I have experience in Risk management, it means Credit, Market and Liquidity Risk. ©

Tá»· Lệ Kèo Nhà Cái Keonhacai. Soi Kèo Bóng Đá. Nhận Định Bóng Đá Euro 3 ©

As the wizards! Then throw her in your species: fatal or is that means a report on tv with some very bad news, its a sewer, garbage ball is the brig. ©

Hi, my name is Dennis Arrindell, and I am a health economist. Health economics is a branch of economics concerned with issues related to effectiveness, efficiency, value and behavior in the production and consumption of health and healthcare. Because of my profession as a health economist, I frequently provide data driven advice on issues related to the organization of health care. You can acces my online course about healthcare claims data here: Healthcare data analytics is a complex subject with many dimensions. Too often, we see very different types of healthcare data all lumped together on one pile. Examples of healthcare data other than claims data are medical records, biomarkers, data generated by patient wearables, patient reported experiences and genetic data. However, all these fields require very different types of domain knowledge. Domain knowledge is understanding, ability and information that applies to a specific topic, profession or activity. By lumping all these different types of healthcare data together, big data analytics in Healthcare information systems seems very promising, but in practice rarely delivers the promised results. One reason why these promised results are rarely delivered, has to do with the fact that the different types of healthcare data and their different characteristics are not properly understood in depth, because specific domain knowledge is missing. By far the most common and readily available type of big data in health care is claims data, sometimes also referred to as administrative data or billing data. The reason why claims data is the most large scale, reliable and complete type of big data in healthcare is rather straightforward. It has to do with reimbursement, that is, the payment of health care goods and services depends on claims data. Healthcare providers may not always find the time to fill in all required paperwork in healthcare, but they will always do that part of their administration on which their income depends. Thus, in many cases, analyzing healthcare claims data is a much more pragmatic alternative for extracting valuable insights. For that purpose I have created an online course with the purpose to enhance the understanding of healthcare claims data and its possible applications in data science. In doing so, this course contributes to the broader domain of healthcare data by familiarizing students with the type of big data that is most common in healthcare, namely claims data. You can acces my online course about healthcare claims data here: ©