Tackling US Health Plan Challenges with Advanced Analytics

WHITE PAPER Tackling US Health Plan Challenges with Advanced Analytics Transforming the way health plans do business

Table of Contents Executive Summary...1 Managing the Challenges of Health Care Reform...2 Case Study Identifying and Managing Risk Before It Affects Business Results...3 Transitioning from Wholesale to Retail...4 Case Study Optimizing Acquisition and Retention...5 Bending the Medical Expense Cost Curve...6 Improving Quality of Care...6 Alternative Care Delivery Models...6 Case Study Gaining Better Insight into Cost Drivers and Price Change Implications...7 Conclusion...8 i

Executive Summary The US health care landscape is transforming rapidly, fueled by continued rising medical costs, economic instability and the pressures of the health care reform law. In the midst of this explosive change, health insurance plans face tremendous challenges and opportunities, many of which may require them to transform the way they do business. Ultimately, the future of individual health plans and potentially the US health insurance industry as a whole will be determined by how nimbly and effectively they are able to address these challenges. Thoroughly understanding emerging market dynamics and developing effective strategies to address them will undoubtedly require more sophisticated levels of information gathering, analysis and decision making than ever before. This paper summarizes the results of research conducted by Stonegate Advisors LLC and sponsored by SAS. The research which was based on interviews with 40 key executives across 15 national and regional health plans explored how advanced analytics can help health plans address emerging business needs. The interviews revealed three critical focus areas for health plans: Managing the business challenges of health care reform. Shifting from a wholesale to a retail business model. Bending the medical expense cost curve. For each of these three areas, this paper provides real-life examples that show how leading companies in health care and other industries have successfully applied advanced analytics to solve similar business problems. 1

Managing the Challenges of Health Care Reform Health plans anticipate that reform and the subsequent creation of federal subsidies and health insurance exchanges (HIEs) will result in the restructuring of the US health insurance market, creating a huge influx of new individual buyers as many as 30 million nationally who were either previously uninsured or were insured through an employer. This dramatic market shift, the disintermediation of traditional broker and employer channels, and the inability to medically underwrite business beginning in 2014 will introduce an unprecedented level of change and consequently risk to plans traditional individual and small group business models. As a result, there is a growing sense of urgency among health plans to evaluate potential market shift scenarios and understand the resulting impact these shifts will have on revenue, membership and earnings. Health plan leaders report that they are under mounting pressure to determine if and how they will play in the subsidized HIE markets, what types of products and price points should be offered, and how they will simultaneously sell through their direct or agent-assisted channels. While there is still significant uncertainty about specifically how each individual state s HIE will operate, the typical product development cycle within health plans necessitates much earlier decisions about product, service and distribution designs. To enable go-to-market development, plans have recognized an urgent need to gain a better handle on member preferences and price sensitivity in order to deploy innovative products and services that will both appeal to their desired markets and be ready to deploy in time to meet regulatory requirements. Health plans recognize the level of complexity they are facing due to the transformational nature of health care reform. They believe that their traditional decision-making approach may become obsolete in the new environment, given the introduction of so many new and unpredictable factors. Other industries have very successfully used advanced analytics to help dynamically forecast risk and predict customer behavior. Health plans have the opportunity to apply these same lessons to their own businesses as they prepare for reform. 2

Case Study Identifying and Managing Risk Before It Affects Business Results A leading international financial institution provides the perfect example of how advanced analytics can help mitigate risk in evolving markets. The company needed to transition its risk management monitoring function from being reactive to being proactive, so it deployed an advanced analytics solution that enabled it to conduct detailed segmentation analysis across its portfolio of customers to discover potential problem areas before they turned into actual losses. According to the company s chief risk officer (CRO), We needed to be able to discover stresses in the portfolio before they became significant problem areas. We needed a dynamic system that is updated regularly so we could do proper analytics on our portfolio and discover red flags. If we hadn t addressed this issue, then we would be driving more or less blind and would be firefighting, as we had no way to address potential problem areas proactively. He went on to say, We are now able to see that a person in a certain age bracket from a certain state, earning between x and y, renting his property and having high credit-card usage and a deteriorating CCRIS (Central Credit Reference Information System) record, is contributing the most to delinquency levels, and as such, we stop financing this client profile. Moreover, we can put extra attention to this group of customers to prevent delinquency levels from deteriorating further. This is very powerful. Effective risk management also allows the bank to be more competitive when pricing its products and services, and it improves the assets base of the bank. The CRO says, The challenge is to ensure that you know where your risks are in the portfolio so that you can manage them proactively. It is also important that you dynamically adjust underwriting criteria to ensure that you continue to finance the right clients. The company s analytics tool allows it to provide regulatory reports as well as management reports for internal use. It automatically generates the required monthly reports for the central bank and provides the basis for management reports that are reviewed weekly and monthly as part of the overall executive management and governance of the bank. The system provides robust analytic capabilities that enable managers to run fast and easy granular analyses on credit portfolios and identify potential risk areas. This proactive risk management capability will translate into improved underwriting criteria, enhancing the quality of the bank s overall asset base and eventually allowing it to move to risk-based pricing. These capabilities, along with the company s newfound ability to slice and dice its portfolios, are a differentiator among competitor banks and give it a distinct edge. 3

