Health Plan Optimization. Leveraging data to improve performance across the enterprise

Transcription

1 Health Plan Optimization Leveraging data to improve performance across the enterprise DST Health Solutions March 2015

2 The key to informed decisions Historically, payers have based decision-making on a combination of experience and intuition as well as insights gleaned from analytical tools that could answer questions about what had happened in the past and what was happening now. In today s rapidly changing industry, healthcare leaders are adopting advanced modeling and analytics tools that enable them to measure, evaluate, react and measure again. These tools can help decision-makers explicitly understand the constraints under which they operate as well as the cost differences that strategic and tactical decisions will produce. Advanced modeling and analytics, combined with experience and intuition, will yield the best possible decisions. 2 DST Health Solutions

3 Health Plan Optimization Leveraging data to improve performance across the enterprise Executive summary The healthcare market is changing rapidly, and payer organizations need new tools and technologies to keep pace with new challenges. Across the enterprise, payers face a multitude of complex demands, including healthcare reform legislation, rising medical costs, pressures to contain premiums, the emergence of a consumer-retail market, the aging of the population, the emergence of new provider-reimbursement models, and the prospect of gaining millions of previously uninsured members many of whom have chronic medical conditions in the coming years.. The key to successfully meeting these challenges lies in the interpretation of massive volumes of data available to payer organizations. Payers are awash in data data regarding medical and pharmacy claims, member and provider demographics, call-center operations, HEDIS scores, member complaints, provider contracts and much more. Transforming data into actionable information can dramatically improve decision-making. Until recently, however, payers didn t have the analytics tools necessary to do so. Using advanced modeling and analytics tools, payers today can leverage the value in the wealth of data they possess, thoroughly evaluate current operations, assess the impacts of proposed changes, forecast the outcomes of decisions, identify the best possible courses of action and optimize business functions accordingly. These advanced tools include both predictive and prescriptive analytics. Predictive analytics makes forecasts about the future; prescriptive analytics recommends particular courses of action and shows the likely outcomes of each decision. DST Health Solutions offers an advanced analytics solution, DST Business Performance Optimization, which enables payers to improve the performance of business functions across the enterprise. As former health plan business managers themselves, the DST team has deep industry knowledge, combined with extensive experience using the analytics tool that supports DST Business Performance Optimization, ensuring that the right questions get asked and answered. DST Health Solutions 3

4 Actionable recommendations, real-time improvements To compete in today s market, payers need high-speed, high-powered tools fully capable of executing complex multivariate analyses based on actual operating models and capable of yielding actionable recommendations that can deliver real-time improvements in costs, service and quality. 4 DST Health Solutions

5 Health Plan Optimization Leveraging data to improve performance across the enterprise New tools and technologies for a new environment As demands upon payers grow in scope and complexity, decision-making increasingly requires systematic modeling and analysis of data. Spreadsheet models generally fail to capture the complexity and interconnectedness of modern payer operations. Existing analytics tools, which focus primarily on diagnostic and descriptive analyses, also tend to be ineffective, as they provide limited statistical capabilities and serve largely as rearview mirrors and dashboards. They don t give payers a clear understanding of the enterprise-wide impacts of decisions, the magnitude of the tradeoffs associated with decisions, or what to do about these issues. Traditional operational-modeling tools and optimization software, which were designed to address complex but relatively static business issues such as manufacturingline design and logistics, typically require the support of a large staff of statisticians, data programmers, database administrators, financial analysts and operational experts. The staff might spend weeks or months creating run-once applications devoid of sensitivity analysis and incapable of adjusting variables or constraints in real time. Once an optimal solution is achieved, any sensitivity analysis that would help executives understand the risk parameters of a decision would require that the entire process begin again. Advanced optimization solutions, which offer both predictive and prescriptive analytics, are the key. These tools go beyond describing a business function and making forecasts about it they recommend specific courses of action. They are cost-effective and easier to use, requiring only trained quantitative analysts rather than the large specialized staff of the past. Using fully developed modeling applications such as optimization tools, payers can evaluate multiple potential courses of action and the tradeoffs and constraints associated with each, which adds a powerful new dimension to decision-making. Organizations that realize the full value of optimization across the enterprise will have a superior and more defensible competitive position, according to Thomas Davenport and Jeanne Harris, authors of Competing on Analytics: The New Science of Winning. 1 Payers have the data necessary to achieve that objective, but few have had the tools to translate the data into an optimized operation. This is beginning to change as advanced analytic tools such as optimization emerge. Predictive and prescriptive analytics Predictive analytics uses statistical, modeling, data-mining and other techniques to evaluate large volumes of data and make forecasts about what is likely to happen in the future. The emerging technology of prescriptive analytics goes beyond descriptive and predictive models by recommending one or more courses of action and showing the likely outcome of each decision. 2 DST Health Solutions 5

