Answering the Three Biggest Questions About Waste in Health Care Payments Payer Organizations Turn to Predictive Analytics

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1 white paper Answering the Three Biggest Questions About Waste in Health Care Payments Payer Organizations Turn to Predictive Analytics December 2012»» Summary My special investigations unit keeps an eye on fraud and abuse, but what about waste? The line between fraud and waste may be a fine one, but when you re convinced that a dubious claim is not fraudulent, does your payer organization have a way of looking into it? And if your organization does pursue waste, are you catching it before payment goes out the door? Or do you have to pay and chase? What if your fraud management system were smart enough to detect waste also? The potential for savings is huge in three commonly overlooked areas: too many tests, inefficient claims processing and premature discharge of patients. By using predictive analytics and models, payer organizations can save money by identifying, controlling and stopping unnecessary health care payments. This paper describes examples of waste in health care payments and examines the advantages of predictive analytics as woven into FICO Insurance Fraud Manager. Specialists in medical claims, quality, benefits and finance can use the paper as a point of departure for reducing losses due to waste. Make every decision count TM

2 »Money» Going Out the Door Estimates suggest that as much as $700 billion a year in healthcare costs do not improve health outcomes. They occur because we pay for more care rather than better care. Peter Orszag, director of the White House Office of Management and Budget 3 Since the late 1990s, multiple studies have looked at waste in health care payments defined as spending on higher utilization that does not, on average, produce better outcomes and steadily estimated waste in the US at 20% 30% of total health care expenditures, or $700 million to $1.2 trillion annually. 1 The HRI study 2 highlighted several categories of waste in US health care spending. Almost $450 million of that waste moves through insurers claims operations and is within reach, but the resources for closer scrutiny rarely suffice, and prompt-payment legislation compels insurers to reimburse large numbers of claims they do not have the time to second-guess. As insurers and payer organizations take aim at waste with their claims management systems, three particular categories are ripe for examination. In all these scenarios, the waste is obvious; the problem is that the scenarios themselves remain hidden in the large volume of claims data. 1. Too many tests $210 billion per year wasted Lab tests are a valuable first step in any diagnosis, but the sheer volume of tests performed in the US is conducive to waste. Example: An excessive number of tests are performed on the same patient in the same day. Explanation: The provider is unaware of the redundant nature of the tests, or is intentionally maximizing reimbursements. Example: Different providers perform identical tests on a patient within a short period of time. Explanation: The patient visits his or her general practitioner, who performs an EKG. The test is positive, so the patient receives a referral to a cardiologist. The G.P. does not send the EKG results to the cardiologist, who performs another EKG a few days later. Example: Providers order multiple, redundant tests aimed at ruling out a given diagnosis. Explanation: An emergency room patient presenting with a head injury receives x-rays as well as a CT scan, suggesting that the provider is practicing defensive medicine against a possible lawsuit. Example: Provider performs the same test repeatedly on a patient over weeks or months. Explanation: The insurer is billed for identical hearing tests performed by the same provider on the same patient over an extended period of time, suggesting an experimental or otherwise non-standard course of treatment. 1 The Dartmouth Institute for Health Policy and Clinical Practice, Reflections on Geographic Variations in U.S. Health Care, May PricewaterhouseCoopers Health Research Institute (HRI), The price of excess: Identifying waste in healthcare spending, August 2009, 3 In Thomson Reuters, Where Can $700 Billion in Waste be Cut Annually from the U.S. Healthcare System? October 2009, Fair Isaac Corporation. All rights reserved. page 2

3 2. Inefficient claims processing $210 billion per year wasted The HRI study cited several barriers to reducing inefficiency in health care payments, including complexity of administration, die-hard business practices and low government priority. Whatever the cause, inefficient claims processing remains a source of payment problems that insurers could successfully address if they were able to identify them. Example: Duplicate billing for routine lab tests. Explanation: The provider orders tests, and the office manager bills the insurer as if the lab tests were performed on site. In reality, the patient has them performed at a lab, which also bills the insurer. These claims may come in with different dates of service, making detection even more difficult. Example: Early billing for prescription refills. Explanation: As a convenience to regular customers, a pharmacy refills a 30-day supply on day 25, whether the patient has requested the refill or not, and notifies the patient that the prescription is ready for pickup. The insurer pays the pharmacy at the point of fill. The patient accumulates an unnecessarily large supply of medication or, if the patient fails to pick up the prescription, the pharmacy ends up returning the pre-ordered medication to stock, often failing to reverse the charges. Example: High frequency of prescription refills. Explanation: A pharmacy fills a prescription for birth control pills (28 per month) but miscodes as birth control patches (28 per month). Since only 4 patches are needed in a month, the insurer over-reimburses the pharmacy by a factor of 7. Example: High frequency of billing for physical exams. Explanation: A provider bills for repeated physical exams, rather than using problem-focused evaluation and management codes. Example: Routinely billing for problem-focused evaluation and management. Explanation: To maximize reimbursement, a provider seeks out a patient issue during a routine physical and then adds on a problem-focused evaluation and management code. Example: Bypassing negotiated rate for drug reimbursement. Explanation: An insurer negotiates to reimburse a supplier at a discounted rate for a special drug, but the supplier submits the claim from a different office or business entity, so the insurer inadvertently reimburses at the higher rate instead of the discounted rate Fair Isaac Corporation. All rights reserved. page 3

