Advanced Analytics Systems for Smarter Benefits, Claims, and Entitlement Management


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This IBM white paper introduces the field of analytics, and discusses how analytics can be utilized in claims and benefits processing systems. It also provides an example of an advanced analytics system developed for the U.S. Social Security Administration.

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Advanced Analytics Systems for Smarter Benefits, Claims, and Entitlement Management

  1. 1. Analytics Solutions Advanced Analytics for Smarter Benefits, Claims, and Entitlement Management Toward a Smarter Government January 2010
  2. 2. On behalf of the IBM Analytics Solution Center in Washington, D.C., we are pleased to present this white paper, “Advanced Analytics for Smarter Benefits, Claims, and Entitlement Management.” Improving the performance of the nation’s systems that handle benefits, claims, and similar citizen requests has been on the top of many agencies’ priority lists. Recent newspaper headlines have highlighted the need to improve these systems. Fortunately, today’s enterprise information management systems with advanced analytical capabilities can be employed to provide faster processing, while at the same time reducing fraud and improper processing. This white paper introduces the field of analytics, and discusses how analytics can be utilized in claims and benefits processing systems. It also provides an example of how such a system was developed for the U.S. Social Security Administration. We hope that this white paper will be useful to managers across the government as they continue to transform their agencies to meet the needs of their constituents. Christer Johnson Stephen Brady Partner Sr. Technologist North American BAO Advanced Analytics Leader Analytics Solution Center IBM Global Business Services IBM Federal About the Analytics Solution Center, Washington, DC The ASC mission is to: • Help clients understand how analytics can benefit the performance of their mission through thought leadership events. • Demonstrate analytics solutions and technology options for solving your organization’s mission problems. • Provide collaboration to seek and explore innovative, yet realistic, technical approaches. • Support the development of proofs of concepts and pilot analytical solutions. Customers, Partners and individuals are invited to join this community by visiting our web site at (IBM DeveloperWorks registration required to join). We provide access to a range of useful content regarding information technology that can be applied to the missions of government departments, agencies, and organizations. Contact to get more information or to ask us a question via email.
  3. 3. Introduction of 97 days back to only 20 days. Claims processing is fundamentally a customer service While processing speed and throughput are important business, evaluated in terms of effectiveness in measures of the effectiveness of a claims handling delivering benefits to claimants. Particularly in the system, consistency in the way claims are handled and government domain there is a tension between the view avoidance of fraud and abuse are essential as well. of benefit programs as entitlements and the demand of Confidence in the fairness of a government program is the electorate that public resources not be wasted. In undermined when similar claims may be honored in times of stress, such as natural or economic disasters, one geographic region and denied or only partially both the number of claims to process, and the urgency granted in others. This kind of inconsistency can result of those claims in terms of human need and suffering when human beings are forced to interpret complex are elevated. guidelines and can not easily reference previous experience or evolving trends. The extreme cases of problems in claims handling are of course the ones that rise to the level of news Sources as diverse as USA Today and the US Senate coverage. Still when some figures come to light, such Panels on Healthcare report that as much as 10% of as the average of 196 days required from the time a the more than a trillion dollars spent each year on claim for veterans benefits is submitted to the Veterans healthcare in the United States may be wasted on Administration until the final disposition of that claim, or fraud and abuse. Clearly the problem is a significant the average of 145 days required by the US Immigration one. While organizations may devote a lot of effort to and Naturalization Service to process an application for chasing down and recovering payments in the case of citizenship, it is hard to argue that improvements are not fraudulent or flawed claims, better still would be to warranted. At the other end of the spectrum, when the identify the questionable claims before payment is Center for Medicare and Medicaid Services determines made, as part of the claims processing workflow. how to satisfy a claim within an average of 5 days, some could wonder whether unjustified claims are being paid The solution to smarter claims handling derives from a without being subjected to proper scrutiny. At either combination of improved process design, application of end of the scale, applying more intelligence to the information technology, and accommodation of the processing of claims or requests can ensure timely people who will use the technology and conduct the handling without compromising on verification of the process. Simplifying and facilitating data entry and use validity of those claims. The Appendix to this paper of content management systems make unstructured outlines a solution developed by IBM for the U.S. Social data more accessible and usable. Introducing rule- Security Administration, using the principles described based management of the workflow associated with in this paper which allowed the processing of certain claims processing ensures greater consistency and classes of disability benefits to be cut from an average transparency in the handling of claims, as well as
  4. 4. contributing to more expeditious processing. And the factor that actually makes the process “smarter” is analytics. Analytics contribute to understanding how the system operates, allow you to develop rules for governing and directing the flow of claims that reflect both organizational objectives and a deep understanding of actual and intended outcomes, and support continual reassessment of both internal and external factors and their impact on the effectiveness of the claims handling process.