6 New tools, new opportunities Advanced optimization tools give payers unprecedented opportunities to assess business functions, fully understand the impact of proposed changes and select the best course of action. The ability to build models on which to base these analyses is essential to optimization. Optimization across the enterprise Among payer business functions being transformed by advanced optimization tools today are network management, provider payment, benefit design, formulary management, care management and bid preparation. Network management Choosing those providers and facilities optimal for the specific membership of a health plan is critical to payer success, but it requires significantly more data than payers typically use in network adequacy today. Establishing optimal networks also requires sophisticated analytic techniques. Current technologies make it difficult to assess the broad implications of non-financial contracting terms; for example, languages spoken by the provider, quality and performance measures and patient satisfaction. Advanced modeling and analytics tools enable payers to evaluate and balance a wide range of data in order to select providers and facilities that best meet the organization s objectives. These tools also can identify the costs associated with any deviations from the optimal network. Executives and managers can use these solutions to: Right-size and right-cost provider networks Model and update networks to reflect the market shift away from inpatient care and toward ambulatory care Ensure appropriate coverage Balance network complexity with appropriate geo-access and pricing 6 DST Health Solutions

7 Health Plan Optimization Leveraging data to improve performance across the enterprise Provider payment Traditionally, provider contracts have comprised a description of services and an associated reimbursement scheme; increasingly, they also include provisions related to quality measures and patient satisfaction. However, little consideration has been given to information-sharing, payer regulatory requirements, and clinical assessment all of which will help determine payer success in the changing healthcare environment. Advanced modeling and analytics tools can help payers take these and other variables, including member demographics, socioeconomic and geographic considerations, into account as they develop new payment and incentive structures for providers. Payers also can use modeling and analytics to assess innovative new provider-contracting models, including pay-for-performance, bundled payments, and shared cost/savings models. Data-driven provider negotiations Payers and providers regularly sign contracts calling for a variety of payment methodologies for a broad range of medical issues. The methodologies might range in complexity from fee for service to capitation; the medical issues might range from acute episodes such as a sore throat to complex cases involving total patient care. An optimization tool can help payers determine the circumstances under which a particular payment methodology is the right one to employ, taking into account such variables as the differences between and within specialties, geographic areas, socioeconomic attributes of patients, provider practice patterns and other factors. During contract talks, the payer can present all of that data to providers, helping them understand the rationale for the proposed payment methodology. If a payer wished to develop contracts with providers in a specific geographic area with a high percentage of low-income families with a high incidence of chronic disease, for example, the optimization tool could help the payer determine which reimbursement strategies would: Yield the highest-quality care Provide the strongest resolution of gaps in care Deliver the highest HEDIS scores Produce the lowest medical costs for the payer and the members DST Health Solutions 7

