Analytics Solutions

Advanced Analytics for Smarter Benefits, Claims,

and Entitlement Management

January 2010

Toward a Smarter Government

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 www.ibm.com/ascdc (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 ASCdc@us.ibm.com to get more information or to ask us a

question via email.

Introduction

Claims processing is fundamentally a customer service

business, evaluated in terms of effectiveness in

delivering benefits to claimants. Particularly in the

government domain there is a tension between the view

of benefit programs as entitlements and the demand of

the electorate that public resources not be wasted. In

times of stress, such as natural or economic disasters,

both the number of claims to process, and the urgency

of those claims in terms of human need and suffering

are elevated.

The extreme cases of problems in claims handling are

of course the ones that rise to the level of news

coverage. Still when some figures come to light, such

as the average of 196 days required from the time a

claim for veterans benefits is submitted to the Veterans

Administration until the final disposition of that claim, or

the average of 145 days required by the US Immigration

and Naturalization Service to process an application for

citizenship, it is hard to argue that improvements are not

warranted. At the other end of the spectrum, when the

Center for Medicare and Medicaid Services determines

how to satisfy a claim within an average of 5 days, some

could wonder whether unjustified claims are being paid

without being subjected to proper scrutiny. At either

end of the scale, applying more intelligence to the

processing of claims or requests can ensure timely

handling without compromising on verification of the

validity of those claims. The Appendix to this paper

outlines a solution developed by IBM for the U.S. Social

Security Administration, using the principles described

in this paper which allowed the processing of certain

classes of disability benefits to be cut from an average

of 97 days back to only 20 days.

While processing speed and throughput are important

measures of the effectiveness of a claims handling

system, consistency in the way claims are handled and

avoidance of fraud and abuse are essential as well.

Confidence in the fairness of a government program is

undermined when similar claims may be honored in

one geographic region and denied or only partially

granted in others. This kind of inconsistency can result

when human beings are forced to interpret complex

guidelines and can not easily reference previous

experience or evolving trends.

Sources as diverse as USA Today and the US Senate

Panels on Healthcare report that as much as 10% of

the more than a trillion dollars spent each year on

healthcare in the United States may be wasted on

fraud and abuse. Clearly the problem is a significant

one. While organizations may devote a lot of effort to

chasing down and recovering payments in the case of

fraudulent or flawed claims, better still would be to

identify the questionable claims before payment is

made, as part of the claims processing workflow.

The solution to smarter claims handling derives from a

combination of improved process design, application of

information technology, and accommodation of the

people who will use the technology and conduct the

process. Simplifying and facilitating data entry and use

of content management systems make unstructured

data more accessible and usable. Introducing rule-

based management of the workflow associated with

claims processing ensures greater consistency and

transparency in the handling of claims, as well as

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.

Analytics

The term “analytics” appeared a couple of times in the

introduction, but what does this term refer to? In fact,

what we call analytics today is really an extension and

aggregation of efforts that have been pursued for a

number of years, as people have tried to apply

computers to help improve the quality of decisions being

made by human beings.

We further subdivide analytics into three categories of

increasing complexity and impact: descriptive,

predictive, and prescriptive.

Figure 1: Analytics Landscape

Descriptive analytics are probably the most commonly

used and understood. These techniques deal with what

has happened in the past and categorize, characterize,

and classify existing data. This includes dashboards,

budget report, and various types of queries. These

techniques are most commonly applied to structured

data, though there have been numerous efforts to

extend their reach to unstructured data, often through

the creation of structured metadata and indices.

Descriptive analytics help provide an understanding of

the past. Predictive analytics use the understanding of

the past to make “predictions” about the future. For

example, a particular type of claim that falls into a

category that has proven troublesome in the past might

be flagged for closer investigation. Descriptive

analytics may begin by providing a very static view of

the past, but as more and more instances are

accumulated in the experience base of the system, and

with algorithms that can execute in very short periods

of time, this evaluation, classification, and

categorization can be performed repeatedly endowing

the overall work process with a measure of adaptability.

As descriptive analytics reach the stage where they

support anticipatory action, a threshold is passed into

the area of predictive analytics. Predictive analysis

applies advanced modeling techniques to examine

scenarios and help detect hidden patterns in large

quantities of data to project future events. It segments

and groups individuals to predict behavior and defines

trends. It utilizes techniques such as clustering, expert

rules, decision trees and neural networks. Predictive

analysis most commonly is used to calculate potential

behavior in ways that allow you to:

• Examine time series, evaluating past data and

trends to predict future demands (level, trend,

seasonality)

• Determine ‘causality’, creating models from past

demand patterns while considering other relevant

data, to help forecast future demands (such as

impact on demand for replacement parts at a

municipal bus maintenance facility caused by

Degree of Complexity

Standard Reporting

Ad hoc reporting

Query/drill down

Alerts

Simulation

Forecasting

Predictive modeling

Optimization

What exactly is the

What will happen next if ?

What if these trends

What could happen…. ?

What actions are needed?

How many, how often,

What happened?

