The volume of data structured and unstructured, internal and external coursing its way into every healthcare organization is voluminous and increasing exponentially. For health insurance companies (payers), data today comes from disparate sources that include patient-ecosystem interactions across channels such as call centers, healthcare and medical devices, social media, patient-provider conversations, smartphones, emails, faxes, medical reports, day-to-day business activities, and others.

Gartners 2011 Top 10 list of IT Infrastructure and Operations Trends predicts an 800% growth in data over the next five years. However, organizations actually process only about 1015% of the available data, most of which is structured. While managing this overwhelming data flow can be challenging, payers can reap real benefits such as increased productivity and an enhanced patient-ecosystem experience by capturing, storing, aggregating, and eventually analyzing data. Payers can glean objective-driven business value by going beyond simply managing Big Data to harnessing the actionable insights from it. This will help them sustain competitive advantage and stay ahead of the curve in this information age.

12 Ways Health Insurance Companies Can Leverage Big Data for Business Value

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About the Author Ajay Bhargava Ajay Bhargava is the global Practice Director of Tata Consultancy Services (TCS) Analytics and Big Data Practice for insurance and healthcare customers. He has more than 24 years of industry, research, and teaching experience in areas relating to databases, enterprise data management, data warehousing, business intelligence, Big Data, and advanced analytics. Over the years, Ajay has provided business and technology-oriented strategic, mentoring, and customer-centric solutions to clients. He publishes articles, and is a speaker and panelist at various conferences on enterprise data management, business intelligence, and advanced analytics. He has taught at The University of Texas, Austin, and College of Engineering, Pune, India, as adjunct faculty. Ajay holds an M.S. in Computer Science and an M.S. in Aerospace Engineering from The University of Texas at Arlington. He obtained his B.Tech. in Aeronautical Engineering from Indian Institute of Technology (IIT), Mumbai.

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Table of Contents

Big Data Defined

Business Value

Harnessing and Harvesting Big Data

Twelve Ways to Create Value from Big Data

The Opportunity

Getting Started

Conclusion

Introduction

Since the advent of the Internet, data generated by both humans and devices has witnessed an exponential increase, presenting a challenge that is difficult for organizations and existing technologies to cope with. Not only the size, but also the rate at which data is being generated, and the growing variety of data to be assimilated, prompted the evolution of Big Data and its associated technologies and algorithms. Globally, enterprises, payers included, are increasingly finding themselves in an environment where they are data rich, but information poor. The regulatory environment, mergers and acquisitions, and the need to provide better patient care, faster and more economically, is driving payers to adopt Big Data to stay ahead in a very competitive landscape. This paper establishes an understanding of what constitutes Big Data, defines what it takes to harness and harvest it, and suggests 12 different ways in which payers can derive actionable business insights. It then goes on to show how one can get started in a small, cost-effective way, to harness tangible business value using Big Data.

Big Data Defined

Big Data opens up new opportunities that were not achievable by analyzing structured content in traditional ways. Although there is plenty of hype in the industry surrounding Big Data, it can be described by the following five characteristics:

Volume: Big Data refers to the enormous and exponentially growing amount of data flooding every enterprise. Big Data relevant to healthcare insurance companies can come from a variety of sources including:

o Structured, granular Call Detail Records (CDRs) in call centers o Detailed sensor data from medical devices o External data regarding the weather, geography, clinical, pharmaceutical, demographic and

psychographic behavior o Unstructured data from sources that include social media, medical reports, and Electronic Health

Records (EHR), in addition to genomic data

Data providers such as HealthData.gov supply data feeds to healthcare providers, Medicare, Medicaid, childrens health programs, treatments, population statistics, and so on.

Variety: Proliferating channels have led to burgeoning data types. This presents payers with the challenge of extracting insights from varied types of data. This data goes beyond the usual structured environment of data warehouses and comes from disparate source systems such as mobile and online data, patient-generated data, medical device scans (X-rays, Magnetic Resonance Imaging (MRI), and so on), social media, text (Medicare claims), audio, video, log files, and more.

