Quality of care (IT systems)

Quality of care. Within this research line, we are working on improving healthcare and preventing errors. This is achieved by developing quality indicators, case mix correction, comparing various hospitals, and providing feedback to healthcare providers. This feedback allows to enhance their behavior and/or care processes. We aim to reuse routinely collected data and investigate how changes in computer programs or in data recording may contribute to healthcare improvement. We primarily focus on the intensive care, but we also have projects in other specialties and in healthcare systems of low- and middle income countries, for example in Asia and Africa.

Scientific projects

SES and ICU-outcomes

For health outcomes, socioeconomic status (SES) is an important determinant. Generally, a lower SES is associated with more adverse healthcare outcomes. It is unclear whether this phenomenon is present in patients admitted to the intensive care unit (ICU), especially in The Netherlands.

Therefore, this nationwide observational cohort study will investigate SES in the total Dutch ICU population and across regions, the association between SES and (hospital/long-term) mortality, and its effect on individual estimated mortality risks. Depending on the outcomes, we will try to improve the case-mix model (APACHE IV) for estimating individual mortality risks and examine its influence on benchmarking Dutch ICUs. We will use a composited (occupation, income, education) SES score per household and per postal code to identify possible differences between these scores.

 Research line: Quality of care (IT systems)

NICE2Improve antibiotics

In the ICU, 71% of the patients receive antibiotics. Of these antibiotics, 30% to 60% is prescribed unnecessary, inappropriate, or suboptimal.

Audit & Feedback is proven to be effective in stimulating quality improvement, therefore the NICE2Improve dashboard was developed. This dashboard includes actionable quality indicators and a toolbox with suggestions of improvement actions to support ICU staff on how they can increase appropriate antibiotics use. The purpose of this research is to pinpoint areas where specific ICUs might experience barriers to quality improvement, to devise a method for creating a tailored toolbox, and to evaluate the effectiveness of the tailored toolbox in enhancing antibiotic use in the ICU. 

Research line: Quality of care (IT systems)

NICE Federated Learning

Similar to the NICE FAIR project, in this project we test the feasibility of Federated Learning (FL), a decentralized machine learning technique. It is a potential solution to the question: "How can we analyze health data without needing to gather that data centrally?".

We will test FL in the context of NICE’s benchmarking activities, which compares mortality rates of all Dutch ICUs based on the APACHE IV case-mix adjustment. We will examine the performance of NICE's yearly APACHE IV recalibration when calculated centralized (current practice) versus decentralized (FL) and investigate its effect on the position of ICUs in funnel plots. This decentralization is recreated virtually, by creating partitions of the NICE's database, simulating separate hospital databases.

This project should result in a first assessment of the use of FL as a decentralized data analysis method for the NICE, opening the door for further research and benchmark opportunities.

Research lines: Quality of Care (IT Systems) Reusable Health Care

Staff: Daniel Püttmann,  Ferishta Raiez, Sebastian van der Voort,  Ronald Cornet, Nicolette de Keizer


This project studies the various ways to transform the NICE ICU quality registration data structure in order to lower the registration burden on intensivists, to increase data collection from monthly to daily, to make the NICE data reusable for researchers, and to facilitate federated analysis without having to share data directly. Various standardization solutions are explored, such as the data structure standard OMOP CDM and the data exchange standard HL7 FHIR. These are then evaluated in collaboration with international organizations such as European Health Data & Evidence Network (EHDEN), and the Critical Care Asia Africa (CCAA) registry network. By making following the Findable, Accessible, Interoperable and Reusable (FAIR) principles we hope to make the NICE data easier to share, use, and reuse.

Research lines: Quality of Care (IT Systems) & Reusable Health Care

Staff: Daniel Püttmann, Nicolette de Keizer, Ferishta Raiez, Ronald Cornet

Staff involved