Stochastic Optimization

Based on: Competing on Analytics, Davenport and Harris, 2007

Descriptive

Prescriptive

Predictive

How can we achieve the best

How can we achieve the best outcome including the effects of

Mis

sio

n I

mp

act

known, predicted, or seasonal changes in

passenger demand,)

• Extract patterns from large data quantities via data

mining, to predict non-linear behavior not easily

identifiable through other approaches.

In operational terms, predictive analysis may be applied

as a guide to answer questions such as: “Who are my

customers and what is the best way to target them?”;

“Which patients are most likely to respond to a given

treatment?” or “Is this application likely to be

fraudulent?”

It is at this level that the term “advanced analytics” is

more aptly applied. Included are techniques for

predictive modeling and simulation as well as

forecasting. In simulation, you are creating a model of

the system and inferring from the model what the

behavior of the actual system will be. This requires

being able to build algorithms or mathematical

constructs that provide a sufficiently accurate

representation of the observable behavior of a system.

This in turn can be used to evaluate proposed changes

to a system before they are implemented, thus

minimizing cost and risk. Much of business process

modeling falls into this category. Forecasting can be

applied in lots of way, not the least of which is predicting

workload, often translated into resources required,

including human resources.

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

consider, weigh, and trade off - scheduling or work

planning problems for example. Twenty, fifteen, even

ten years ago these problems could only be solved

using computers running algorithms on a particular

data set for hours or even days. It was not useful to

embed such problem solving into a decision support

system since it could not provide timely results. Now,

however, with improvements in the speed and memory

size of computers, similar computations can be

performed in minutes. While this kind of information

might have been used in the past to shape policy and

offer guidance on action in a class of situations,

assessments can now be completed in real time to

support decisions to modify actions, assign resources

and so on.

An example of these sorts of decision support tools

comes from the U.S. Postal Service and involves the

assignment of mail to commercial air carriers. Years

ago, all the carriers were viewed as interchangeable.

However, once it was possible to scan mail and track

how the mail was delivered and which carriers were

involved, it was possible to differentiate service by

carrier and to modify the assignments so they went to

better-performing carriers. This produced a

measurable improvement in overall delivery speed

which probably resulted not only from better choices of

carriers, but also from the incentive created by adding

this factor into the assignment process.

Building Smarter Claims Handling

To this point the discussion has been about the kinds of

analytic tools that can be brought to bear on the

problem of improving a claims handling process. In this

section we discuss what a process might look like that

would produce a system that is shaped and guided by

these analytic tools.

Figure 2 illustrates a typical claims process, consisting

of a number of separate applications and steps of

gathering and evaluating claims information and then

effecting the actions required to carry out any decisions

made as part of that evaluation.

Figure 2: Basic Benefits/Claims Management Process

The first step in trying to improve such a process is to

analyze the store of historical data produced as a

byproduct of the operation of the process. Here, all of

the data analysis and data mining tools can be brought

into play to build a detailed picture of the process,

including benchmarks on performance and identification

of parameters and correlations between parameters that

affect the eventual dispensation of any claim. This may

reveal any number of features that might be the targets

of subsequent efforts, such as process bottlenecks,

processing inconsistencies, and obstacles to reaching

appropriate outcomes. Often the desired behavior can

be codified in the form of policy which can then be

instantiated in a set of rules to govern claims

processing. The entire system will be linked together

under the control of a workflow management system

directed by a rules management system, even while

the separate underlying applications are preserved.

This addresses issues both of consistency in claims

handling and process transparency, and

accommodates regulations for auditing and oversight.

Alternatively, the characterization of the process can be

used to construct a model of the claim handling system

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

intelligence to the information gathering stage.

Informed by historical data, customer facing

applications can be created that employ text analytics

and contextual information to ensure that claims are

represented consistently and correctly within the

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.

Even within a more automated claims handling

process, a requirement for human evaluation and

assessment will not disappear. Still not all humans are

equal in their experience and areas of competency.

Ensuring that tasks are assigned to appropriate staff

members for handling is a classic workforce

management and scheduling problem, and hence

Intake Eligibility Evidence Approvals Payments Verificat

ion

Data repository

of decisions

External

Input

susceptible to mathematical optimization techniques.

This area in particular has shown significant progress

with the increase in available computing power and

advances in the necessary algorithms.

Continued collection of outcome data, particularly in

cases of fraud and abuse allows the cycle to be

completed and sets the stage for continual improvement

in the claims handling process. The ease with which

rule driven systems can be updated enhances the ability

of the system to react to changes in behavior of those

who use the system and to respond when external

circumstances produce a surge in the number or a shift

in the type of claims that are being received.

In many cases, the key to better leveraging advanced

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

statistical analysis on past claim data to regularly

balance their desire for speedy processing with their

appetite for risk and their overall financial exposure. In

other words, these companies use past data to

estimate the risk associated with each claim or

application and then use statistical models to determine

how to handle each claim or application. In some

cases, they may automatically approve a claim and in

others, the will delay for extensive verification.

A smarter benefits processing system is illustrated in

Figure 3.

Figure 3. Smarter Benefits/Claims Processing System

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.

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.

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