Tieto Case Studies AI Revolutionizes Diagnostics of Rare Diseases: A Case Study on Helsinki University Hospital

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	AI Revolutionizes Diagnostics of Rare Diseases: A Case Study on Helsinki University Hospital Tieto

AI Revolutionizes Diagnostics of Rare Diseases: A Case Study on Helsinki University Hospital

Tieto

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
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. Read More
About 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. Read More
Customer Name	Helsinki University Hospital (HUS) Read More
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. Read More Log in to view content
Contents

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

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.

About 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.

Customer Name

Helsinki University Hospital (HUS)

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.

Impact #1	The implementation of the data lake service and the HUS Acamedic analytics workspace has revolutionized the diagnostics of rare diseases. The service has not only reduced healthcare costs but also enhanced the patient journey by reducing unnecessary visits to the doctor and the uncertainty of the rare disease patients when a faster transition to targeted treatment becomes possible. The technology also enables medical research to continue in a secure, streamlined manner. The AI solution suggests healthcare solutions, but the proposal is always evaluated by a medical professional, ensuring that the system remains human-controlled. The service has also eased and unified medical research, making it a learning experience for both the AI and the people.

Impact #1

The implementation of the data lake service and the HUS Acamedic analytics workspace has revolutionized the diagnostics of rare diseases. The service has not only reduced healthcare costs but also enhanced the patient journey by reducing unnecessary visits to the doctor and the uncertainty of the rare disease patients when a faster transition to targeted treatment becomes possible. The technology also enables medical research to continue in a secure, streamlined manner. The AI solution suggests healthcare solutions, but the proposal is always evaluated by a medical professional, ensuring that the system remains human-controlled. The service has also eased and unified medical research, making it a learning experience for both the AI and the people.

Benefit #1	Significant reduction in public health care costs by reducing the use of diagnostic services and ineffective treatments.
Benefit #2	Efficient processing of large amounts of data, saving healthcare personnel’s time.
Benefit #3	Scalable computing capacity that enables the fast analysis of massive datasets at low costs and with no needed investments to own hardware.

Benefit #1

Significant reduction in public health care costs by reducing the use of diagnostic services and ineffective treatments.

Benefit #2

Efficient processing of large amounts of data, saving healthcare personnel’s time.

Benefit #3

Scalable computing capacity that enables the fast analysis of massive datasets at low costs and with no needed investments to own hardware.

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Overview

AI Revolutionizes Diagnostics of Rare Diseases: A Case Study on Helsinki University Hospital

Operational Impact

Quantitative Benefit