Head of Data & AI, Tietoevry Care
The challenge
To build 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. To accelerate medical research with a modern, high-security digital environment with the latest analytics capabilities.
The solution
The data lake service and its HUS Acamedic analytics workspace provide doctors and researchers with access to large data masses, comprehensive analytics tooling and the latest AI technology. The eCare for Me project enables faster access to impactful care for patients with rare diseases, which has a significant positive impact on patient’s health and wellbeing but significantly cuts public health care costs by reducing the use of diagnostic services and ineffective treatments.
About the customer
Helsinki University Hospital (HUS) and Tietoevry have co-developed the data lake service that enables 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 utilizes HUS’s data lake service and its new HUS Acamedic research environment. In the project, that is a part of the CleverHealth Network ecosystem, real world data and machine learning fuels the development of an AI solution that can be used to provide more effective and faster treatment for patients with rare diseases. The project has been funded with the support of Business Finland.
A more humane patient journey
Enhanced diagnostics reduce unnecessary visits to the doctor and the uncertainty of the rare disease patients when a faster transition to targeted treatment becomes possible.
Faster diagnostics with machine learning
The research environment enables efficient diagnostics. Utilizing machine learning and artificial intelligence, the service can process large quantities of information quickly.
Reduced healthcare costs
The service can save significant costs on medical care when a diagnosis and the right treatment are found as soon as possible after seeking treatment.
The modern research environment enables efficient processing of large amounts of data
Thanks to the Tietoevry and HUS developed HUS Acamedic research environment and the eCare for Me project, rare diseases can now be diagnosed faster than ever before. Medical experts are now able to use comprehensive and up-to-date research data to support decision-making.
Although the Finnish medical expertise is outstanding, the diagnosis of rare diseases is challenging and requires access to extensive research data. Approximately 6,000 to 8,000 rare diseases have been identified. In the HUS Acamedic research environment, research results can be compiled and combined from different data sources quickly.
“The data lake structure of HUS Acamedic and its machine-learning technology enables efficient processing and research of extremely large data sets. It would be a tremendous task to compile all the equivalent information manually”, says Mikko Seppänen, the chief physician and director of HUS's Rare Diseases Unit.
The patient often seeks specialist care through first entering primary health care. The cost for the public healthcare sector can be as much as 40 times greater before the diagnosis is found.
“The cost of treatment is directly connected to the time spent on finding the diagnosis, as well as the suffering of the patient. One can expect significant savings from utilizing the data lake service. Even when the diagnosed disease has no treatment yet, such services are very beneficial for the patient, ”Seppänen adds.
The mental uncertainty can be a tough experience for the patient. The societal benefits do not only come from saving concrete health care costs, but also from the decrease in human suffering.
Mikko Seppänen
Chief Physician, Rare Diseases Unit, HUS
A faster diagnosis is a relief to the patient
In the HUS Acamedic research environment, artificial intelligence, and machine learning can be used to develop new service concepts and treatment processes that speed up the diagnosis of rare diseases and enhance directing patients to the right treatment. The technology also enables medical research to continue in a secure, streamlined manner.
“A faster diagnosis saves not only costs but also lives, especially in cases where a targeted treatment for the disease exists. The most tragic cases are when the diagnosis takes too long to reach and the patient’s disease progresses in a harmful way. These cases can be prevented if the diagnosis is reached faster, ”Seppänen sighs.
With AI accelerated data processing it is possible to decrease the burden and suffering of the patients and to reduce unnecessary hospital visits.
“Reaching a diagnosis dispels the patient’s worries, even if no symptomatic treatment can be found. Once the diagnosis is made, the patient's trust in the health care system slowly resets”, Seppänen says.
When the root cause of the symptoms is found quickly, the patient’s entire story changes completely.
Mikko Seppänen
Chief Physician, Rare Diseases Unit, HUS
A unique artificial intelligence solution enhances data processing
The HUS Acamedic analytics workspace enables the analysis and research of large quantities of data, consequently 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. Similar data lake solutions have not yet been released. The service co-developed by Tietoevry and HUS is the first one to become certified and its information security is top-notch.
“The pseudonymized patient data can be gathered in one place, where the data can be directly examined. The system complies with the European and the Finnish data practices that are among the world's most strict data security practices”, Seppänen describes.
Although the HUS Acamedic research environment utilizes machine learning and artificial intelligence, decisions are still made by a physician and the system is human-controlled. The service saves costs but also eases and unifies medical research.
“The artificial intelligence suggests healthcare solutions, but after that, the proposal is always evaluated by a medical professional who has the expertise and ethical understanding of what is best for the patient. However, artificial intelligence is superior in compiling, gathering, and analyzing data. This is a learning experience for both the AI and the people”, says Seppänen.
Seppänen believes that the data lake solution can achieve significant benefits for the patients, the economy, and the innovation culture.
“If the international exchange of research data is made possible, the data lake solution can improve care practices, patients’ quality of life, and at the same time bring the world innovations that improve patient well-being”, he adds.
The service makes the work of a healthcare professional more meaningful. Now we receive referrals of exactly those patients in whom we specialize. It also helps train specialists and improves treatment outcomes.
Mikko Seppänen
Chief Physician, Rare Diseases Unit, HUS
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