Marloes de Winter en Tineke de Vries zijn beiden werkzaam als Qualified Medical Engineer in opleiding binnen het consortium Eindhoven MedTech Innovation Centre (e/MTIC). Dit is een samenwerking in de regio Eindhoven met de TU/e, Philips, Catharina Ziekenhuis, Maxima Medisch Centrum en Kempenhaeghe. Marloes en Tineke voeren hun QME jaarproject beiden uit in het Catharina Ziekenhuis. Dit zijn ontzettend innovatieve projecten waar data centraal staat. Nieuwsgierig? Lees hier onder meer!
Marloes de Winter
For post-myocardial infarction (MI) ventricular tachycardia (VT) patients, primary risk stratification is based on left ventricular ejection fraction (LVEF) and symptoms. This proves to be insufficient in identifying patients at high risk. LVEF is a general measurement which barely encompasses the VT development complexity. This complexity might be better interpreted by visualizing and analysing the different routinely obtained data of MI patients. Through the strong collaboration within e/MTIC in this EngD QME project, we create an integration of technical and clinical practice. In hospitals an abundance of data is created but not always used to its full potential. Coupling different multimodal data and analysing their time-evolving trends in a dashboard can unravel new useful insights. In addition, the implementation of this data management workflow and dashboard necessitates active engagement with a multi-disciplinary team. Collaborating with PhDs allows us to enhance the depth and quality of our analyses with patient-specific models, based on obtained data. Furthermore, actively involving stakeholders such as clinicians and ICT specialists enables an understanding of the practical implications, feasibility and real-world applicability of our findings.
Tineke de Vries
Project: Design, implementation and application of a perioperative high-resolution monitoring database
Vital sings of patients are continuously monitored during surgery and on the Intensive Care. Availability of this data in high resolution gains interest for better clinical monitoring, continuous quality surveillance, and for research such as training prediction models with artificial intelligence. The aim of my project is to design and implement a high-resolution database of all vital signs that are available on the patient bedside monitor, such as heart activity, blood pressure, and respiration. This data is automatically saved and stored, and data from different medical devices (e.g. the patient monitor and mechanical ventilator) are synchronized. The database will be connected to the electronic health record in a data lake, to provide context to the data. Three pilots are setup to further develop the workflow for using the data within clinical and research settings. For this, we collaborate with the AI expertise centre of the Catharina Hospital. As EngD Qualified Medical Engineer trainee based in the Catharina Hospital and TU/e, I closely collaborate with PhD students with Medicine and Engineering backgrounds as well as hospital specialists and industry partner Philips. Within these collaborations we combine different areas of expertise which allows us to design an optimal solution for all partners.