8 Optimization across the enterprise Benefit design The notion of personalized, self-designed benefit plans carries great appeal in the changing market, but how might the payer implement such products? To assess new products, payers have tended to rely on actuarial methods developed in the group-health era. These may have little validity in the masscustomization marketplace. An optimization tool can help both consumers and payers quickly evaluate a vast range of possible benefit designs and gauge the many tradeoffs involved in changing copayments, deductible amounts, out-of-pocket maximums, inclusions and exclusions, incentives and other variables. The optimized organization can: Develop designs that appeal to specific populations by taking into consideration such variables as member income, lifestyle, beliefs, media preferences and technology usage Quickly test new designs and determine the impacts on the member, payer and, if applicable, employer Develop mass-customized designs that provide the broadest possible coverage and the most attractive price points Create member portals that assist consumers in designing personalized benefit plans based on their unique preferences, finances and other considerations Formulary management Drug costs have been increasing at unsustainable rates at the same time that the usage of drugs is increasing. Payers are looking for effective ways to manage their formularies while promoting member health. One reason for cost increases is that the pharmaceutical industry is introducing specialty pharmaceuticals that are extremely expensive. One study recently estimated that specialty drug spend is expected to more than quadruple by 2020, reaching approximately $402 billion a year. 3 The costs of non-specialty drugs are soaring as well. Bloomberg ran an analysis of established drugs last year and found that, since October 2007, the prices of dozens of name-brand medications had doubled. 4 Drug usage also is increasing. In part, that s because the U.S. population is aging, and millions of Americans who previously were uninsured are getting healthcare coverage under the Affordable Care Act. Many have medical conditions that have gone untreated for years. The result is pent-up demand for medical care and, with it, medications. As spending increases, the ability to select those therapeutic agents that demonstrate the greatest degree of clinical effectiveness at the lowest cost is essential. Creating a sound formulary, however, requires more than an analysis of effectiveness versus cost. Payers also need to be able to take into account variables such as manufacturers pricing and rebate strategies, patient adherence, patient safety, dispensing fees and evidence-based medicine. A thorough analysis of these factors, in addition to cost and effectiveness, can help the payer develop a detailed formulary, with associated costs and rebates, as well as a clear understanding of clinical effectiveness. With an optimization tool, payers can undertake such analyses and determine: The costs of deviating from the lowest-cost formulary in order to accommodate other clinical or patientsatisfaction objectives The optimal distribution network, including dispensing costs The costs of deviating from the lowest-cost distribution network 8 DST Health Solutions

9 Health Plan Optimization Leveraging data to improve performance across the enterprise Care management Establishing the optimal mix of care management initiatives for the population served by the payer can help reduce costs and improve quality of care as well as quality of life for members. Payers will need to focus intently on this as the population ages and, in the wake of the ACA s enactment, as payers gain large numbers of previously uninsured members who have chronic medical conditions and significant pent-up demand for treatment. To manage care effectively, payer organizations have developed numerous programs, including smokingcessation, case management, disease management, utilization management, education and wellness education initiatives. But are these efficacious? Are they cost-effective? How much should be spent on each in order to derive maximum value for the total amount spent? An advanced optimization tool can help the payer determine which interventions should be deployed with which members and at what point in their disease process in order to provide the highest quality of life and best health status for members as well as the lowest medical costs for the payer. The optimized organization can readily identify gaps in care, match members with proper interventions and, ultimately, engage members in managing their own health. The payer can determine: Whether the dollars spent on wellness, cessation and education programs are yielding desired results The optimal distribution of dollars to achieve desired results Analytics provides insights for care management outreach, benefit design and other functions Market Sizing In-depth Insights Demographics Economic Capacity Income, Spending, Credit, Ability to Pay Lifestyles Shopping, Travel, Leisure, Auto, Dining, Technology Media Mix & Genre; TV, Print, Online, Radio, Outdoor Channels Brick & Mortar, Online, Mobile, Mail Psychographics Beliefs, Attitudes, Values, Opinions, Perceptions Consumer Lists Prospect Lists Databases As the healthcare market shifts to a consumer-retail model, healthcare payers will need to leverage data about individual consumers and market segments in order to conduct member outreach more effectively. Graphic adapted from and courtesy of IXI Services, a division of Equifax. DST Health Solutions 9

