Analytics in healthcare
Financial intelligence (FININT) is the gathering of information about the financial affairs of entities of interest, to understand their nature and capabilities, and predict their intentions. Generally the term applies in the context of law enforcement and related activities. A feature in analytics in healthcare organisations to forecast certain aspects of the healthcare financial management process. Predictive models are used today in various industries, ranging from weather forecasting to anticipating how many of a company’s products will be sold during a specific period of time. With small IT departments, Big Data has a lot of scope for growth in the healthcare sector.
Analytics in medical billing
Claim management are an important segment in healthcare where analytical ability can play a pivotal role. Introducing analytical tools for the claims allows staff to analyse the denials more easily. While the processing of claims and collection-capabilities of analytic platforms are quite useful, many of the users are attracted to these tools and products due to their easiness of operation. Payers can now detect certain patterns in submitted health claims data and are able to weed out the fraudsters. For insurers, examining the claims data is a great opportunity for them to control their expenses and costs. It helps in effectively controlling expenses, avoiding unnecessary costs and remaining competitive in pricing their patients. Thus, many providers look into the data hosted by their servers for better fraud detection. Not only insurers, but hospitals as well as cyber security will also require big data, intelligence and analytics. Cyber security industry is heavily investing in machine learning to be resistant to cyber attacks.
Analytics in hospital test
Adapting analytics tools have shown a dramatic growth in the result extracted from mining of data. Examples of how hospitals have used available analytics tools range from the review and analysis of hospital productivity, performance evaluations of treatment plans, charting drug abuse, and patient’s risk factors. All of these are areas in which big data initiatives can provide the tools to help transform healthcare, improve patient care and reduce costs. Nowadays, radiology has gone beyond imaging. Say in case of MRI scan, if proper software is used, then a lot more information can be tracked and it would lead to a more accurate diagnosis and treatment.
Value-based (data-backed) care is much more organised when it comes to providing proper outcomes. It is of no surprise that machine learning solutions will soon emerge as the new norm beyond Security Information and Event Management (SIEM) and ultimately displace a large portion of heuristics, and signature-based systems within the next five years.
