Data collection and dataset management form the first component of a new concept in healthcare: Digital Twin. Digital Twin means a multi-modal modelling approach to data management, analysis, prediction, and interpretation at the patient level.

But data collection is a problem within healthcare, and it’s a messy one. Healthcare data is fragmented and ever-changing. And -for a good reason- researchers deal with patient privacy and security. Safe, orderly and robust data collection is a necessity for the future of healthcare. To realize this first component of Digital Twin, we’ll perform a case study with the following research question:

How to identify and integrate various data sources for patients with cardiovascular risk factors during their entire life course?

Holistic Patient files

This work package will result in Holistic Patient files, covering a given patient in great detail. These files contain patient-specific data, including demographic data, weight, height, vital signs, medical history, physical examination, medication and allergies, laboratory test results, imaging, and more, as far as available in the Hospital Information System (HIX). We will use the Observational Health and Science and Informatics (OHDSI) standard data model to integrate the data sources and transform the identified Erasmus MC sources.

Case-study

Hypertension is the proof of principle for this study. A common condition and associated with the increased risk of other cardiovascular diseases. It is also the leading risk factor responsible for all-cause morbidity and mortality worldwide. The focus starts on women with hypertensive complications around pregnancy and adult and elderly patients with complex, treatment-resistant hypertension.

Model for managing patient data

We will use our multi-model dataset and advanced techniques, including artificial intelligence (AI) algorithms, to develop a model to manage hypertension. Novel analysis techniques will enable us to process a lot of patient information and model the complex relationships between them.

In turn, this will provide us with more accurate predictions on hypertension outcomes. We will then utilize these predictions in the decision-making process of hypertension management strategies. Digital Twin can improve healthcare and patients’ outcomes by offering more personalized healthcare in this context.

Current projects