January 4, 2011

Part I: Harnessing Health Information in Real Time: Lessons from the Financial Services Industry to Mitigate Healthcare Waste, Fraud and Abuse

By

sparente100ED. NOTE: This is the first of a two-part series on healthcare waste fraud and abuse by Stephen T. Parente, Ph.D, Professor, Department of Finance, Carlson School of Management, University of Minnesota. In Part II, Parente offers policy solutions.


Health information technology (IT) is a critical component of a high-performance healthcare industry. At its best, this technology not only can alert a patient or physician to past medical history, avoiding redundant services or diagnostic tests, but also provide new information that could save someone’s life and offer previously unknown options for health improvement and medical care financing.

It also can speed payment for healthcare providers, as well as be used more strategically to reduce waste, fraud and abuse. Today, fraud prevention technologies are being proposed for review of medical care at an increasing rate.

The optimism behind these technologies rests on the success of their use in the financial services industry nearly 20 years ago to mitigate credit-card fraud. This article describes the case study from financial services’ original operability platform and discusses the opportunity facing the U.S. today to reduce billions of dollars in fraudulent payments. 

Lessons from Financial Services IT Systems

Interoperability has been deployed successfully in some industries, arguably most notably so in the financial services industry. One of the largest differences between the medical community and the financial services industry is that the latter is rewarded largely for velocity of information, since it provides revenue with each transaction.

To provide some perspective, the case of the financial services industry rallying against the threat of credit-card fraud is considered below. To succeed, firms had to break away from their “silo mentality” for storing and using data, and agree to link their data for the collective purpose of avoiding fraud.

In the late 1980s, financial services firms were at a crossroads in identifying and preventing fraud. Tools and technology were immature, and firms typically would pay for a product and service but investigate fraud after the fact, otherwise known as “pay and chase.”  Culturally, key stakeholders were inclined to look at their solutions with a retrospective approach, and only within their own credit portfolios. Fraud mitigation was a manual process based on a small number of fraud cases detected by prior experience. This process was highly ad hoc, and put in place by hard-coding cases into a credit-card issuer’s processing system.

Furthermore, the detection methods at the time could not measure how much fraud was prevented or if a new test strategy or treatment was more or less effective than a previous strategy. It was easy for perpetrators to go undetected. If a perpetrator was identified, it was usually after numerous fraudulent transactions. Perpetrators also often discarded stolen credit cards after several transactions and moved their scheme to another issuer. Similar to medical providers or insurers, banks acting as credit-card issuers did not share information; they operated within the silo of their own business and within their own market. To move forward, the financial services industry required a change of culture and practice, supported by new technology that bridged data silos with highly structured and proprietary data systems.

In 1993, a technology-based incentive to change fraud detection in financial services arose: the introduction of predictive models for identifying fraud. Predictive modeling historically had been used in the financial services industry for underwriting credit and loans, and it was an accepted and proven method from several decades prior. The predictive-modeling technology is analogous to the U.S. health insurance industry’s use of risk adjustment of medical claims data to determine future premiums or provider reimbursement.

A critical innovation was the development of a fraud-scoring, predictive model designed to perform real-time assessments on every single transaction. The volume of transactions handled was on the same scale as healthcare transactions in the medical provider and insurance communities. A workflow management and workstation methodology was introduced to automatically and efficiently present only high-risk transactions to the system for automatic rejection or review by a fraud analyst.

Because the solution was expensive to implement, it initially met some resistance from credit-card issuers. However, as its value was demonstrated to individual firms, acceptance started to grow. Credit-card issuers were discovering that their initial return on investment was generally between 10:1 and 30:1. This means that for every dollar spent on the system, the issuer saved between $10 and $30 in avoided fraud losses. Since the results of the initial implementations were so compelling, other credit-card issuers immediately requested to have the solution implemented in their systems. They recognized not only the positive results but also the automated infrastructure used to interact with each consumer personally. Initial fears that credit-card account holders would react negatively to being contacted and asked if they were using their credit card proved to be unfounded. In fact, the approach of calling consumers and stressing the “fraud protection” intent of the call became a public-relations success. Today, virtually all credit-card issuers use a real-time solution, which includes predictive modeling and workflow management, to prevent fraud within their portfolios.



