Interview with Hugo van Mens
Can you introduce your research topic?
Patients can access their electronic medical records through patient portals. However, medical terms such as diagnoses are difficult to understand and confusing.
How do you want to help patients understand their medical records?
To clarify medical terms to patients, healthcare professionals naturally use more general and simple terms. Based on this principle, we developed an algorithm to generalize difficult medical terms to more patient-friendly, easy terms. The algorithm uses the hierarchy from SNOMED CT. This is a comprehensive medical terminology system. For example, `SARS-CoV-2` is a coronavirus and can be clarified using this more general term `coronavirus`. The algorithm can clarify thousands of medical terms, using only a few hundred plain language clarifications.
Did the solution help?
We implemented the clarifications in a hospital patient portal problem list in clinical practice. Results showed that most patients clicked on the information buttons to read the clarifications and that patients found most clarifications to be of good quality. This shows that it is a feasible solution. The clarifications are useful for patients and clinicians do not have to change how they register medical data.
What are your future plans?
Future work involves improving the quality and coverage of the clarifications and using AI models such as ChatGPT to generate clarifications.
Curious and want to know more?
Read one of the scientific articles or the impact story on the APH website:
Van Mens H.J.T. et al. Evaluation of Patient-Friendly Diagnosis Clarifications in a Hospital Patient Portal. Appl Clin Inform, 2023
Visual summary of the research project
