Background Comparing inpatient fall rates can serve as a benchmark for quality improvement. To improve the comparability of performance between hospitals, adjustments for patient-related fall risk factors that are not modifiable by care are recommended. Thereafter, the remaining variability in risk-adjusted fall rates can be attributed to differences in quality of care provided by a hospital. Research on risk-adjusted fall rates and their impact on hospital comparisons is currently sparse. Therefore, the aims of this study were to develop an inpatient fall risk adjustment model based on patient-related fall risk factors, and to analyse the impact of applying this model on comparisons of inpatient fall rates in acute care hospitals in Switzerland. Methods Data on inpatient falls in Swiss acute care hospitals were collected on one day in 2017, 2018 and 2019, as part of an annual multicentre cross-sectional survey. After excluding maternity and outpatient wards, all inpatients older than 18 years were included. Two-level logistic regression models were used to construct unadjusted and risk-adjusted caterpillar plots to compare inter-hospital variability in inpatient fall rates. Results One hundred thirty eight hospitals and 35,998 patients were included in the analysis. Risk adjustment showed that the following factors were associated with a higher risk of falling: increasing care dependency (to a great extent care dependent, odds ratio 3.43, 95% confidence interval 2.78–4.23), a fall in the last 12 months (OR 2.14, CI 1.89–2.42), the intake of sedative and or psychotropic medications (OR 1.74, CI 1.54–1.98), mental and behavioural disorders (OR 1.55, CI 1.36–1.77) and higher age (OR 1.01, CI 1.01–1.02). With odds ratios between 1.26 and 0.67, eight further ICD-10 diagnosis groups were included. Female sex (OR 0.78, CI 0.70–0.88) and postoperative patients (OR 0.83, CI 0.73–0.95) were associated with a lower risk of falling. Unadjusted caterpillar plots identified 20 low- and 3 high-performing hospitals. After risk adjustment, 2 low-performing hospitals remained. Conclusions Risk adjustment of inpatient fall rates could reduce misclassification of hospital performance and enables a fairer basis for decision-making and quality improvement measures. Patient-related fall risk factors such as care dependency, history of falls and cognitive impairment should be routinely assessed.

National quality measurements with risk-adjusted provider comparison in health care nowadays usually require administrative or clinically measured data. However, both data sources have their limitations. Due to the digitalisation of institutions and the resulting switch to electronic medical records, the question arises as to whether these data can be made usable for risk-adjusted quality comparisons from both a content and a technical point of view. We found that most of the relevant information can be exported with little effort from the electronic medical records. In using this data source an even more sophisticated operationalization of the data of interest is needed.

Purpose Falls are a highly prevalent problem in hospitals and nursing homes with serious negative consequences such as injuries, increased care dependency, or even death. The aim of this study was to provide a comprehensive insight into institution‐acquired fall (IAF) prevalence and risk factors for IAF in a large sample of hospital patients and nursing home residents among five different countries. Design This study reports the outcome of a secondary data analysis of cross‐sectional data collected in Austria, Switzerland, the Netherlands, Turkey, and the United Kingdom in 2017 and 2018. These data include 58,319 datapoints from hospital patients and nursing home residents. Methods Descriptive statistics, statistical tests, logistic regression, and generalized estimating equation (GEE) models were used to analyze the data. Findings IAF prevalence in hospitals and nursing homes differed significantly between the countries. Turkey (7.7%) had the highest IAF prevalence rate for hospitals, and Switzerland (15.8%) had the highest IAF prevalence rate for nursing homes. In hospitals, our model revealed that IAF prevalence was associated with country, age, care dependency, number of medical diagnoses, surgery in the last two weeks, and fall history factors. In nursing homes, care dependency, diseases of the nervous system, and fall history were identified as significant risk factors for IAF prevalence. Conclusions This large‐scale study reveals that the most important IAF risk factor is an existing history of falls, independent of the setting. Whether a previous fall has occurred within the last 12 months is a simple question that should be included on every (nursing) assessment at the time of patient or resident admission. Our results guide the development of tailored prevention programs for persons at risk of falling in hospitals and nursing homes.