C.G.M (Charlotte) van sassen, MD, Postdoctoral researcher

Charlotte van Sassen is finishing her PhD on improving clinical reasoning education using malpractice claims with diagnostic errors. This research is a collaboration between iMERR and the Department of General Practice. In addition to her work on diagnostic errors, patient safety and clinical reasoning education, Charlotte’s research interests include the application of Artificial Intelligence (AI) in medical education and clinical practice. To further her expertise, she is currently a fellow at the Institute for Healthcare Improvement, where she is advancing her strategic, operational, and thought leadership skills in quality and safety. She is also a member of the D-CREDO team, a European research consortium dedicated to enhancing the skills of health professions students and educators in the use of digital health tools for clinical reasoning practice and education. Alongside her research activities, Charlotte is a general practitioner and a lecturer in the Department of General Practice. She is also the founder of Huisartsen van Nederland, a company that develops AI-based patient flow management software for general practice, including an AI-driven triage tool.

Charlotte's publications on ResearchGate

Charlotte is a lecturer for educators, residents and supervisors at the Department of General Practice, ErasmusMC. Her teaching topics cover diagnostic errors, patient safety, health law, various perspectives on the use of AI in medical education and clinical practice, dermatology and general primary care topics.

General Practitioner, lecturer and founder/CEO Huisartsen van Nederland.

ZonMW HGOG grant (programma Huisartsgeneeskunde en Ouderengeneeskunde)

Fellowship Institute for Healthcare Improvement (IHI)

Nationale Zorginnovatieprijs 2023 (Huisartsen ven Nederland)

PhD Learning from mistakes: Defining case descriptions to improve clinical reasoning education.

Diagnostic errors are the most common cause of patient liability claims against primary care physicians. It is estimated that diagnostic errors occur in 5% of all primary care patients and are more likely than other types of errors to have serious consequences, such as death. Diagnosing is a complex task and clinical reasoning education (CRE), e.g., practicing with fictitious case vignettes, is therefore an important part of general practitioner training. The use of malpractice cases as vignettes is a promising approach for improving CRE, because malpractice cases may represent knowledge gaps with a relevant impact on patients and can provide a variety of atypical and context-rich examples. However, it is not yet clear how these vignettes should be presented, as malpractice cases can evoke strong emotions that may either facilitate or hinder learning. This research investigates whether diagnostic errors and malpractice claims in clinical case vignettes are effective for improving CRE in general practice (GP) residents.

Fellowship project Institute for Healtcare Improvement

At ErasmusMC, over 135 AI initiatives are being developed to improve patient safety and workforce well-being, including tools for clinical decision support, risk stratification, and administrative assistance. However, over 100 of these initiatives are still in early stages due to barriers like unfamiliarity with large-scale implementation, regulatory complexities, and limited funding for implementation rather than development. To address this, a strategic framework will be created to identify key barriers and facilitators and provide actionable recommendations to help AI projects transition from pilot to full implementation through stakeholder interviews and data analysis of ongoing initiatives. The goal is to successfully implement at least five AI tools within 12 months, enhancing patient safety and healthcare efficiency. This will impact stakeholders involved in AI development, research, and implementation, as well as healthcare providers and patients at ErasmusMC, with potential applicability to other healthcare organizations.