Abstracts by keywords: likelihood of hospitalization
Given the challenges of demographic change, the healthcare system is faced with the question of how to allocate resources efficiently and fairly. Only 20% of the insured are responsible for 81.2% of the hospital, medication and doctor expenses of the statutory health insurance (GKV) (1). Approximately 5% of patients account for 50% of utilization (Fig. 1). Almost half of these expenses relate to hospital treatment. In many cases, these are unavoidable and serve to ensure the safety and good quality of treatment for patients. A common reason for claiming benefits is the presence of a chronic illness. The top 10 reasons for hospitalization include diagnoses such as heart failure, angina, hypertension, and type 2 diabetes (1). The acute deterioration of chronic diseases such as diabetes or heart failure often leads to hospitalization. The risk of hospitalization increases significantly if a person has several chronic illnesses at the same time. In order to be able to assess a person's risk, risk prediction models are developed (predictive modeling). The aim of risk models is to avoid (re-)admissions to hospital, to prevent the incurrence of hospital costs and to achieve better health outcomes for the patient with intensive, cheaper medical intervention (2). Risk models can take a lot of information from the service sectors and master data into account in parallel and can be used to predict hospital stays for any reason as well as for specific diseases.