Integration of Predictive Analytics, Risk Adjustment and Population Health Management

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1 White Paper Risk Adjustment and Population Originally produced as an MCOL ebrief sponsored by LexisNexis

2 Technological Advances and Innovative Insights The ways health plans and integrated delivery systems use data and the types of data they use are changing dramatically. New data management imperatives from the federal government have combined with technological advances and innovative insights to make prior approaches to keeping patients and bottom lines healthy inadequate. Moving forward, says Kendra Lindly, Vice President of Product Management, Analytics at Caradigm, a population health company based in Bellevue, Wash., as the lines between health plans and providers blur, responsibility falls much more on a community of care managers that includes providers. Now, all health stakeholders must integrate their current data sources with new types of information and apply predictive science to build a robust population health analytics platform that empowers them to focus their efforts and gain a competitive advantage in tomorrow s marketplace. Health organizations must integrate information about population risk gleaned from risk adjustment and information about population health gleaned from predictive modeling activities. Because health care has traditionally relied on risk adjustment to stratify groups of patients for care/case management, organizations relied on utilization data, and predictive modeling became popular to identify members who need intervention. That still remains a critical element of managing a patient care-focused business. But staying competitive under health reform -- as the U.S. health care system transitions from pay-for-service to pay-for-value -- requires a more macro approach to taking care of large groups of people. Health organizations must integrate information about population risk gleaned from risk adjustment and information about population health gleaned from predictive modeling activities. And they must add information from new, previously untapped sources to get the clearest picture of which patients will benefit most from which interventions. Familiar Concepts Take on New Importance The terms risk adjustment, predictive analytics and population health management have earned buzzword status since the passage of the Patient Protection and Affordable Care Act. 1 The major use of predictive modeling has been for case finding. According to Ian Duncan, FSA, FIA, FCIA, MAAA, a Professor in the 1

3 Department of Statistics & Applied Probability at the University of California Santa Barbara 2, that means identifying patients who are candidates for programs and interventions. Risk adjustment, he adds, is a subset of predictive modeling, being an application of a model. Generally, it means accommodating statistical differences that arise from the individual characteristics of a given patient or group of patients and that s usually accomplished by controlling for factors like disease or demographics, which have a direct impact on health status. Analytics have been an active part of the health care industry for at least 25 years. Data Analytics Aid Care Managers Analytics have been an active part of the health care industry for at least 25 years. Typically, analytics relied and focused on what occurred in the past -- retrospective claims utilization. As analytics evolved, pharmacy, lab and clinical data have been added. Now, a processing engine typically conducts a line-by-line review of the data. Statistical, clinical and financial models study the data, and aggregate and organize them. The engine then creates new analytic elements say, a patient risk score or forecasted patient cost -- that are more useful than the data were in their raw state. The end result: Care Managers can identify the highest-risk and highest-cost covered lives most likely to benefit from interventional management programs. Once data are analyzed, the information that comes from them goes to work; now, it s called population health management. While its execution is often complex, its underlying concept is simple indeed. David Kindig MD PhD, considered by some to be the father of the population health science discipline, defined it like this a decade ago 3 : We propose that the definition be the health outcomes of a group of individuals, including the distribution of such outcomes within the group, and we argue that the field of population health includes health outcomes, patterns of health determinants and policies and interventions that link these two. We note its differentiation from public health, health promotion and social epidemiology. 2

4 Health Reform Sharpens the Focus on Population Health The concepts and the functions themselves risk assessment, predictive modeling and population health management are familiar, but there s more focus on them now than ever before. The main reason: The federal government has adopted an aggressive stance in favor of better managing the overall health and cost of entire patient populations. Indeed, the infrastructure of health reform the move to account-able care organizations is itself a huge boost for population health management. Here, in addition, are specific elements of the ACA that emphasize population health: The Affordable Care Act s risk adjustment program is designed to reinforce market rules that prohibit risk selection by insurers, accord-ing to a recent Kaiser Family Foundation Brief. 4 Simply put: Funds are transferred from plans with lower-risk enrollees to plans with higher-risk enrollees. The goal of the program is to encourage insurers to compete based on the value and efficiency of their plans rather than by attracting healthier enrollees, the Brief points out. It uses enrollee demographics and medical diagnoses to estimate financial risk, then compares plans in each market segment to assess which plans to charge and which plans to pay. When you change your approach to improving quality and reaching the right members, that s when the predictive analytics approach has to change to the holistic patient and his or her clinical risks. Section 3025 of the Affordable Care Act added section 1886(q) to the Social Security Act establishing the Hospital Readmissions Reduction Program, which requires CMS to reduce payments to Inpatient Prospective Payment System hospitals with excess readmissions, effective for discharges beginning on October 1, Provisions in the ACA aim to promote community and populationbased activities, including the establishment of the National Prevention, Health Promotion and Public Health Council; the Prevention and Public Health Fund; and funding for Community Transformation Grants. The ACA also adds a new IRS requirement that hospitals conduct a Community Health Needs Assessment once every three years, and an implementation strategy to meet the needs identified through it. 3

