Health Data Forum 2022
Health Data Quality:
a Dynamic Complexity

Pre-conference Tutorials

Data Quality Tutorials given by European Experts
openEHR Tutorials

Data Quality Tutorials given by European Experts

Wednesday, 16 November 2022

The big health data revolution has not reached its full potential due to concerns related to the quality of health data. Data quality is about having confidence in the quality of the data that you record and the data you (re)use. Obtaining high-quality data in a healthcare setting seems evident and straightforward, unfortunately, this doesn’t appear to be the case in practice. Scientific research provides us with multiple case studies showing us the negative impact of low health data quality.

Three misunderstandings on Data Quality

“Data quality is about measuring”

While measurement is an integral part of the data quality journey, it also involves the management of people, processes, policies, technology and standards within a hospital or GP clinic.

“Data quality is the concern of our data management team”

Data is generated and collected at every point of interaction. All stakeholders, including patients, are involved and should participate in the data quality effort.

“Data quality is the concern of our data management team”

Data quality is defined within the context of ever-changing requirements. The quality of clinical data should therefore be regularly assessed and reassessed in an iterative process to ensure that appropriate levels of quality are sustained.

Data quality is a dynamic complexity, an ever-changing requirement that needs to be redefined over time and over different projects. Because of this complexity, there are enormous challenges to overcome before achieving high-quality data.

What can you learn from our data quality tutorial?

  • Why is data quality so important in our healthcare setting?
  • What is the impact of low data quality?
  • Data quality dimensions and how to create data quality rules.
  • Data quality assessment requirements
  • How to define tools to assess data quality?
  • Data quality curation
  • Implement practical solutions to address the root causes of data quality problems
  • Monitor and verify improvements that were implemented.

Who should attend this tutorial?

Professionals whose work involved handling health data

  • Pharmaceutical sector
  • Healthcare sector
  • Academia



Pascal Coorevits, PhD
Medical Informatics Professor,
Ghent University

Ricardo João Cruz Correira, PhD
Health Informatics Systems Professor,
University of Porto


Carlos Sáez Silvestre, PhD
Senior Researcher,
ITACA Institute

Christel Daniel, MD. PhD
AP-HP IT Department

Damien Leprovost, PhD
Data Quality Project Leader,

Maxim Moinat
Data Engineer (Medical Informatics),
Erasmus MC

Steve MacFeely, PhD
Director of Data and Analytics,

Ravi Shankar Santhana Gopala Krishnan

openEHR Tutorials

What is a modular openEHR-based electronic health and social care record? How does it differ from a traditional model? How does the vendor and technology neutral openEHR data model look like? We offer 2 courses to learn more about openEHR.

The openEHR Awareness course is meant for any role and there is no need to have prior knowledge about openEHR (opening session: 30 September)

Introduction to openEHR Clinical Modelling Course will familiarize you with the openEHR clinical modelling approach and the key tools used to develop openEHR data models (opening session 16 November)

The cloud-based eLearning environment will be open from the moment you register and will close six months after the Summit (20 May 2023), so the sooner you register the longer you can access all the great openEHR content that is available and the longer you will benefit from this training.