Transitioning from Wholesale to Retail Consumers have begun to play a far more active role in the health care decisionmaking process, as their exposure to cost has risen with high-deductible products. Reform will accelerate the movement to retail as the individual market explodes and employer groups consider dumping in favor of continuing to offer group coverage. To be successful in a new and unfamiliar retail environment, health plans must rapidly develop the ability to connect with members in a way that drives value for the individual, rather than for the group purchaser. In an evolved retail health insurance market, members will not be satisfied with limited service interactions and mediocre and confusing access to benefit information. They will begin to expect interactions and service levels similar to what they experience with other service providers in the retail space (e.g., mobile service providers and online retailers). To successfully transform their business to a direct-to-consumer model, health plans must fully embrace the philosophy that understanding member preferences and behavior and engaging members at every possible touch point is a new base requirement of doing business. Health plan leaders report that member-centric product and service strategy historically has not been an area of focus, and consequently they have limited expertise in this area. Plans recognize they must begin developing capabilities to integrate and augment their existing member data in a way that will provide them with a more comprehensive and robust view of their individual members. Plans need to quickly cleanse and link traditional claims data with other forms of consumer data, potentially including data from third-party sources (e.g., census data, Nielsen, Simmons and other market research data). In a fiercely competitive market, the differences between the prices and values of health benefit products will become indiscernible. Total customer management will become a prerequisite in the new consumer-driven retail market, with health plans tracking touch points at every stage of the member relationship life cycle; from prospecting, acquisition and customer service to retention and ongoing advocacy. To achieve this level of integration, common platforms to consolidate health plans internal data (claims, enrollment, providers, benefits, medical management, call center and other disparate data) must be developed, and these platforms will need to be integrated with providers clinical data (EMRs, EHRs) and potentially even provider practice management solutions. 4

Case Study Optimizing Acquisition and Retention The ability to utilize advanced analytics to understand and provide value to customers on a one-to-one basis is a strategic competitive advantage that drives business results. A progressive telecommunications provider, for example, has been so successful applying advanced analytics that it has been able to reap US$50 million more in total annual value. The team responsible for reducing churn and increasing product adoption needed to model customer engagement based on a combination of customer attributes and usage of different products both free and revenue-generating ones. Some products are more closely tied to customer retention than others, says the company s senior manager of business intelligence. To effectively manage churn and keep customer engagement high, we needed holistic insight into what the strongest levers are. Beyond descriptive reporting on who uses what, we needed a way to model usage patterns in order to design optimal marketing programs. Prior to using advanced analytics, it was a challenge to associate customer attributes and product usage with subsequent churn. The company employed descriptive analytics to track monthly service disconnects, as well as traditional baseline reporting that described what had already happened but it lacked a holistic, real-time depth of intelligence into who its customers were, what they were using and what programs the company could craft to encourage desired customer behaviors. Within five weeks of implementing the analytics solution, team members saw measurable results. They were able to examine linear and nonlinear models of several different groups of customers within different usage scenarios, which gave them a high level of customer insight in a very short period of time. We started to look at this data from multiple dimensions, report on it and really drive down into root cause for churn, says the company s director of customer loyalty and retention. With the analytics SAS has enabled us to perform, we ve been able to rapidly identify which products are most retentive to different customer segments. That s very important and it s something that was difficult to achieve in the past. Today, the company has a better idea of which customers might leave in the near term and is better able to target those customers with the right offers to keep them loyal. Additionally, the success of the modeling efforts has been easy to promote within the company because the results speak for themselves: Loyalty and retention campaign response rates have skyrocketed from 5 percent to 20 percent, and the company has achieved an ROI of 268 percent. It s an incredible story, says the senior manager of business intelligence. Prior to bringing in SAS, everything was a one-size-fits-all approach to customer knowledge. We ve been able to take a much more segmented approach, which has translated into significant cost savings as well as reduced churn. 5

