Activities Report 2022 / 2023
Public Health Rotterdam

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Medical Decision Making

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“Our research supports optimal evidence-based and personalized decisions in healthcare, to improve quality and care and patient outcomes”

Medical decision-making aims to support patients, clinicians and healthcare policymakers in making the best decisions about diagnostic, therapeutic and other healthcare interventions. The mission of our research group is to contribute to optimal evidence-based, personalized, and sustainable decisions in healthcare. We work on the development and application of quantitative methods to measure quality of care and to translate group-based evidence to information that supports decision making in individuals. We also evaluate interventions aimed to improve quality and outcomes of care, in experimental, quasi-experimental and observational studies. We contribute to providing interventions that work for individuals who actually need them, leading to the efficient use of resources and a more sustainable health care system. Our specific expertise includes advanced prediction modelling, design of randomized trials, comparative effectiveness research (CER), and outcomes research. We apply our methods in collaboration with multiple clinical groups within and outside Erasmus MC to directly improve patient care.

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The ICAI STROKE Lab

In the ICAI STROKE Lab we collaborate with other departments in the Erasmus MC, the Erasmus University and Philips. In the ICAI stroke lab we aim to improve care and outcome of ischemic stroke patients. ICAI, the Innovation Center for Artificial Intelligence, brings together knowledge

institutes, industry, and governmental and societal partners in the Netherlands to develop talent and technology in the area of artificial intelligence (AI).

In our ICAI STROKE Lab we will develop data-driven AI modeling tools to support healthcare professionals in decision-making in the entire stroke patient journey, from calling 112 to rehabilitation. Making better decisions will improve the effectiveness of the entire stroke patient journey and lead to more personalized care. Specifically, researchers in our group will work on tools that can be used in the ambulance to support decisions on which hospital to go to, based on decision models combining severity of the stroke, other patient characteristics, and real time driving times, and on tools that support decisions on which patients to treat with endovascular treatment (a specific treatment only beneficial for patients with more severe strokes) based on prognostic models combining clinical characteristics and advanced image analysis.

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Intuitive measure to quantify life expectancy of patients with cancer

Together with the Netherlands Comprehensive Cancer Organisation (IKNL) we performed a study in which we applied a new method to assess life expectancy of individuals with cancer to nationwide data from the Netherlands since 1989. In this study, we introduced an innovative approach to determine the lifelong prognosis of individuals who have experienced or are currently dealing with cancer. The method allows for the calculation of overall life expectancy based on type of cancer, age, gender, year of diagnosis, and stage. This calculation is then compared to the life expectancy of a similar group in the general population.

Our analysis revealed an increase in life expectancy for all 17 types of cancer studied, even when compared to the general population, though significant variations were observed among different cancer types. Using life expectancy as an outcome measure sheds light on the impact of a cancer diagnosis on an individual's entire life. Healthcare professionals can use it to better inform patients about the long-term effects of cancer on their lives. Furthermore, adopting life expectancy as a measure offers insight into the overall progress in oncological care and identify areas where opportunities for improvement exist.

Section: Medical Decision Making