration: secure integration with hospital networks was required to facilitate data exchange while ensuring privacy and security.
– Scalability: the infrastructure needed to quickly scale to handle peak loads and growing demands, especially when new scans were received.
– Continuous Data Transmission: ensuring reliable and continuous data transmission through a VPN with unified standards across different hospitals was essential.
– Dynamic Scaling: implementing a system that could dynamically scale based on real-time data analysis needs.
Solutions
- HIPAA Compliance
Gart Solutions implemented strict data management practices and utilized HashiCorp Vault to securely store and manage sensitive information, ensuring compliance with HIPAA regulations. (review our other case study of Electronic Medical Records Software for the government-based E-Health platform).
- Secure Integration
A secure network architecture was designed using Kubernetes for container orchestration, enabling safe integration with hospital networks (review another project with Kubernetes solution).
- Scalable Infrastructure
Terraform was leveraged to automate infrastructure provisioning, and Jenkins was used for continuous integration and deployment, ensuring quick scalability to handle peak loads. (review another project where the Terraform cloud tool was used to manage dynamic environments).
- Reliable Data Transmission
A VPN with unified standards was established for secure and continuous data transmission between BrainKey.ai and hospital networks.
- Dynamic Scaling depends on the workload
A monitoring system was set up to track the number of records in the queue. When the queue size crossed a certain threshold, it triggered an increase in performance and scaling. Similarly, a decrease in queue size below a threshold indicated that it was time to scale down, ensuring resource efficiency and optimal performance.
