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AI Revolutionizes Diagnostics of Rare Diseases: A Case Study on Helsinki University Hospital
Technology Category
- Analytics & Modeling - Machine Learning
- Sensors - Temperature Sensors
Applicable Industries
- Education
- Healthcare & Hospitals
Applicable Functions
- Product Research & Development
Use Cases
- Clinical Image Analysis
- Root Cause Analysis & Diagnosis
Services
- Data Science Services
- System Integration
The Challenge
Helsinki University Hospital (HUS) and Tietoevry were faced with the challenge of building a certified trusted Research Environment that is compliant with the EU General Data Protection Regulation (GDPR) and the Findata legislation on the secondary use of national social and health data. The goal was to accelerate medical research with a modern, high-security digital environment with the latest analytics capabilities. The challenge was particularly significant in the context of diagnosing and treating rare diseases, which are often difficult to diagnose due to their rarity and the extensive research data required. The cost for the public healthcare sector can be as much as 40 times greater before the diagnosis is found, making the need for a more efficient diagnostic process crucial.
The Customer
Helsinki University Hospital (HUS)
About The Customer
Helsinki University Hospital (HUS) is a leading healthcare provider in Finland. In collaboration with Tietoevry, they have co-developed the data lake service that enables the development of advanced treatments and optimized care pathways in healthcare while also accelerating HUS’ world-class medical research. The Rare Diseases eCare for Me project, which utilizes HUS’s data lake service and its new HUS Acamedic research environment, is a part of the CleverHealth Network ecosystem. The project aims to use real-world data and machine learning to develop an AI solution that can provide more effective and faster treatment for patients with rare diseases.
The Solution
The solution was the development of a data lake service and the HUS Acamedic analytics workspace. These tools provide doctors and researchers with access to large data masses, comprehensive analytics tooling, and the latest AI technology. The eCare for Me project, a part of the CleverHealth Network ecosystem, utilizes HUS’s data lake service and its new HUS Acamedic research environment. Real world data and machine learning fuel the development of an AI solution that can be used to provide more effective and faster treatment for patients with rare diseases. The HUS Acamedic analytics workspace enables the analysis and research of large quantities of data, saving the healthcare personnel’s time. The cloud-based solution also offers scalable computing capacity that enables the fast analysis of massive datasets at low costs and with no needed investments to own hardware.
Operational Impact
Quantitative Benefit
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