  5. 5. Analytics the creation of structured metadata and indices. The term “analytics” appeared a couple of times in the Descriptive analytics help provide an understanding of introduction, but what does this term refer to? In fact, the past. Predictive analytics use the understanding of what we call analytics today is really an extension and the past to make “predictions” about the future. For aggregation of efforts that have been pursued for a example, a particular type of claim that falls into a number of years, as people have tried to apply category that has proven troublesome in the past might computers to help improve the quality of decisions being be flagged for closer investigation. Descriptive made by human beings. analytics may begin by providing a very static view of We further subdivide analytics into three categories of the past, but as more and more instances are increasing complexity and impact: descriptive, accumulated in the experience base of the system, and predictive, and prescriptive. with algorithms that can execute in very short periods of time, this evaluation, classification, and Figure 1: Analytics Landscape categorization can be performed repeatedly endowing the overall work process with a measure of adaptability. How can we achieve the best As descriptive analytics reach the stage where they Stochastic Optimization outcome including the effects of Prescriptive support anticipatory action, a threshold is passed into Optimization How can we achieve the best Predictive modeling What will happen next if ? the area of predictive analytics. Predictive analysis Forecasting What if these trends applies advanced modeling techniques to examine Mission Impact Predictive Simulation What could happen…. ? scenarios and help detect hidden patterns in large Alerts What actions are needed? quantities of data to project future events. It segments Query/drill down What exactly is the and groups individuals to predict behavior and defines Ad hoc reporting How many, how often, Descriptive Standard Reporting What happened? trends. It utilizes techniques such as clustering, expert Degree of Complexity rules, decision trees and neural networks. Predictive Based on: Competing on Analytics, Davenport and Harris, 2007 analysis most commonly is used to calculate potential behavior in ways that allow you to: • Examine time series, evaluating past data and Descriptive analytics are probably the most commonly trends to predict future demands (level, trend, used and understood. These techniques deal with what seasonality) has happened in the past and categorize, characterize, • Determine ‘causality’, creating models from past and classify existing data. This includes dashboards, demand patterns while considering other relevant budget report, and various types of queries. These data, to help forecast future demands (such as techniques are most commonly applied to structured impact on demand for replacement parts at a data, though there have been numerous efforts to municipal bus maintenance facility caused by extend their reach to unstructured data, often through
  6. 6. known, predicted, or seasonal changes in consider, weigh, and trade off - scheduling or work passenger demand,) planning problems for example. Twenty, fifteen, even • Extract patterns from large data quantities via data ten years ago these problems could only be solved mining, to predict non-linear behavior not easily using computers running algorithms on a particular identifiable through other approaches. data set for hours or even days. It was not useful to In operational terms, predictive analysis may be applied embed such problem solving into a decision support as a guide to answer questions such as: “Who are my system since it could not provide timely results. Now, customers and what is the best way to target them?”; however, with improvements in the speed and memory “Which patients are most likely to respond to a given size of computers, similar computations can be treatment?” or “Is this application likely to be performed in minutes. While this kind of information fraudulent?” might have been used in the past to shape policy and offer guidance on action in a class of situations, It is at this level that the term “advanced analytics” is assessments can now be completed in real time to more aptly applied. Included are techniques for support decisions to modify actions, assign resources predictive modeling and simulation as well as and so on. forecasting. In simulation, you are creating a model of An example of these sorts of decision support tools the system and inferring from the model what the comes from the U.S. Postal Service and involves the behavior of the actual system will be. This requires assignment of mail to commercial air carriers. Years being able to build algorithms or mathematical ago, all the carriers were viewed as interchangeable. constructs that provide a sufficiently accurate However, once it was possible to scan mail and track representation of the observable