1. Latest articles

Characteristics and outcome of COVID-19 patients admitted to the ICU: a nationwide cohort study on the comparison between the consecutive stages of the COVID-19 pandemic in the Netherlands, an update

This study reports mortality rates among patients with COVID-19 during the first wave and compared these rates with the following waves. Using COVID-19 patient data from Dutch Intensive Care Units (ICUs), collected by Stichting Nice, between May 2020 and January 2023,  the mortality risks in the initial upsurge of the third wave was similar to the first wave, but mortality rates decreased in later periods. Curious? Click here for more information.

Fabian Termorshuizen et al. DOI: 10.1186/s13613-023-01238-2

The effect of computerised decision support alerts tailored to intensive care on the administration of high-risk drug combinations, and their monitoring: a cluster randomised stepped-wedge trial

In nine Dutch intensive care units, this study assessed the impact of tailoring potential drug-drug interaction (DDI) alerts to the ICU setting. Using a cluster randomised stepped-wedge trial, a customized clinical decision support system (CDSS) only providing alerts for potential DDIs considered as high risk was implemented and evaluated. The study found a 12% decrease in the number of administered high-risk drug combinations, indicating that tailoring alerts to the ICU setting improves CDSS effectiveness and patient safety. Click here for more information.

Tinka Bakker, Joanna Klopotowska et al. DOI: 10.1016/S0140-6736(23)02465-0

Strain on Scarce Intensive Care Beds Drives Reduced Patient Volumes, Patient Selection, and Worse Outcome: A National Cohort Study

This study analyzed the impact of Intensive Care Unit (ICU) resource allocation during the COVID-19 pandemic on a non-COVID-19 cohort (120.393 patients) in Dutch ICUs. Compared to the prepandemic period cohort (164.737 patients), the pandemic cohort had lower number of non-COVID patients (27% lower), fewer medical patients (3% lower), fewer comorbidities (4% lower), more vasoactive medication (6% higher), and a slightly higher case-mix adjusted hospital mortality (odds 1.08). Click here for more information.

Sylvia Brinkman, Nicolette de Keizer et al. DOI: 10.1097/CCM.0000000000006156

Predicting 30-day mortality in intensive care unit patients with ischaemic stroke or intracerebral haemorrhage

This study developed and validated two predictive models to estimate the 30-day mortality for patients with a stroke admitted to intensive care units (ICUs): one model for patients with ischaemic (N=8 422) and one model for patients with haemorrhagic stroke (N=5 881). The 30-day mortality was 27% in the first group and 41% in the second group. Both models showed high discrimination (AUC of 0.85) and good calibration. Click here for more information.

Fabian Termorshuizen et al. DOI: 10.1097/EJA.0000000000001920

        Differences in the epidemiology, management and outcomes of kidney disease in men and women

        This review reports on differences between men and women with kidney disease. Women show a higher prevalence of chronic kidney disease stages 3-5, and men a higher prevalence of albuminuria. Women are less aware of their disease, and receive less screening, nephrologist care, and face greater barriers to kidney transplantation access. Men experience faster renal decline, higher mortality, and increased cardiovascular risk. Click here for more information. 

         Nick Chesnaye et al. DOI: 10.10338/s4181-023-00784-z

        All articles of Medical Informatics

        2. PhD theses

        The latest PhD theses of our department.

        3. Research in the spotlight

        Making medical terms patient-friendly

        Interview with Hugo van Mens researcher on Reusable Health Data

        Patients can access their electronic medical records through patient portals. However, medical terms such as diagnoses are difficult to understand and confusing.

        How do you want to help patients understand their medical records?

        To clarify medical terms to patients, healthcare professionals naturally use more general and simple terms. Based on this principle, we developed an algorithm to generalize difficult medical terms to more patient-friendly, easy terms. The algorithm uses the hierarchy from SNOMED CT. This is a comprehensive medical terminology system. For example, `SARS-CoV-2` is a coronavirus and can be clarified using this more general term `coronavirus`. The algorithm can clarify thousands of medical terms, using only a few hundred plain language clarifications.

        Did the solution help?

        We implemented the clarifications in a hospital patient portal problem list in clinical practice. Results showed that most patients clicked on the information buttons to read the clarifications and that patients found most clarifications to be of good quality. This shows that it is a feasible solution. The clarifications are useful for patients and clinicians do not have to change how they register medical data.

        What are your future plans?

        Future work involves improving the quality and coverage of the clarifications and using AI models such as ChatGPT to generate clarifications.

        Curious and want to know more?

        Read one of the scientific articles or the impact story on the APH website:

        Van Mens H.J.T. et al. Evaluation of Patient-Friendly Diagnosis Clarifications in a Hospital Patient Portal.

        Van Mens H.J.T. et al. Diagnosis clarification by generalization to patient-friendly terms and definitions։ Validation study.

        Impact story on the APH website

        4. Podcast Health Informatics (in Dutch)

        All podcasts

        5. Technical reports

        Check here for all our technical reports!