The Power of Data Visualization: an Illustrated Assessment of Quality of Care across Care Centers in Flanders
Abstract
Quality of Care (QoC) indicators are systematically monitored in Flemish care centers since 2013. From 2020 onwards the monitoring is performed by VIKZ (Vlaams Instituut voor Kwaliteit van de Zorg), a new institute and network organization for healthcare quality improvement financed by the Flemish government. The initial aim was to provide data driven information that allows centers to evaluate their performance based on confidential feedback on process and outcome indicators. This reveals dimensions where action for improvement appears most needed. Today, this data gathering is no longer voluntary and reports additionally serve to inform the public at large, as well as government for quality monitoring and policy making. Evaluating care through outcome indicators is complicated, however, since outcomes are driven by resident characteristics in interaction with the standard of care deployed, while resident populations of care centers tend to have a large turnover.
Reports for the specifically targeted groups focus on the important questions asked. Amidst the complexity, data visualization is often critical to bring the key message efficiently in an effective and informative fashion. It can equally be misleading, however, if not well thought through or executed. Data visualization is thus crucial in many data processing steps going from raw data cleaning, over data description and analyses, discussion and interpretation, to the final reporting for specific audiences. Several publications offer formal guidance on this. We will illustrate implementation of purposeful data visualization-in-action across those steps with general tips and tricks and real-world examples gathered over years of involvement in this quality of care project.
Bio’s (there are three speakers)
Bob De Clercq has a PhD in Bioscience Engineering and a Master in Statistical Data Analysis. He has a broad interest in R&D in many areas of technology, with passion for modelling and data sciences. In 2018 he started working at the UGent Center for Data Analysis and Statistical Science as statistical consultant. He is also involved with statistics teaching in FLAMES and the Academy for Lifelong Learning.
Dries Reynders holds Master’s degrees in Physics and Statistical Data Analysis. He joined Stat-Gent in 2015 as a statistical consultant, engaging in a wide range of projects for industry, government and academics. His main interests concern clinical trials, survival analysis and causal inference in observational data.
Els Goetghebeur is Full Professor in the Department of Applied Mathematics, Computer Science and Statistics. She chairs the UGent expert Center for Data Analysis and Statistical Science. She is Editor-in-Chief of ‘Statistics in Medicine’ and chair of the causal inference group of STRATOS ‘Strengthening Analytical Thinking for Observational Studies’. Her research is devoted to the development and application of data analytic methods for primarily biomedical applications generally and with a focus on causal inference, survival analysis and quality of care. She has taught many years in an international context, including at Harvard, Stanford and the London School of Hygiene and Tropical Medicine. She is director of the Academy for Lifelong Learning of the Faculty of Science at UGent and one of the steering group member of FLAMES.