4 3. Premature discharge of patients $25 billion per year wasted When providers overreact in the name of lower costs, their efforts run the risk of backfiring. Example: Premature claims subsequent to hospitalization. Explanation: A hospital treats a heart attack patient, then releases the patient early to free up a bed. The hospital is paid on a diagnosis-related group (DRG) basis. The patient relapses, the hospital readmits the patient and submits a second claim with a slightly different DRG. Traditional, rules-based claims edit systems are designed to take into account a limited number of factors surrounding a claim. Payer organizations need more context and intelligence in their tools to spot areas of waste like too many tests, inefficient claims processing and premature discharge of patients.»three» Important Questions that Predictive Analytics Help Answer Smart insurers supplement their traditional efforts with predictive analytics and models built on the wealth of claims data. While the technology behind these tools and models is complex, the questions it helps answer for payer organizations are in fact quite simple: What don t I know about? First, insurers want to know the size of the waste problem, because waste is often hidden, and there is very little time available to find and measure it. Rules- and business intelligence-based systems work when the problem is obvious enough that you can write a rule on it, but predictive analytics find less intuitive patterns and relationships among events and shine light on aberrancies. The combination of rulesbased claims systems with statistical analysis tools and predictive analytic models gives insurers the insight they need to identify, control and stop waste in health care payments. Once payer organizations understand how much waste costs them, they can take action. Now, how can I control it? Having identified the types of claims most affected by waste, insurers then set priorities for dealing with them. The bigger the profit leak, the sooner the organization will take steps to control it by educating medical providers, requesting more documentation and assigning employees to research aberrant patterns. Better yet, how can I stop payment on it? The crucial step in solving the waste problem is to stop payment before it goes out. Evolved predictive analytic systems pointedly advise insurers not to pay on specific claims. They not only use rules, profiles, models and statistics to score claims for waste quickly, but they also alert insurers in time to keep the money from going out the door. Even in an era of ever greater pressure to reimburse promptly, these systems save payer organizations from having to chase down and attempt to recover payments made on wasteful claims Fair Isaac Corporation. All rights reserved. page 4

5 »» How Predictive Analytics Make a Difference Rules process a limited quantity of data. Consider a rule like, If the policy allows one EKG per year, then score two claims for EKG in a month as potentially wasteful. In that limited scope, the second EKG would score high for waste. But in an analytical model that took into account factors like the patient s recent heart attack, the second EKG would not score high for waste. Conversely, a rule that permits three routine child check-ups in a year might tolerate all three of those claims bunched together in subsequent weeks. But an analytical model weighing years of claims data, provider data and patient data would spot patterns that scored high for waste. The analytics change with behavior Flexibility is another advantage that predictive analytics add to rules-based systems. As insurers identify patterns of wasteful claims, they can write rules to stop them, but as the patterns change, those rules may no longer apply. Because analytics reflect the constantly changing reality contained in billing data, they are able to identify patterns even as they evolve.»» Conclusion Payer organizations no longer need wonder about the pervasiveness of wasteful claims in health care reimbursement. They can now use predictive analytics and models to supplement their existing systems and detect waste before it costs them money. FICO Insurance Fraud Manager shows organizations what they don t know about waste in health care billing, allows them to control it and helps them stop payment on wasteful claims. One early adopter, Government Employees Hospital Association (GEHA), a 450,000-member federal employee health plan, realized hard-dollar savings from fraud, abuse, error and waste in one out of eight providers, enabling it to achieve 3:1 ROI within the first year. Payers in both private and public sectors are beginning to recognize the value that predictive analytics and models add to rules-based systems, and they are beginning to realize the benefits. As analytics play a greater role in addressing waste in health care spending, organizations that begin today will soon be in a position to lead Fair Isaac Corporation. All rights reserved. page 5

6 about FICO FICO (NYSE:FICO) delivers superior predictive analytics solutions that drive smarter decisions. The company s groundbreaking use of mathematics to predict consumer behavior has transformed entire industries and revolutionized the way risk is managed and products are marketed. FICO s innovative solutions include the FICO Score the standard measure of consumer credit risk in the United States along with industry-leading solutions for managing credit accounts, identifying and minimizing the impact of fraud, and customizing consumer offers with pinpoint accuracy. Most of the world s top banks, as well as leading insurers, retailers, pharmaceutical companies and government agencies, rely on FICO solutions to accelerate growth, control risk, boost profits and meet regulatory and competitive demands. FICO also helps millions of individuals manage their personal credit health through Learn more at For more information North America toll-free International web (0) info@fico.com FICO and Make every decision count are trademarks or registered trademarks of Fair Isaac Corporation in the United States and in other countries. Other product and company names herein may be trademarks of their respective owners Fair Isaac Corporation. All rights reserved. 2919WP 10/12 PDF