Contextualized and Standardized Datasets for Better Healthcare

To support for example reporting, quality improvement initiatives, healthcare dashboards, we develop refsets – structured collections of relevant data points, enriched with clinical context. The overarching goal is to promote a data-driven healthcare system that enables continuous improvement of quality of care and thereby enhancing the patients’ quality of life.

Each refset is built around a specific theme or care process, ensuring the data is meaningful and actionable for:

Enabling AI applications and research
Regulatory data submission (e.g. quality indicators)
Internal quality improvement projects
Policy dashboards and performance monitoring
Enabling AI applications and research

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Objective

Our goal is to make relevant data from different sources reusable in a standardized and meaningful way, without adding administrative burden. By adding context to isolated data points, we ensure that the resulting information is clinically accurate and interpretable. Refsets enable reliable, structured data collection that can be used to monitor and improve healthcare quality and processes.

Methodology

Each refset is developed in response to a clearly defined reporting or analytical need. In collaboration with clinical experts, we identify the relevant data points, map them to international standards such as OMOP, and ensure the resulting dataset is traceable, complete, and reusable.

This standardized approach across projects allows us to compare insights, track trends over time, and measure the impact of improvement initiatives.

General info and contact

Keywords (#): structured data, OMOP, datadriven healthcare, healthcare quality improvement, standardization, value-based healthcare, healthcare analytics

RADar project research lead: Dr. Siel Depestele

Data team:

• Joica Werbrouck
• Justine Lemaître
• Hanne Vanluchene
• Jelle Bossuyt
• Yves Ovaere
• Thomas Forment
• Wim Bruneel

Contact us: data.team@azdelta.be