1. Introduction

1.1 Indicators of quality of hospital care

Overall quality of hospital care can be estimated using several types of quality indicators based on hospital admission data. Such indicators for identifying potentially suboptimal quality of hospital care might focus for example on unexpected in-hospital or post-discharge mortality, potentially preventable hospital readmissions or unexpected long duration of admissions. In the Netherlands, hospital admission and discharge data is registered in the LBZ, the national hospital discharge register covering all general, university and a few specialised hospitals. Other specialised clinics, independent treatment centres and private clinics are not included. Inpatients as well as day cases and prolonged observations without overnight stay are registered. Administrative data of the admission as well as diagnoses and procedures are registered for each hospital discharge.

In the Netherlands, Dutch Hospital Data (DHD) annually provides hospitals participating in the LBZ registration with a set of indicators based on their performance in the previous year. Up to 2016 this set included the (unadjusted) hospital readmission rate, which is the ratio of the number of observed readmissions to the total number of hospital admissions. However, this ratio does not correct for case mix differences and might therefore not correctly reflect differences between hospitals in the true number of potentially preventable readmissions. DHD has therefore asked Statistics Netherlands in 2017 to develop a model to estimate the expected readmission risks adjusted for relevant covariates, in a fashion similar to the estimation of the hospital standardized mortality rates (HSMR). Since then, the model has been updated in 2018, 2019, 2020 and 2023. This report describes the most recent update conducted in 2025, based on model years 2022-2023.

1.2 Predictive value of the hospital readmission model

Internationally, models for estimating hospital readmission rates are used for the purpose of risk stratification but also as a quality indicator. Previous studies show that several patient characteristics contribute to the risk of being readmitted to the hospital. An overview in a systematic review by Kansagara et al. (2011) shows the various validated models that have been used internationally, the covariates included in those models and their overall predictive value. Common covariates include comorbidity indexes, age, sex and/or prior use of medical services (hospitalizations). Regardless of the number of included covariates, only a small fraction of the models are moderately discriminative (AUC/C-statistic>0.70). The model developed by Statistics Netherlands includes similar covariates as well as additional covariates such as severity of the main diagnosis, urgency of the admission and socio-economic status. However, the overall predictive value of the model did not exceed previously published values (AUC=0.69). The level of case mix correction applied by the model did however significantly improve comparability of outcomes of the individual hospitals with the national average. In other words: although the case mix correction is probably incomplete, it does, to some extent, reduce the confounding effect of differences between hospital patient populations. As such, applying the model to calculate adjusted readmission ratios for individual hospitals is an improvement over calculating crude rates (Van der Laan et al. 2017). Additionally, despite the limited discriminative ability, the adjusted readmission ratio might provide insight in e.g. diagnoses or specialties within hospitals where quality of care can be improved (Hekkert et al. 2018).

1.3 Development of the hospital readmission model in the Netherlands

The initial hospital readmission model, developed by Statistics Netherlands in 2017, was based on the linkage of admissions and readmissions that occurred within the same hospital (intra-hospital readmissions). In 2018 Statistics Netherlands improved this intra-hospital readmissions model by excluding planned transfers to and from neighbouring or specialized hospitals (‘2016 model’; this model was based on LBZ data of 2015 and 2016 and was named after the most recent year of included data). It is common practice for hospitals to refer inpatients to other hospitals for specific procedures, such as coronary interventions. Such planned transfers should not be labelled as readmissions.

The results of this improved intra-hospital model were compared to that of a newly developed inter-hospital model, that also took into account readmissions in other hospitals, while excluding planned transfers. Since readmissions can also take place in other hospitals, including inter-hospital readmissions in the model might improve its predictive value. However, the predictive value of both models was largely comparable, and it was concluded that apart from views regarding the relevance of inter-hospital readmissions for measuring quality of care, practical considerations might determine which of both models will be used for calculating the readmission ratios of the individual hospitals (Van der Laan et al. 2018). A practical disadvantage of the inter-hospital ratio is that hospitals need patient information from other hospitals to study the files of the patients with readmissions and to calculate the ratios themselves. Hekkert et al. (2019) also found a limited overall impact of including readmissions in other hospitals in the Netherlands. For these reasons, DHD decided to use the intra-hospital model (excluding planned transfers) in their regular hospital indicators reports.

1.4 Aim of the current project

This year (2025), Statistics Netherlands updated the intra-hospital model (‘2023 model’), excluding planned transfers, based on LBZ data of 2022 and 2023. The outcome is described in chapter 3.

1.5 Output

Statistics Netherlands only calculates the model for estimating the hospital readmission risks, adjusted for relevant covariates, not the outcomes for the individual hospitals. DHD will use the present model, based on LBZ data of 2022-2023, to estimate the expected readmission risk for each individual primary (index) hospital admission in 2024. For each hospital, the standardized (adjusted) readmission ratio can be calculated as the observed number of readmissions (x 100) divided by the sum of the expected readmission risks of the index admissions of that hospital. DHD will use these outcomes for their indicators reports for each hospital.