behavior of a system. how the mail was delivered and which carriers were This in turn can be used to evaluate proposed changes involved, it was possible to differentiate service by to a system before they are implemented, thus carrier and to modify the assignments so they went to minimizing cost and risk. Much of business process better-performing carriers. This produced a modeling falls into this category. Forecasting can be measurable improvement in overall delivery speed applied in lots of way, not the least of which is predicting which probably resulted not only from better choices of workload, often translated into resources required, carriers, but also from the incentive created by adding including human resources. this factor into the assignment process. Once you understand the past and can begin to predict what might happen in the future, you can begin to think about what the best response or action will be. This is the area of prescriptive analytics. There are many problems that simply involve too many choices or alternatives for a human decision maker to effectively
  7. 7. Building Smarter Claims of subsequent efforts, such as process bottlenecks, Handling processing inconsistencies, and obstacles to reaching appropriate outcomes. Often the desired behavior can To this point the discussion has been about the kinds of be codified in the form of policy which can then be analytic tools that can be brought to bear on the instantiated in a set of rules to govern claims problem of improving a claims handling process. In this processing. The entire system will be linked together section we discuss what a process might look like that under the control of a workflow management system would produce a system that is shaped and guided by directed by a rules management system, even while these analytic tools. the separate underlying applications are preserved. This addresses issues both of consistency in claims Figure 2 illustrates a typical claims process, consisting handling and process transparency, and of a number of separate applications and steps of accommodates regulations for auditing and oversight. gathering and evaluating claims information and then Alternatively, the characterization of the process can be effecting the actions required to carry out any decisions used to construct a model of the claim handling system made as part of that evaluation. which can itself be the object of study as a way of identifying potential for process improvement. The process can then be further modified by applying Figure 2: Basic Benefits/Claims Management Process External Input intelligence to the information gathering stage. Informed by historical data, customer facing applications can be created that employ text analytics Data and contextual information to ensure that claims are repository Intake Eligibility Evidence Approvals Payments Verificat of decisions represented consistently and correctly within the ion handling system, contributing even more to consistency of processing outcomes. Claims handling policy may allow for expedited handling of some claims and require closer scrutiny for others. The first step in trying to improve such a process is to analyze the store of historical data produced as a Even within a more automated claims handling byproduct of the operation of the process. Here, all of process, a requirement for human evaluation and the data analysis and data mining tools can be brought assessment will not disappear. Still not all humans are into play to build a detailed picture of the process, equal in their experience and areas of competency. including benchmarks on performance and identification Ensuring that tasks are assigned to appropriate staff of parameters and correlations between parameters that members for handling is a classic workforce affect the eventual dispensation of any claim. This may management and scheduling problem, and hence reveal any number of features that might be the targets
  8. 8. susceptible to mathematical optimization techniques. statistical analysis on past claim data to regularly This area in particular has shown significant progress balance their desire for speedy processing with their with the increase in available computing power and appetite for risk and their overall financial exposure. In advances in the necessary algorithms. other words, these companies use past data to Continued collection of outcome data, particularly in estimate the risk associated with each claim or cases of fraud and abuse allows the cycle to be application and then use statistical models to determine completed and sets the stage for continual improvement how to handle each claim or application. In some in the claims handling process. The ease with which cases, they may automatically approve a claim and in rule driven systems can be updated enhances the ability others, the will delay for extensive verification. of the system to react to changes in behavior of those who use the system and to respond when external A smarter