Abstracts by keywords: predictive modeling
The most comprehensive study on the prevalence of COPD to date is the international Burden of Obstructive Lung Disease (BOLD) study (Buist & McBurnie, 2007). In Germany, the Hanover region took part in this work. The lung function was examined in 683 volunteers over the age of 40 and a survey was carried out as to their state of health. Overall, more than 13 percent of the study participants in the Hanover area were diagnosed with COPD, a good 7 percent had a mild, 5 percent a moderate and almost 1 percent a severe disease stage. Since the test subjects came from the Hanover metropolitan region, it cannot be said with certainty whether the numbers are representative for the whole of Germany. However, the overall evaluation of the BOLD study suggests this. According to this, every tenth of the more than 9,000 study participants in the twelve countries involved suffers from chronic obstructive pulmonary disease in stage II or higher. The current figures on the development of COPD in Germany were based on 6.8 million COPD cases in 2010. By 2030, the number of people affected is expected to rise to 7.9 million. (Geldmacher & Biller, 2008). COPD is also important from an economic perspective. One study determined the total costs of COPD by weighting the costs per level of severity. The main part of the costs is caused by hospital stays (26%), medications (23%) and early retirement (17%). (Herse & Kiljander, 2015); (Grønseth & S.-A., 2014); (Nowak & Dietrich, 2004). Early detection of COPD is of great importance, as timely cessation of exposure to the triggering noxa can at least partially prevent disease progression. (Vogelmeier & Buhl, 2018). There is also evidence that the natural course of the disease can be significantly influenced by the early onset of drug treatment. (Troosters & Celli, 2010).
04.06.2018
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.