Velocity: Payers must be able to access, process, and analyze huge volumes of information as quickly as possible in order to make timely decisions. They specifically need to reduce latency to:

o Optimize cross-selling and up-selling services in a call center environment o Provide quick turnaround on enterprise intranet documents search to study the impact of different

events o Reduce delivery time for business reports in a data warehousing environment

Veracity: The reliability and consistency of data its dependability and quality is a critical issue for payers looking to derive meaningful insights. This holds true for both Big Data and small data. In some cases, such as voice-to-text conversions, it is possible to gain meaningful insights even from not-so-perfect data in

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terms of data quality especially if payers are trying to analyze macro-level phenomena such as performing sentiment analyses.

Value: Payers that harness business objective-driven value insights will surge ahead of the pack in this challenging environment. Today, Big Data analytics helps caregivers (including healthcare providers and clinicians), payers, and regulators in monitoring performance and deriving efficiencies and savings while controlling costs. Value, therefore, is the most important of the five Big Data characteristics.

Business Value

According to McKinsey & Co., 1 Big Data creates value in five different ways:

1. Managing Big Data increases transparency, making data more easily accessible to relevant stakeholders

2. As they create and store more transactional data in digital form, organizations are able to collect accurate, detailed performance data in real-time or near real-time, enabling experimentation to identify needs and improve performance

3. Big Data gives organizations the means to improve patient-ecosystem segmentation and in turn, better

develop and tailor products, services, and promotions to target each specific segment

4. Big Data strategy includes sophisticated analytics to provide actionable insights that minimize risks and improve decision-making

5. Big Data is indispensable for organizations looking to create new business models and improve products and

services

Harnessing and Harvesting Big Data

There are two fundamental aspects to Big Data. The first is harnessing, which involves collecting, administrating, and managing Big Data. The second is harvesting the skills and techniques required to apply science to data in order to derive actionable and meaningful insights.

Harnessing Big Data

At the most basic level, harnessing is the amassing of Big Data; it relates to how payers manage Big Data and how they develop an ecosystem that can not only create but also sustain Big Data. Previously, with far lower data volumes and complexity, it was easier to harness data than it is today; however, the benefits of using this data were limited as well. Gartner estimates that between 8090% of all data produced is unstructured. Payers can now tap into a treasure trove of unstructured data of all varieties: text, audio, video, medical records, click streams, claims, and log files. They can also combine this unstructured data with structured data from sources such as disease or geographic information, as was done to predict the flu outbreak in the US by looking at Twitter responses. 1 McKinsey Global Institute Report, Big Data: the Next Frontier for Innovation, Competition, and Productivity, May 2011. http://www.mckinsey.com/insights/business_technology/big_data_the_next_frontier_for_innovation

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Today, harnessing Big Data involves accessing a combination of data from sources such as social media as well as from newer technology areas such as genomics offering payers access to data and the ability to analyze it. However, Big Data can no longer be managed with traditional technologies. Instead, organizations must leverage a whole new class of platforms, such as the open source Hadoop ecosystem and its commercial variants. Hadoop is a distributed file system with the MapReduce paradigm, a framework for processing problems in parallel, across huge datasets using a large number of computers. The MapReduce program includes two functions:

Map: This step essentially filters and sorts the data. The master mode entails the breaking and allocating of processing, which is then carried out by individual nodes. The individual nodes process the task and return it to the master

Reduce: In this step, the master node collects the answers to all the sub-problems and combines them to form the output

Each step can be carried out in parallel and technologies can be built on top of them. The Hadoop-based frameworks represent a paradigm shift not only because they are able to handle different kinds of data, but also because they have the ability to provide speedy processing capabilities for huge volumes of data on commodity hardware. Additionally, IT departments that are typically responsible for administering and managing the Big Data environment are adopting programming languages such as R and Python. While these languages originated in the context of handling Big Data at Yahoo and Google, payers can use them to handle many traditional data processing tasks as well. One example of the use of the Hadoop platform is an Extract, Transform, Load (ETL) enabler for business agility. Today, payers are adopting this use case, often as their first experience in leveraging Big Data to meet business Service Level Agreements (SLAs). Even though the traditional data warehousing environment still involves structured data, payers who invest in this platform know they will be able to support unstructured data as well, now and in the future.

