PIL related projects
ZonMW Implementatie Impuls (VIMP)
Jan2025 t/m Dec2026
Data-driven medication safety in the Intensive Care setting – ADAPTIC project
This implementation project aims to improve medication safety for patients in the Intensive Care Unit (ICU). We start by addressing two key risk areas: drug–drug interactions and medication-related acute kidney injury. If our data-driven approach proves successful, the developed tools will be expanded to other relevant risk areas.
Involved PIL team members: Dr. Joanna Klopotowska (project leader), Dr. Arthur Wasylewicz, Charlotte Mittendorf & Anne Langermans
ADAPTIC project group further consists of the following stakeholders: Prof. dr. Nicolette de Keizer and Prof. dr. Dave Dongelmans (Stichting Nationale Intensive Care Evaluatie, NICE), Iwan Meynaar (NVIC), Dr. Nicole Hunfeld (KNMP, NVZA SIG Intensive Care), Dr. Liesbeth Bosma ( NVZA SIG Intensive Care, Metavision expert), Dr. Laura Nijstad (NVZA ICT cie and HiX expert), Harmen Huls (EPIC expert), Marianne le Comte (KNMP-GIC), Leonora van Dorp-Grandia (Z-Index – G-Standaard).
Evaluation of the quality of antibiotics prescribing by reusing real-world data rom EHRs of ICU patients – Finalized
In this project led by dr. Jeroen Schouten (intensive care physician at Radboud UMC), we contributed by providing our expertise on real-world data and real-world data analytics. In this project, the SIMPLIFY database (containing real-world data from electronic hospital records (EHRs) of ICU patients) and data from the National Intensive Care Registry are used to gain insight into the extent and quality of antibiotics prescribing in Dutch ICU patients.
Involved team members: Joanna Klopotowska, Izak Yasrebi-de Kom
Funded by SURF grant
Development and validation of de-identification algorithm – for clinical narratives – Finalized
In this project led by the Department of Research Data Management of Amsterdam UMC, we contributed by providing our expertise on de-identification tooling and will organise the annotation process of clinical narratives by medical experts as part of the validation of the developed de-identification algorithm. Such algorithm is needed to remove mentions of directly identifying information about patients from the clinical narratives. Directly identifying information includes for example: surnames, birth dates, social security numbers or home addresses. This directly identifying information must be removed in order to preserve patient privacy while reusing clinical narratives for research.
Involved team members : Joanna Klopotowska, Hemmik Leopold
Investigating food-drug interactions prevalence and types in hospitalised patients – Finalized
In this project led by dr. Pauline Bollen (hospital pharmacist at Gelderse Vallei Hospital), we contributed by providing our expertise on reusing real-world data from electronic hospital records, data analytics and computerised decision support systems. Concomitant intake of food and medications may increase or decrease drug absorption and subsequently affect clinical health outcomes.
Involved team members: Joanna Klopotowska