benefits processing system is illustrated in circumstances produce a surge in the number or a shift Figure 3. in the type of claims that are being received. In many cases, the key to better leveraging advanced Figure 3. Smarter Benefits/Claims Processing System analytics for claims processing is to change the overall philosophy from one that is based on a manual review of each claim independently from others to one that uses rigorous statistical analysis of past claims and decisions to help guide the decision process on future claims and begin accelerated payments for selected (“low risk”) types of claims. Such a change in philosophy should start with rigorous analysis of the claim and decision data from previous years. The purpose of this analysis would be to understand and identify the most common decision or reimbursement percentage associated with each claim type. Inherent in the value of analyzing past data on claims and payment decisions is the assumption that the organization would be able to use such statistical analysis to justify automated pre-payments for certain categories of claims. This is not different in principle than what most insurance companies and financial institutions do with their “scoring models” for claims or credit card/loan applications today. These companies conduct rigorous
  9. 9. Conclusion Benefits processing always involves a tension between the desire to provide prompt relief from a claimant’s loss or expenses and the need to manage financial resources responsibly and to avoid paying fraudulent claims. When the balance moves too far in the latter direction, claims processing times can become unconscionably long. In this paper we have discussed how the application of advanced analytics techniques can be used to transform a benefits processing system so that it exploits historical information to help develop claims handling policy which can then be embodied in a rules-driven workflow. When this is combined with tools that improve the quality of claims information gathered from customers, visualization capabilities to provide transparency into the operation of the system, and optimization techniques for fine-tuning the assignment of work to human evaluators, the result is smarter claims handling. Technology is an enabler, but in the end the impact of any of these changes to the claims handling process is limited by the ability of organizations to modify the overall business process, the way people work and the way they think about the work they are doing. In the benefits and claims handling area, the effects of the process on the end-customers, i.e., the citizens in a benefits processing system should never be ignored.
  10. 10. Appendix Case Study: Predictive Modeling for Disability Claims Client: Social Security Administration (SSA) Industry: Government, Social Services Challenge: A recent challenge faced by SSA focused on the both the time required for reviewing and approving disability benefits for disabled citizens and the consistency of these determinations across the country. The backlog generated had come to attention of Federal oversight organizations such as the General Accountability Office, which only added to the urgency on the part of the SSA. Solution: Extending prior work at SSA with structured data mining tools such as logistic regression, IBM expanded its suite of predictive models to include advanced text analytics to infer meaning from unstructured data in disability applications so that SSA could automatically score applications to identify potential quick decisions. Benefits: The predictive model for reviewing new disability applications has reduced the average cycle time for approving an application from 90 days to 20 days for selected cases and continues to drive the agency toward higher levels of consistency across the system. The Quick Disability Determination (QDD) process has been featured in Congressional testimony and numerous press releases from the SSA as one of its most successful programs.
  11. 11. © Copyright IBM Corporation 2010 IBM Corporation Route 100 Somers, NY 10589 U.S.A. Produced in the United States of America 01-10 All Rights Reserved IBM, the IBM logo, and are trademarks or registered trademarks of International Business Machines Corporation in the United States, other countries, or both. If these and other IBM trademarked terms are marked on their first occurrence in this information with a trademark symbol (® or ™), these symbols indicate U.S. registered or common law trademarks owned by IBM at the time this information was published. Such trademarks may also be registered or common law trademarks in other countries. A current list of IBM trademarks is available on the Web at “Copyright and trademark information” at Other company, product, or service names may be trademarks or service marks of others. References in this publication to IBM products or services do not imply that IBM intends to make them available in all countries in which IBM operates. GQW03001-USEN-00