For many years, healthcare claim payments have been withheld from providers after submission. Several reasons have been promulgated as to why this occurs, including:
- A payer's inability to adjudicate claims in a timely manner due to submission errors.
- Program integrity staff possessing insufficient time to assess whether claims reflect potential fraud or abuse, often due to shortcomings tied to existing legacy technology, rules and edits, and finite resources.
Regardless of the reason, the result of this issue is increased financial burden on ethical providers. The payment float period requires "good" providers to pursue lines of credit or loans to manage their cash flow, which adds interest costs to an already thin margin.
With the continued escalation of healthcare costs and the implementation of healthcare reform on the horizon, it is imperative that healthcare constituents implement new or improved existing solutions to identify, prevent and reduce fraud and abuse. There is an immediate opportunity to advance beyond the existing healthcare system's current approach and meaningfully reduce the cost of services through more innovative mitigation solutions.
Claims currently are paid and reviewed afterward to ascertain if they violated documented policies. If they are found to be questionable, organizations such as Medicare then seek reimbursement through the aforementioned "pay and chase" strategy. Zone Integrity Contractors or RACs then are utilized to protect the Medicare Trust Fund.
In order to make a significant impact on fraud and abuse in healthcare, new technology and predictive models with real-time assessments must be utilized to review suspect claims prior to payment, thereby minimizing the impact to honest and ethical providers and mitigating the administrative impact caused by RACs.
Lessons Learned from Financial Services
Fortunately, proven solutions can be leveraged from financial services to identify and prevent fraud and abuse in an effective manner. The financial services industry has a fraud deterrent infrastructure that has been validated during the last several decades as stable and efficient. Real-time technology and advanced predictive analytics surgically are utilized to target the small minority of suspect perpetrators and minimize impact on honest accountholders. Those analytics also are utilized, versus hard-coded rules, to create a statistical model for future behavior. A robust predictive model is significantly more flexible than rules-based solutions for several reasons:
- It identifies fraud and abuse patterns outside of judgmental or policy edits.
- It can focus on high-risk segments and create the capability to identify more fraud and abuse while impacting fewer "goods."
These predictive models evaluate transactions at the time of processing and before payment. If the transaction appears to be high-risk, or out of the ordinary, it will be flagged. The predictive model then will provide a reason why the transaction was flagged, with the flagging and application of reason codes done in an automated manner.
Only a small percentage of fraud transactions are declined immediately, with a minimal number of transactions queued to be researched prior to settlement. Subsequent interactions are efficient and timely, typically performed within hours. Communications are not adversarial, but customer-service oriented, reflecting an "innocent until proven guilty" mentality.
During an October 2010 vendor day meeting, Peter Budetti, M.D., J.D., indicated that a predictive modeling solution was an exciting new tool to prevent healthcare fraud in Medicare. If future demonstrations and models are successful in fighting fraud, the role of the RACs will be greatly reduced or eliminated.
Benefits of Prepayment Fraud and Abuse Prevention
A real-time fraud and abuse prevention solution is both an effective means to reduce problems as well as an efficient platform that offers a timely opportunity to interact with other providers. Similar to financial services, such a solution provides for the detection of unusual behavior patterns prior to payment, actually reducing the number of negative interactions while maximizing detection of fraud perpetrators who must be prosecuted. The solution also eliminates the high false-positive rate that typically is accompanied by the rules-based procedures used today. The new technology only focuses on questionable transactions or providers and pays the rest.
The proposed predictive solution has several benefits for a provider that can be quantified financially:
- Minimized impact on ethical providers, reducing payment declines and administrative costs for appealing or supplying medical records.
- More timely communication with providers for out-of-behavior claims - requesting medical records or information is completed within days, not months.
- The reduction or elimination of the role played by RACs, reducing administrative burden and costs associated with audits.
- The swift implementation of a policy change to pay providers sooner, reducing a provider's need to access outside funds to manage cash flow.
The implementation of a real-time fraud and abuse prevention solution represents a long-term opportunity to reduce or eliminate the role of RACs, as well as a method by which "good" providers are paid sooner, because it addresses the risks associated with historical fraud detection solutions.
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About The Authors
Allan Klindworth, MBA
W. Allan Klindworth is an entrepreneurial leader with over 20 years experience in both healthcare and financial services. Allan has a history of driving change by developing and deploying technology and predictive solutions that automate fraud risk management decision-making. In 2009 Allan co-founded TerraMedica, a healthcare technology company that specializes in solving the fraud and abuse problems for healthcare stakeholders.
Stephen T. Parente, PhD
Stephen T. Parente, Ph.D is the Minnesota Insurance Industry Professor, Department of Finance, Carlson School of Management at the University of Minnesota. Dr. Parente also holds an appointment as adjunct faculty member at Johns Hopkins University and once served as a Legislative Fellow in the US Senate during the Bush and Clinton Administrations' health reform initiatives.
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