Apply Artificial Intelligence to improve health

Clinical Data Science introduces Artificial Intelligence applications to improve health of citizens and patients and improve health care.

Depending on the applications AI applications can take many forms. For instance we develop patient facing apps and websites where patients can use our outcome prediction models.

We also develop plugins for Electronic Health Records that allow for decision support for health care professionals and shared decision making with patients. Making sure AIs are well understood by skilled and non-skilled users and are safe to use are important research topics in our group.

We have also developed and apply methods to evaluate what effect an AI has on efficiency and outcomes. We often work closely with companies as AI applications are medical devices which need to be developed by professional providers and certified before they can be used (e.g. CE mark or FDA approval).

Publications in this theme

Health Literacy and eHealth: Challenges and Strategies

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Given the impact of health literacy (HL) on patients' outcomes, limited health literacy is a major barrier to improve cancer care globally. HL refers to the degree in which an individual is able to acquire, process, and comprehend information in a way to be actively involved in their health decisions. Previous research found that almost half of the population in developed countries have difficulties in understanding health-related information. With the gradual shift toward the shared decision making process and digital transformation in oncology, the need for addressing low HL issues is crucial. Decision making in oncology is often accompanied by considerable consequences on patients' lives, which requires patients to understand complex information and be able to compare treatment methods by considering their own values. How health information is perceived by patients is influenced by various factors including patients' characteristics and the way information is presented to patients. Currently, identifying patients with low HL and simple data visualizations are the best practice to help patients and clinicians in dealing with limited health literacy. Furthermore, using eHealth, as well as involving HL mediators, supports patients to make sense of complex information.