Patient trajectory visualization for FHIR healthcare data: A use case on melanoma patients.

Abstract

Fast Healthcare Interoperability Resources (FHIR) is gaining popularity as a standard framework for the exchange of electronic health record (EHR) data. Despite the advantages of FHIR, it is difficult for clinicians to understand the data in EHR. To support clinicians in accessing data about a patient, we created a pipeline that extracts, transforms, and visualizes patient data from FHIR. We employ a web-based timeline visualization that shows all clinical data recorded for the patient over their disease trajectory. This can help clinicians to use the patient data more efficiently and to get a clear picture of the patient’s disease progress and physical condition more quickly, which could help them to develop the best treatment plan for their patients. The source code with an example synthetic, but realistic patient is available at https://github.com/rtg-wispermed/Patient_trajectory_public

Wolfgang Galetzka

Researcher in the first cohort

My research interests include Deep Learning, Computer Vision, Radiomics, and Explainable AI.

Bahadır Eryılmaz

Researcher in the second cohort

My research interests include Deep Learning, Natural Language Processing, Computer Vision, Reproducibility

Elisabeth Livingstone

Principal Investigator

My research interests include Medical Research, Dermatology, and Digitalization.