The yearly spending on healthcare by the US is more than $3 trillion and will touch $5 trillion in 2025, per industry reports. Fraud, waste, and abuse (FWA) in healthcare are not unknown problems. Even the magnitude of these problem is not known in entirety. Estimates from the Center for Medicare and Medicaid Services (CMS) put FWA yearly at $272 billion, whereas the National Health Care Anti-Fraud Association (NHCAA) estimates that around $90 billion each year is lost to FWA. Clearly, healthcare fraud, waste, and abuse (FWA) is a mammoth problem in US healthcare system. Below is how NHCAA differentiates among FWA:
- Fraud: Healthcare fraud is an intentional deception or misrepresentation that the individual or entity makes knowing that the misrepresentation could result in some unauthorized benefit to the individual, or the entity or to some other party. For example,
- Billing for medical services or equipment that were never performed or delivered
- The deliberate performance of medically unnecessary services for the purpose of financial gain
- Kickbacks - receiving payment or other benefit for making a referral
- Waste: It is healthcare spending that can be eliminated without reducing the quality of care such as overuse/underuse or ineffective treatments. It is generally caused by misuse of resources. For example,
- Overutilization of services and/or procedures that, directly or indirectly, result in unnecessary costs to the insurance companies
- Payment for services that fail to meet professionally recognized standards of care
- Prescribing a medication for 30 days with a refill when it is not known if the medication will be needed
- Abuse: Healthcare abuse is defined as improper actions or billing practices that creates unnecessary costs and is purely unintentional. Abuse involves the payment for procedures, items, or services that are billed with no intent to deceive, but the outcome is inefficiency that results in unnecessary cost to the payer.
- Charging in excess for procedures, services, or supplies
- Services billed does not comply with the coding guidelines
In fiscal year (FY) 2016, the government recovered over $3.3 billion as a result of healthcare fraud judgments, settlements, and additional administrative impositions in healthcare fraud cases and proceedings from Medicare alone. Since its inception in 1997, the fraud and abuse control program has returned more than $31 billion to Medicare alone. In the last fiscal year, this program has returned $5.0 for each dollar invested. The return on investment for commercial players would be much more as the regulations are not that strict. Considering the magnitude of the problem and the rate of return, I think this would be the most valuable healthcare proposition for the next few years.
There are dozens of vendors offering healthcare provider solutions in the market who are addressing this problem through healthcare IT solutions. However, there is huge scope of innovation around technology-driven solutions to combine domain, analytics, technology, and predictive modeling in healthcare to combat this problem. Most of the solutions focuses on analytics, predominantly pulling out the outlier cases, exaggerated payments, more focused toward volume and dollar value; however, the payers progressively look for solutions which promote cost efficiencies and reduce revenue leakage with the ultimate goal of reducing the cost of care.
The ideal solution should use a set of decision-making algorithms coupled with a knowledge base of facts and observations and medical judgment incorporated in an automated data processing system to deny inappropriate medical claims. It should pay only appropriately coded claim amounts. It should have an inbuilt prospective model and set of rules that could identify and flag fraudulent healthcare claims before the payment is being made with an overall objective of minimizing the impact on providers, beneficiaries, and payers, ultimately leading to minimized healthcare costs.
The use of data analytics and predictive modeling have been a growing trend within the detection of FWA in healthcare programs for a little over five years and their popularity and significance will continue to expand. Having substantial experience in building such healthcare IT solutions, my point of view is that a prospective approach to combat fraud is ideal rather than going for a solution which runs on paid claims or post-adjudicated claims (also known as pay-and-chase model) as the recovery process is time-consuming and payers have to chase the money which has already gone out of the door due to mistakes, billing errors, and intentional fraud schemes.
I feel that the most important thing is to have an impeccable collaboration among statisticians, technology experts, and claims investigators during development of a robust FWA solution. The perfect solution that can process, analyze, and help enterprises take decisions for proper claims submissions and payments promptly can bring immediate return on investment. To build such a “perfect solution,” we need to have the right balance of data, analytics, and technology, predictive modeling in healthcare, and human experience to stay ahead of the race. Indeed, the use of data analytics in healthcare could take it to the next level.