10 Optimization across the enterprise Bid Preparation Payers today place blind bids for members on health insurance marketplaces and, to some extent, in the Medicare Advantage and managed Medicaid markets. Without knowing the degree of risk presented by prospective members, payers must decide what premiums the new members will pay. Underbidding can lead to financial problems, with the cost of care exceeding premium revenue. Overbidding can price the payer out of the market. Additional issues arise after a blind bid has been approved. Key among these is the urgent need to ascertain member risk so that member care can be managed appropriately. Yet medical claims, which generally serve as the basis for risk analysis, typically don t arrive for months. An advanced optimization tool can help payers meet these pre- and post-bid challenges effectively. Before the payer places a bid, what if analyses can be run, addressing questions such as: What is likely to happen if a proposed Silver plan, for example, attracts large numbers of 50-year-olds instead of 20-year-olds? What additional resources would be needed to cover the members medical needs? How might the payer cover these medical needs while continuing to seek a reasonable margin? On a post-bid basis, the tool can be used for early detection and evaluation of the risk burden posed by members and for identification of specific care management and wellness programs that can improve the quality of care and control costs. Pharmacy claims, which typically arrive much sooner than medical claims, can also be used as a basis for these determinations. 10 DST Health Solutions

11 Health Plan Optimization Leveraging data to improve performance across the enterprise Optimization solutions Advanced optimization tools have been successfully used by Fortune 500 companies in many industries. These tools also have been introduced in a number of hospitals and healthcare payer organizations in the United States but remain greatly underutilized. The tool is powered by a series of functions When an advanced optimization tool is combined main question. The algorithm understands the with a superior analytics team, it can help payer organization s constraints, resource availability, task- organizations transform business functions across the processing time, relative prioritization, max-min logic enterprise. It can help answer specific questions or and many other operating-decision rules and criteria. describing the interactions of specific variables of the operating entity. These programs generate hundreds or thousands of equations, and each equation may contain a dozen or more variables. Through the use of complex, sophisticated mathematical algorithms, the programs search for the optimal solution for the system of equations, with a primary focus on the sets of related questions best characterized by phrases such as: What is the best? This question could apply to cost, result, process, approach or other parameter. What would happen if? This could refer to a process change, upcoming event or challenge, or different approaches to resolving an issue of set of issues. As sophistication of data analysis increases, so do value and competitive advantage Descriptive analytics Diagnostic analytics Predictive analytics Prescriptive analytics What happened? Why did it happen? What s likely to happen next? What s the best course of action to take? The advanced optimization tool goes beyond describing a business issue and making forecasts; it also enables users to see the impacts of multiple possible courses of action. DST Health Solutions 11

12 Optimization solutions Advanced modeling and analytics make optimization more cost-effective than ever. Historically, optimization programs have been difficult and costly to use. An advanced optimization tool streamlines the process. Using advanced data modeling and operational methods, modern optimization engines reduce the laborious process of data entry, variable and relationship programming and configuration, and constraint valuation and prioritization. This enables a relatively small staff to undertake and maintain the optimization process. It also allows end-users to interact with the model and conduct sensitivity, what-if and other analyses through a graphical user interface without having to rebase the model on each iteration. An advanced optimization tool, however powerful, is insufficient in itself to meet payer needs. Creation of highly effective models for use in the analysis is critical. Developing a model that takes into account the nuances and relevant variables of a specific business function within a payer organization requires extensive experience within the payer industry as well as extensive experience with advanced modeling and analytics. Thoughtful execution of the course of action suggested by the analysis is equally critical. 12 DST Health Solutions