Table 1: Understanding Big Data harnessing and harvesting

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Harvesting Big Data

Unstructured data cannot be consumed in its raw form. It must be processed into a consumable form before it can be interpreted or acted upon. Harvesting utilizes technology and algorithms that enable payers to analyze, deliver actionable insights, and derive real value from Big Data. Skills such as statistical analysis, data mining, econometrics, business analytics, bioinformatics, and visualization techniques are in high demand as they provide a solid foundation for deriving useful insights from the data. Universities are trying to fill the supply-demand gap by offering various graduate programs in business analytics to provide for the next generational skills needed to mine actionable insights. Various startup companies are providing analytical models that enable organizations to analyze large amounts of healthcare data.

Figure 1: Top-down approach to create actionable insights from Big Data

While the ability to successfully harness and harvest data is critical to a Big Data strategy, payers can derive true value from their data with the help of analytics. Defining use cases and hypotheses for Big Data harvesting becomes crucial when following a focused top-down approach to creating actionable insights (see Figure 1). Although this is a focused approach, payers often need to perform exploratory data analysis to first identify use cases utilizing Big Data. This initial bottom-up approach becomes a prerequisite for determining and prioritizing use cases for which Big Data Proof of Concepts (PoCs) must be pursued. Real value is derived when actionable insights can make a positive difference in achieving the organizations strategic objectives. Some of the other comparisons between harnessing and harvesting are shown in Table 1.

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Harvesting and harnessing activities are complementary to one another two sides of the Big Data coin. Big Data platforms do not replace existing traditional data management and analytics platforms. Instead, they merely complement, add to, and improve upon the existing investments for better outcomes.

Twelve Ways to Create Value from Big Data

Leading-edge payers have either started or are planning to start exploiting Big Data in at least a dozen ways, each of which adds value to their organization in one or more of the five ways described earlier. These use cases include: 1. Improving performance in existing data warehouse environments: IT has begun to adopt Hadoop-based architecture to speed up ETL in a data warehouse environment to meet reporting business SLAs. Typically, companies explore this use case to experiment with a Big Data platform such as Hadoop, to derive high business value with minimal investments. This enables them to get funding for the next set of use cases for incremental business value creation. 2. Detecting fraud: According to a recent Government Accountability Office estimate, one of every seven dollars spent on Medicare is lost to fraud and abuse. Most of the healthcare (Medicare/Medicaid) fraud is perpetrated by just a few providers intent on abusing the system, costing taxpayers billions of dollars and putting beneficiaries health and welfare at risk. Typical types of fraud include incorrect medical coding or pre-existing conditions, same address used for hundreds of patients, charge posting, and fictitious claims for services not rendered. Payers who have succeeded at fraud prevention are those who adopted a multi-channel approach to fraud detection by looking at structured data in their claims and patient/provider data warehouses, and combining it with textual data in claims notes, police reports, and information from social media. They are interested in identifying suspicious claims using text analytics and Natural Language Processing (NLP) capabilities, in addition to automated business rules, predictive analytics, social media analytics, association rule mining, and link/network analysis (for example, doctor-chiropractor collusion) techniques. With the advent of health insurance exchanges, payers will be in a position to proactively identify fraud before claims payments are made. 3. Unifying communication by combining patient ecosystem channels. Combining direct patient ecosystem connections that include email, call center, doctor, portal, faxes, medical reports, and more with indirect patient touch points such as social media, blogs, log files and so on, provides a more holistic, 360-degree view of each patients ecosystem. This helps create a personalized, unified communication response, enabling Chief Marketing Officers (CMOs) to achieve better brand value and gain competitive advantage while directly improving the bottom line by reducing communication waste. Also, combining patient ecosystem channels minimizes capital expenditure by using cloud technology along with mobile visualization techniques that help executives in accessing holistic patient ecosystem information anywhere, enabling quick and cost-effective decision-making. 4. Optimizing call center workload. Analyzing telecom data from the call detail records (CDRs) and combining it with claims data helps in understanding what activity was performed by whom and how efficiently. This insight can be used to provide training guidelines for employees. Temporal call pattern analysis on voluminous raw telecom data can assist organizations in optimizing staffing levels as well. By analyzing text and speech in a near real-time environment, organizations are presented with new opportunities to convert the call center from a cost center to an investment center, to leverage cross-sell and up-sell product and service capabilities. 5. Improving clinical outcomes by using medical devices and bioinformatics data. CIOs are currently investigating how analytics can help improve the frequency, regularity, accuracy, and timeliness of patient data collection by sensing device data and responding in near-real time. In addition, devices that alert patients to take medicines,