In today’s healthcare world, a similar challenge faces the proponents of interoperability bridging institutions. In the end, the financial services industry learned there was more to gain than to lose by implementing limited cooperation and data exchange in order to prevent billions of dollars in credit-card fraud. The same lessons apply to healthcare IT investments and applications that range from covering fraud mitigation to comparative effectiveness research.

Parallels to Healthcare

Healthcare and financial services share several opportunities that arise from more effective use of IT. For financial firms, fraud mitigation was a powerful incentive to integrate data, as were ATMs. Once data was organized more centrally in the financial services industry, firms had much better access to information about the performance of retail bank branches and regions. This promoted greater regionalization and later enabled effective nationalization of bank operations. Branch managers were rewarded for better performance in the same way that a hospital CEO could be awarded a raise for performance in a more integrated system. It also became easier to spot weaknesses that required additional staff to address, as well as opportunities to redirect, reduce and re-deploy staff to different locations. Finally, it made the merger and acquisition process for banks easier at all scales.

This is not just a story of Bank of America growing large in the span of 15 years, but also of smaller banks and credit unions that achieved better economies of scale by banding together. In the same way, integrated health IT could empower accountable care organizations and allow smaller physician practices to achieve economies of scale and scope through acquisition from larger entities with waiting interoperable health IT platforms.

The financial services model for mitigating fraud may become more advisable to the healthcare industry as health reform deploys and evolves. Although healthcare fraud and abuse estimates vary widely, constituents agree that the problem is enormous, and growing each year. In testimony before the U.S. Senate Committee on the Judiciary on May 20, 2009, Malcolm K. Sparrow, a prominent expert on fraud, said that healthcare fraud and abuse costs hundreds of billions of dollars per year, with the actual figure ranging anywhere from $100 billion to $400 or $500 billion.(1)

 

In 2002, a study by the Government Accountability Office estimated that one out of every seven dollars paid to Medicare is lost to fraud.(2)  This means that in Medicare alone, there was almost $70 billion in fraud and abuse for 2008 (within a projected $466 billion in total Medicare spending).(3)

 

Extrapolating this assumption for 2017, Medicare would have more than $120 billion in fraud and abuse (within a projected $857 billion in total Medicare spending).(4)  According to experience and research, the vast majority of fraud and nearly all of the abuse is perpetrated by healthcare providers.(5)

 

About the Author

 

Stephen T. Parente, Ph.D the Minnesota Insurance Industry Professor, Department of Finance, Carlson School of Management at the University of Minnesota who, 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.

Contact the Author

sparente@umn.edu

 

To comment on this article go to: editor@racmonitor.com

 

To read article entitled, "Block and Tackle in Dealing with RACs and Medicare Baby Boomers," please click here

 

 



(1) U.S. Senate Committee on the Judiciary, Subcommittee on Crime and Drugs, Criminal Prosecution as a Deterrent to Health Care Fraud, 111th Cong., 1st sess., May 20, 2009, available at http://judiciary.senate.gov/hearings/testimony.cfm?id=3860&wit_id=7953 (accessed November 14, 2010).

(2) U.S. Government Accountability Office, Medicare Fraud and Abuse: DOJ Continues to Promote Compliance with False Claims Act Guidance (Washington, DC, April 2002), available at www.gao.gov/new.items/d02546.pdf.

(3) Centers for Medicare and Medicaid Services, National Health Expenditure Projections 2008–2018 (Washington, DC: U.S. Department of Health and Human Services, 2008), 5, available at www.cms.hhs.gov/NationalHealthExpendData/downloads/proj2008.pdf (accessed November 14, 2010).

(4) Ibid.

(5) Coalition Against Insurance Fraud,Go Figure: Fraud Data,” available at www.insurancefraud.org/stats.htm (accessed November 14, 2010).


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