Healthcare fraud and waste can be prevented with suitable big-data solutions. Medical billing is vulnerable to diverse errors along with waste as well, owing to procedural complexities and the multiple types of products that are available.
Several mistakes are often spotted across electronic medical records, and many patients do not check their insurance documents carefully in order to detect them. FWA is the acronym for fraud, waste, and abuse and denotes both unintentional and intentional billing mistakes which end up leading to serious costs for insurers.
Frauds are a major issue along with healthcare scams and waste of resources in the healthcare or medical sector, while patients are sometimes involved along with fraudsters.
The cost of healthcare fraud could be anywhere around $68 billion per year in the United States alone, as per the National Healthcare Anti-Fraud Association.
Some other sources indicate figures nearer $200 billion or almost 3-10% of overall healthcare spending.
Yet, big data analytics in healthcare can help tackle these issues mentioned earlier, along with others like phantom billing¸ enabling the identification of patterns that are abnormal and other aspects that are based on historical information that is updated continually for ensuring accurate results. This can help insurance companies recover some of these losses as well.
Data analytics can help find errors in inputs, while also combating overcharging and unintentional waste, along with making CMS more effective and lowering the number of times for data inputs by personnel.
It thus greatly lowers the chances of errors. At the same time, it can also help tackle input errors since medical billing codes currently have seven digits in several cases. These may lead to higher patient bills simultaneously while leading to trust and reputation being damaged.
There are other frauds where patients and other personnel may inflate bills or subtly charge more for services rendered. Fake diagnoses are sometimes also deployed for enabling patient approvals with regard to disability payments by the Government.
Big data gathers information from several sources including certifications of physicians and zip codes of patients insurance companies can analyze and compare providers and populations for identifying fraudulent patterns, while contemporary tools can do this automatically as well.
Yet, bigger and more extensive data sets are necessary for better identification in this regard. Big data mechanisms have to be trained to detect any such false positives and other abnormal patterns.
The program should be able to individually identify and flag instances with proper claim-based analytics and other detection mechanisms.
There should also be a system of analysis based on multiple methods or segments. At the same time, as mentioned earlier, the larger the dataset, the better the chances for accuracy.
Big data can play a major role in combating healthcare fraud and waste, identifying abnormal patterns and practices while joining the dots to flag broader schemes as well.
Big data analytics can analyze data in several categories to identify abnormalities and fraudulent claims, flagging individual instances with claim-based analytics and several methods. It can help with greater data visualization while also lowering inflated or fraudulent billing instances.
Using big data analytics can help greatly in preventing healthcare waste and fraud, while lowering financial losses of insurance companies, and helping identify fraudulent claims. It can also enable smoother operational mechanisms throughout the industry.