Imagine giving a blood sample and feeding a machine information about your lifestyle. The machine processes the information and then tells you whether you are healthy, should make some lifestyle changes, or perhaps need treatment immediately. This could become the future, partly as a result of a project at Vejle Hospital, which has received DKK 5.4 million in funding from the EU.
Researchers
At Hospital Lillebælt, researchers are collecting data—lots of data. This includes everything from where people live, their physical activity, eating habits, and many other factors that influence health. The goal is to develop algorithms that can predict an individual's future health.
"We are looking at many known risk factors such as alcohol, tobacco, diet, and exercise, but also some lesser-known ones, and then we want to examine how they interact. Most studies of the known risk factors focus on just one or two of them, but they typically have not compared them with a wide range of other types of data, and that's what we want to do," says Claus Lohman Brasen, senior consultant and one of the project's researchers.
The project has now passed the halfway point of its four-year pilot and continues as part of JA Prevent NCD, which aims to prevent non-communicable diseases across Europe.
Patients can see the concrete benefits of lifestyle changes
Torben Frøstrup Hansen is another researcher working on the project, which has been named Data Harmonization. He explains that a major objective is to encourage patients to take an active interest in their own health. It is nothing new that smoking, alcohol, physical inactivity, and similar factors are harmful to health, and most people are already well aware of this.
The purpose of collecting all these data is to identify people who are at risk of developing non-communicable diseases so that guidance and preventive measures can be offered as early as possible.
"We are fully aware that this is not a miracle tool that will instantly make everyone live an extremely healthy lifestyle. But we hope it can motivate some people to make choices that are better for their health by making the consequences more tangible. If we can clearly demonstrate how much specific lifestyle changes can reduce the risk of different diseases, perhaps more people will choose to make those changes," says Torben Frøstrup Hansen.
One piece of a larger, long-term project
This prevention and health data project, which is part of a broader EU initiative, focuses on using data to predict and prevent diseases, particularly cancer and other non-communicable diseases. The aim is to collect and integrate a wide range of data from various sources, including clinical quality databases, health data from Statistics Denmark, data from general practitioners, and regional healthcare records. The project also includes collecting biological samples for future research through a biobank.
Blood tests
In the future, a blood test may help predict disease.
The central idea is to use this extensive data integration to develop algorithms based on artificial intelligence (AI) and machine learning that can predict an individual's risk of developing specific diseases. For example, information about a patient's lifestyle—such as smoking and exercise—combined with clinical data like blood tests and scans could provide a much more accurate assessment of their risk of developing cancer or other illnesses.
Initially, the algorithms will be tested retrospectively using historical data to determine whether they can accurately predict diseases. Later, they will be tested prospectively by following individuals over time to see whether they can successfully predict future illnesses.
Ethical dilemmas to consider
A key element of the project is determining how data from multiple sources can be used to create predictive models that support early detection and disease prevention. This could eventually allow patients to receive personalized recommendations for lifestyle changes or other preventive measures based on their individual level of risk.
The project also examines how these technologies and data can be implemented in clinical practice, as well as the ethical and practical issues involved in using predictive models in healthcare. For example, researchers are considering how risk information should be communicated to patients and how it can be used to improve health outcomes without causing unnecessary anxiety.
Overall, the project's goal is to develop a comprehensive and accurate way of using health data to predict and prevent disease. If successful, this could lead to better health outcomes through earlier intervention and more personalized medicine.