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request refills, or schedule a checkup, can also be used to predict risks based on medical history, medicine side effects, usage, and so on. Big Data harnessing and harvesting can be used to analyze typical care patterns, workflows, and events, and (proactively) provide actionable insights to payers for reducing costs, and providing better patient care. 6. Minimizing risk and speeding recovery from business disrupting disastrous events. CIOs are facing mounting pressures to define plans to address disaster events that may disrupt business. In the wake of disastrous events, payers struggle with extended power outages, communication failures, loss of IT infrastructure, and transportation disruptions as was seen in the case of Hurricane Sandy affecting the US East Coast.2 Payers are simulating these events, to better evaluate recovery strategies and minimize risk. 7. Enhancing collaboration with pharmaceutical companies. Payers can collaborate with pharmaceutical companies by providing patient insights on product buying behavior, segmentation, and drug usage that might help reduce the cost and improve the efficiency of clinical trials and drug research. This feedback loop is an important tool to bring down the overall cost of healthcare. 8. Utilizing social media to introduce new products and services, as well as for sentiment analysis. CMOs are moving away from capital-intensive television and Internet promotions to introduce new products and services, and target patient ecosystems in specific regions. They are utilizing social media as a cost effective alternative, innovatively changing the business model. Payers can experiment with this approach for regional patient ecosystem segments and then upgrade their strategies to a national level. Certain population segments (for instance, younger demographics) have shown interest in reviewing sentiments about products and services before making buying decisions. Payers can analyze and listen to these blogs, tweets, likes, and so on to improve the quality of service, customer experience, and engagement. Such one-on-one reach out to consumers goes a long way in brand differentiation, retention, and loyalty. 9. Learning from the retail model. With the onset of the Affordable Care Act (ACA) and the creation of Healthcare Exchanges in the US, payers are increasingly adopting the retail model, treating their customers as a Segment of One. This new model is causing a significant strategic shift in providing personalized dissemination of information based on the time, place, and device dictated by the insured. Payers that adapt their products and services quickly to the retail model stand to gain a competitive edge. Analytics behind customer acquisition, churn, cross-channel promotions, increased stickiness and engagement, tailoring the right-product mix to the right customer at the right price, and a lifecycle approach will increasingly become relevant in the healthcare payers space. 10. Leveraging external data for more accurate pricing. Using real-time diagnostics information with external clinical location, Medicare/Medicaid, treatment and childrens health data can lead to more appropriate pricing of medicine, care, and services based on patient, provider, and payer ecosystems. The industry can move towards improved patient-centric, personalized pricing by collecting data on usage, and availing easy price comparison facilities provided by exchanges and concierge services offerings. Insights from supplemental products such as dental, vision, and pet care will go a long way in helping payers understand customer value, market segments, shopping behavior, and ultimately help them better price products and services. 11. Enhancing intranet search capabilities. Many payers, providers, pharmaceutical companies, and patients use Big Data to improve speed and search capabilities on their intranet, especially from unstructured PDFs and MS Word documents, which was once impossible. These search capabilities are currently used by niche concierge services care providers, as well as in call center scenarios to provide real-time recommendations. 2 Gartner, Best Practices for Healthcare Payer CIOs Facing Business-Disrupting Disasters, December 2012.

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12. Employing gaming-type incentives for keeping fit and healthy. In keeping with an ounce of prevention is better than a pound of cure, and with an eye towards reducing claims cost, payers are combining instrumented data sensing with customer incentives for those who are less of a claims risk. These discounts include gym memberships, discounts on certain types of drugs, preferred treatment options and pricing, and discounts on equipment purchase and usage monitoring. This means of patient data collection helps improve provider relationships as well.

Deriving value from use cases

Table 2: The Big Data use cases - VALUE creation grid

Table 2 shows how each of the dozen use cases can create value from Big Data.

