Digital Transformation, Big Data, Analytics, IoT, Mobility, Cloud are the hottest terms around, with lot of confusion even in matured organizations. This is an effort to simplify the area.
Friday, 8 June 2012
Fraud detection in Medicaid / Medicare
Medicaid is a USA government run healthcare program for the poor, elderly and disabled, which is jointly funded by the states and the federal government
Medicaid has been the top priority on many state budgets. Considering the huge investments from both government as well as states, there are whole lot of malpractices to grab this money.
Experts estimate that abuses of Medicaid eat up at least 10 percent of the program’s total cost all over USA which is a waste of more than a $80 billion a year.
Let us see what the types of Medicaid Fraud are:
·Doctors billing for over 24 hours per day of procedures
·Use of single patient IDto generate billing acrossmultiple providers
·Fake companies invoicing for services which were not provided
·Altering the claims forms or misusing the codes to receive higher payment amounts
·Pharmacists filling prescriptions for dead patients
·Home health-care companies demanding payment for treating clients actually in the hospital
·Home health care, visiting nurses billing additional amount
·Patient transportation services claiming charges for patients who are not even moved to and from hospitals/home
One industry example mentioned in the reports: In one brash scheme, immigrants set up a network of fraudulent medical-supply stores in the Southwest, hoping to cheat Medicaid and Medicare. The gang hired recruiters to bring them innocent patients eligible for Medicaid or Medicare. They then paid off local doctors to prescribe motorized wheelchairs worth $7,500 but instead gave them motor scooters worth just $1,500, pocketing the difference. Investigators shut down the scheme after noticing billings for wheelchairs in Arizona, Texas, and other states scaling into the hundreds of millions of dollars.
How analytics can help in Fraud detection/prevention:
·Detecting the patterns of fraud in the bills provided by doctors, hospitals, nurses
·Profile & segment claimants to identify those who are likely to commit fraud
·Detecting over payments due to provider’s incorrect billing
·Use of Big data platforms to analyze huge volumes of data for fraud detection