13 Health Plan Optimization Leveraging data to improve performance across the enterprise DST Health Solutions offers DST Business Performance Optimization: Advanced analytics for today s healthcare organizations Advanced optimization tools are widely used in manufacturing, logistics, financial services and other industries to help businesses achieve maximum efficiency in such areas as production, distribution and marketing. In healthcare, leaders are just beginning to recognize the value of optimization as a tool to help them operate their businesses at peak efficiency and effectiveness. DST Health Solutions is among the first to bring business optimization to the healthcare market. DST Business Performance Optimization uses a proprietary modeling and analytics tool, with predictive and prescriptive capabilities, to help healthcare payers, accountable care organizations and integrated delivery networks optimize business performance across the enterprise. Experienced DST consultants build the necessary models and run the analyses. We currently focus on six key business functions: benefit design, bid preparation, care management, formulary management, network management and provider payment. the business function in question. The DST team that develops the model has deep experience in the payer industry, in the use of advanced analytics and in the execution of strategies that improve payer business performance. After all relevant data is loaded, we adjust the model to ensure it represents the current state. We then run predictive and prescriptive analyses that forecast the outcomes of possible changes to the business function and recommend specific actions to optimize it. Our consultants evaluate the inevitable tradeoffs between variables and constraints, identifying the optimal solution and the costs of choosing an alternative solution for non-economic reasons such as geographic or other considerations. Working with your staff and executive team, our consultants develop a detailed implementation plan. We can also help you execute the business decisions recommended in the plan. For more information about DST Business Performance Optimization, call DST Health Solutions at , us at marketingdsths@dsthealthsolutions.com, or visit us at For each Business Performance Optimization engagement, we obtain pertinent data from your health plan and other sources and load the data into a model, or template, built specifically to represent DST Health Solutions 13

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15 Health Plan Optimization Leveraging data to improve performance across the enterprise End Notes Marketplace enrollment statistics 2 1 Davenport, Thomas H.; and Harris, Jeanne G. Competing on Analytics: The New Science of Winning. Harvard Business School Publishing Corp Bertolucci, Jeff. Big Data Analytics: Descriptive vs. Predictive vs. Prescriptive. Information Week. More than 8 million people selected a plan through December 31, Available at Health Insurance Marketplaces through March 31, 3 CVS Caremark Specialty Trend Management Insights Report. November Available at cvshealth.com/research-insights/cvs-health-research-institute. That includes additional special enrollment period activity reported through April Langreth, Robert. Big Pharma s Favorite Prescription: Higher Prices. May 8, Bloomberg Business. Available at A total of 2.2 million, or 28 percent, of the people who selected a Marketplace plan during the initial open enrollment period were between the ages of 18 and 34. A total of 2.7 million, or 34 percent, were between the ages of 0 and 34. Nearly 3.8 million people selected a Marketplace plan during the March enrollment surge at the end of the initial open enrollment period, including nearly 1.2 million young adults (ages 18-34) or 31 percent DST Systems, Inc. DST Systems, Inc. (DST) has provided the information in this document for general informational purposes only, has a right to alter it at any time, and does not guarantee its timeliness, accuracy or completeness. All obligations of DST with respect to its systems and services are described solely in written agreements between DST and its customers. This document does not constitute any express or implied representation or warranty by DST, or any amendment, interpretation or other modification of any agreement between DST and any party. In no event shall DST or its suppliers be liable for any damages whatsoever including direct, indirect, incidental, consequential, loss of business profits or special damages, even if DST or its suppliers have been advised of the possibility of such damages. DST Health Solutions 15

16 About DST Health Solutions LLC DST Health Solutions delivers healthcare technology and service solutions that enable clients to thrive in a complex, rapidly evolving market. Our portfolio, which includes enterprise payer platforms, population health management analytics, care management, and business process outsourcing solutions, is designed to help clients successfully manage their most important business functions while facilitating strategic and financial growth. We assist commercial, individual and government-sponsored health plans, and healthcare providers in achieving the goal of affording the best possible care to their members and patients. DST Health Solutions is a wholly owned subsidiary of DST Systems Inc. DST Health Solutions 2500 Corporate Drive Birmingham, AL MarketingDSTHS@DSTHealthSolutions.com