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 ID  to generate billing across  multiple 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
·   Identify connections of fraudsters via social network analysis
·   Apply analytics with combination of methods of anomaly detection, business rules, predictive modeling & network analysis
·   Advanced text analytics to analyze unstructured data to reveal fraudulent activities
There are some steps, which individuals can take to prevent fraud:
·   Review your Medicaid bill for each service. Are the dates correct?
·   Only give your Medicaid number to those needing it. (Doctors, hospitals, clinics, etc.)
·   Don't lend your Medicaid card to anyone.
·   Never request medical services or equipment you don't need.
·   Don't sign blank forms for medical services or equipment.
·   Request and retain copies of anything you sign.

4 comments:

  1. Hi I my name is Arun and am thinking into getting into analytics but the problem is, me not being from a premier institute, getting my CV across is becoming really difficult. So, with some added skills like SAS etc I would like to try again. I get all kinds of recommendations on what books I should read but the actually problem lies in the fact that I cant have hands on experience in with SAS .. can you help me with an alternative .. or let me know how can I practice SAS on my personal computer ?? .. Thanks !

    ReplyDelete
    Replies
    1. use open source tools like R or Rapid Miner for hands on experience.

      Delete
  2. Great post about the fraud detection, I really enjoyed it so thanks for sharing this post.

    ReplyDelete
  3. Hello Sandeep,

    Good article in terms of introduction at 30,000 feet perspective. Would be nice to see / read about the execution framework in little granularity.

    Thanks,
    Gaurav

    ReplyDelete

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