Bending the Medical Expense Cost Curve Health plan leaders report they are redoubling their efforts to manage the cost of care and bend the cost curve while improving quality outcomes. In the new post-reform environment, plans will need to predict and manage medical loss ratios (MLR) to ensure compliance with regulatory requirements, optimize the profitability of products without the ability to underwrite, and manage risk associated with the shifts in their customer base. Proactively predicting the cost of care down to the member level will be the key to managing MLR and general medical expense in the post-reform environment. Even without the pressures of reform, health plans understand that bending the cost curve is the most important business challenge they face. Health care costs continue to rise at an unsustainable level, and plans ability to sell product is directly correlated to the affordability of health care and resulting insurance premiums. Improving Quality of Care With an increasingly aged and chronically sick population, care quality improvement programs, such as case and disease management, continue to be important focus areas for plans as they attempt to drive down the cost of care. Plans are seeking new and innovative opportunities to invest in care management solutions, especially since most efforts to improve quality will not be considered part of administrative expense when calculating MLR in the post-reform landscape. Unfortunately, health plans have always been challenged to validate ROI and accurately measure difficult-to-quantify savings and clinical outcomes of health intervention programs (case management, disease management, wellness programs, generic drug waiver programs, medication compliance, care gaps, etc.). One of the primary difficulties has always been how to find a comparable cohort of nonparticipants and apply the right statistical analyses to draw valid conclusions. Alternative Care Delivery Models Similarly difficult to quantify but with much greater potential for meaningful impact are plans efforts to transform care delivery by collaborating directly with providers to jointly manage cost, quality and outcomes. Alternative care delivery models are increasingly becoming focus areas for health plans and providers alike as they seek opportunities to mutually align financial and operational interests in new and innovative ways. Several new models including accountable care organizations (ACO), patientcentered medical homes (PCMH) and a wide variety of evidence-based and pay-forperformance theories are being tested for their ability to decrease total cost of care while maintaining or improving quality. Unfortunately, delivering performance and ROI for many of these models will be very challenging. Multiple metrics along multiple dimensions must be considered, and the ability to dynamically shift approaches to forecasting and measurement will be increasingly important. 6

Case Study Gaining Better Insight into Cost Drivers and Price Change Implications Now more than ever, health plans are recognizing the value of using advanced analytics to solve complex business problems, and such capabilities will be a foundational necessity for implementing any type of new and innovative costreduction strategy or tactic. One forward-looking regional US health plan uses advanced analytics to negotiate aggressively on behalf of its customers. As a result, the plan estimates that it saves nearly $200 million each year in medical costs for its customers, making its data-driven negotiation practices one of its biggest competitive differentiators. One way the plan achieves such savings through discounts is by using advanced analytics to analyze and model its reimbursement strategies and rate payment programs. With just one systems analyst and an advanced analytics solution, the plan performs rate modeling and reporting, develops the ability to defend reimbursement rates to providers, clarifies rate structures and spots billing problems. Our SAS solution has enabled the company and providers to really understand claim information, billing patterns and cost trends, says the company s director of professional and institutional reimbursement. SAS allows us to get to accurate, timely and detailed claim information for our analysis, rate setting and contracting activities. The plan also uses advanced analytics to monitor services, treatment patterns, trends, payment rates and the effect of all these items on premiums. It is able to assemble a complete picture of claims incurred within its service area and throughout the rest of the country and show its clients large employers where their employees are incurring services, the cost of those services and the price benefits of the company s network. The plan is particularly happy with the way the solution mines claims data to address customer concerns about the effectiveness of provider contracting and provider payment rate setting. That detailed claim information serves as a data source for a current customer to compare health plans, says the director of professional and institutional reimbursement. With such detailed information, the other carrier is obligated to prove their provider contracting effectiveness and discounting arrangements compared to ours. The plan clearly understands the future promise of advanced analytics in addressing medical expense through the provider/payer relationship. It is currently developing ways to use predictive modeling to determine how anticipated changes in reimbursement strategies may affect total payment, premium and customer retention. The plan also intends to expand its advanced analytics solution into situational analysis, which will enable it to develop scenarios and apply them to existing conditions to determine the best courses of action. 7

Conclusion US health plans fierce and immediate need to respond to reform, transition their business model from wholesale to retail, and bend the medical expense cost curve is creating an unprecedented sense of urgency and a tremendous amount of activity around using data and advanced analytics to help drive business decisions and new business models. Specifically, these pressures are increasing health plans need to develop sophisticated segmentation to drive risk evaluation; pricing; product design; program development; and acquisition, service and retention strategies. Plans are beginning to fully understand the need to engage and connect consumers and physicians, align incentives to members and providers, implement and evaluate new delivery models, and develop customizable analytics that are based on a common data platform. Health plan leaders are increasingly taking cues from other industries with more experience in advanced analytics. Over the coming months and years, executives will focus on dramatically improving their ability to use advanced analytics, relying heavily on the output of such capabilities to drive business decisions that will carry them forward profitably into an evolved and reformed marketplace. 8

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