Abstracts by keywords: administrative data
It can be assumed that due to demographic developments, increasing mobility in old age and the increasing number of obese people, osteoarthritis and thus also the implantation of knee endoprostheses will continue to increase in the coming years (Großschädl/Stronegger 2013; Weinstein et al. 2013; Koenig and Kirschner 2003). Knee arthroplasty is a highly standardized procedure that is well suited to be processed using a treatment pathway. Through the implementation of clinical treatment paths, an improvement in the process could be achieved, which is partly responsible for the fact that the revision rate and length of stay for knee prosthesis implantations has been continuously reduced (Loftus et al. 2014; König/Kirschner 2003; Lüring et al. 2010). The main benefit of presenting the course of treatment for knee endoprostheses and using this as a guide in practice is that this ensures structured patient care (Lüring et al. 2010). The analysis of specific routine data using an adapted treatment path makes it possible to identify potential in the course of treatment and opens up possibilities for improvement in terms of better therapy results [Graf/Hofmann 2003; Wirth 2003]. The most important data source for health services research in Austria is routine data from social health insurance providers. If the data is well prepared, it is possible to analyze it to create transparency with regard to trends and costs in health services research as well as basic knowledge for health care planning (Pfaff et al. 2011; Howell et al. 2009).
31.03.2015
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.