The Opportunity

Generally speaking, Big Data is not yet a big priority in many payer organizations. They are still contending with severely fragmented data environments and information silos, as well as insufficient investment in tools and technologies, to be able to support a Big Data strategy. Most carriers are still maturing and expanding their use of traditional data analytics and predictive models to optimize processes, reduce fraud, and generally improve their bottom line Payers that arent exploring and embracing Big Data and developing a Big Data strategy will be constrained in gleaning actionable insights from the copious amounts of data flooding their organizations. With respect to Big Data adoption and opportunity, we have observed the following:

Very few payers are using analytics to improve operational areas such as sales, marketing, or clinical outcomes

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Relatively few payers are fully immersed in a comprehensive Big Data strategy and reaping its benefits. However, most payers are currently educating themselves and planning their Big Data approach

Even fewer payers capture, persist, and analyze Big Data within their computing environment today, but those that do, typically leverage traditional computing, storage, database, and analytics in addition to newer platforms such as the Hadoop ecosystem

Larger payers universally plan to embrace Big Data and analytics across all financial and risk management areas as well as most operational areas

Big Data harnessing and harvesting strategies can be used to proactively detect fraud and abuse, and bring down the cost of healthcare, making it a win-win situation for payers and patients

Getting Started

Big Data solutions encompass a new generation of software and architectures designed to economically extract value from enormous volumes and variety of structured and unstructured data. This is done by enabling rapid data capture, discovery, and/or analysis. Payers that have created a culture where business leaders trust and embrace analytics, and act on the insights provided are in the best position to profit from the potential value of Big Data. Payers should take steps to create that culture today if it doesnt already exist in their companies.

The key is to start small with a proof of concept (PoC). Following is an example of how payers can leverage a Big Data platform as well as some key considerations to keep in mind. In this example, IT is interested in using a Big Data environment to speed up long-running ETL processes in a traditional data warehouse environment (use case 1), because traditional processing is the cause for missing reporting SLAs for business. In this example, a relative small number of data sources with large volumes were used to prove the viability of the Big Data platform.

Table 3: Sample Proof of Concept for Big Data adoption

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It is important for payers to develop a good business use case to meet the strategic objectives of that line of business. In addition, solid backing from a CxO/VP level executive is extremely important not only for funding, but also to evangelize and communicate the objectives and need for Big Data adoption to the larger organization, including partners and vendors. Although the scope and investment in terms of people (approximately five full-time employees), tools (for example, the open source Hadoop ecosystem), technologies and infrastructure (such as commodity hardware or cloud) might be small, the architecture should be appropriate for the long haul. To effectively harness and harvest Big Data, organizations must foster close collaboration between IT and business to iteratively experiment and drive actionable insights. Payers can then use this incremental success to get increased funding for next phases and/or use cases.

Conclusion

As payers identify the scenarios for applying Big Data within their businesses, they will need to tweak existing processes to be able to ingest the data variety, ensure data veracity, and manage data volume and real-time data velocity, to derive objective-driven actionable value. A holistic view of payers, providers, pharmaceutical companies, and patients will enable significant cost reductions, increased operational efficiency, and improved patient care. Big Data and analytics can help improve clinical efficiency, quality, and outcomes; identify patient profiles for preventative care segmentation; influence provider behavior; and help minimize fraud and abuse, significantly reducing the insurance risk for payers. Organizations that develop an analytics-driven culture, learn how to harness the power of Big Data, harvest the valuable information and insights it can provide, will be able to create competitive advantage and positively impact their brand and their top and bottom line.

Big Data Challenges: At-A-Glance Seven factors that combine to make Big Data a daunting challenge for payers:

1. An increasingly competitive landscape,

with mergers and acquisitions as well as newly formed health exchanges at federal and state levels

2. The financial tsunami of the past several years, as well as the resulting increasingly demanding regulatory requirements (Health Insurance Portability and Accountability Act (HIPAA) and Affordable Care Act (ACA)) in North America

3. The explosion of data such as genomics and data from medical devices, and channel proliferation

4. The recent opening of electronic health records

5. Siloed data environments at payer, provider, and pharmaceutical companies, and duplication of patient information

6. Global shortage of expertise in analytics and Big Data

7. The constant stream of innovations that have made it essential for payers to simultaneously deal with Big Data while managing the challenges of their existing small data environments

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