5 Emphasis of Analytics Shifts to Keeping Patients Healthy Methodologies of the past clearly are no longer sophisticated enough. It all goes back to the approach, comments Kathy Mosbaugh, Senior Director, Market Planning, Health Care, at LexisNexis, who leads the clinical solutions business within the division. While predictive analytics often include some clinical data, they re often largely one-dimensional, she explains, and focus almost exclusively on forecasted costs. Methodologies of the past clearly are no longer sophisticated enough When you change your approach to improving quality and reaching the right members, she says, that s when the predictive analytics approach has to change to the holistic patient and his or her clinical risks. She adds: If you look at it just financially, you may be ignoring comorbidities. If a person s costs are going up the same as someone with more comorbidities, you need more detail than standard risk adjustment methodologies used for reimbursement can provide. You also need to know that your efforts will be rewarded with patients who respond to them. Mosbaugh s company provides both clinical predictive modeling and a motivation index indicating how likely someone is to be highly engaged with his or her health care. As well, it employs condition-specific predictions that really help focus where you re going to expend care management resources, she says. It s one thing to know that you have enough money coming in; it s another to be able to predict which conditions will increase. That s where risk drivers get specific. Socio-Economic Data Power Better Understanding of Patients Status Traditional sources of data -- medical and pharmacy claims, patientcompleted health risk assessments and billing re-cords clearly are no longer enough. Now, leading-edge companies are turning as well to public records, such as credit reports and bankruptcy declarations. There are conditions that are very much dependent on a person s behavior and circumstances, Mosbaugh explains. For example, depression may lead to a job loss, which can worsen the depression -- and cause additional mental health issues. Substance abuse, 4

6 similarly, can be tied to, say, a negative court ruling. Additional life events that produce documentary information that can enhance the predictive ability of patient data include divorce, a change of residence and a change in income. Caradigm Uses LexisNexis Analytics Function to Predict High-Risk Patients What does a socio-economic data-fueled population health management application look like in practice? Caradigm, a population health company based in Bellevue, Wash., has a portfolio of integrated applications built on top of a data aggregation platform that brings information in from a number of disparate systems. Clinical, claims, lab -- we aggregate the data, normalize them and keep them as our single source of the truth, says Scott McLeod, Director of Product Marketing there. From those data, we run some analytics that are indicative of future events, but I wouldn t call it predictive analytics. Under a valuebased system, plans and integrated delivery systems can avoid penalties and receive incentives by improving their performance on quality measures. Now, he continues, we still bring all the data together to aggregate from those systems, but we feed that information through the MEDai Science and analytics available through LexisNexis. That model indicates not just who s at high risk today, it also identifies those patients who are moving up the risk ladder. We didn t have that capability before. His firm also values the motivation factor documentation the solution provides. Care managers need to focus on patients who will produce the greatest results, McLeod notes. Moving the needle for both care and dollars. The LexisNexis solution and MEDai Science are at the intersection of our solutions that manage data and manage population health. It s like putting a turbo booster on your car. Plans That Integrate Various Data Sources Are Positioned to Win Caradigm saw the writing on the wall. We ve always focused on solutions that enable better care for the patients who need it, McLeod says, but with health reform and changes in reimbursement models, enhancing our predictive ability became even more important. Under a value-based system, plans and integrated delivery systems can avoid penalties and receive 5

7 incentives by improving their performance on quality measures. It s really a reimbursement thing, he adds. It s important to have a good understanding of your patient population to identify those who have gaps in care. It s always a reimbursement thing, and sources like the LexisNexis solution offer the kind of under-standing about patients that allows organizations to expand beyond assessing just the impact on reim-bursement. Plans and integrated delivery systems, too, must better integrate existing risk adjustment and predictive analytics activities, and their population health management programs must incorporate public records-based socio-economic data. That s the information on rubber-hits-the-road real-world life events that can dramatically affect a patient s physical and mental health. It s important to have a good understanding of your patient population to identify those who have gaps in care. When the right information is added to an integrated population health management program that incorporates financially focused risk assess-ment and clinically focused predictive analytics, organizations gain insight into patients health now and in the future. And that s exactly what they need to offer the highest quality attainable. Bibliography/Citations 1 Patient Protection and Affordable Care Act, ppacacon.pdf 2 Predictive Modeling News, Volume 8 Issue 2, February The modeling is population based, meaning it focuses on the patient and the conditions and risks of each patient in the population... What Is Population Health? David Kindig MD PhD and Greg Stoddart PhD, Am J Public Health March; 93(3): Explaining Health Care Reform: Risk Adjustment, Reinsurance, Risk Corridors. health-reform/issue-brief/explaining-health-care-reform-risk-adjustment-reinsurance-and risk-corridors/ 6

8 Differences in Risk Models for Care Management vs. Provider Evaluation/Payment Purposes by Kim S. Jayhan How are models designed for measuring average risk of population for payment purposes different than predictive modeling for care management purposes? A common misconception is that the same analytics can be used to stratify for care management and provider profiling. While both approaches rely on risk adjustments of the population, they are used differently, depending on the need. Predictions driving care management (CM) focus on identifying the best patients who can be engaged to participate in their own care and improve their health, with assistance from the CM team. The modeling is population based, meaning it focuses on the patient and the conditions and risks of each patient in the population, regardless of who is treating the patient. The modeling is population based, meaning it focuses on the patient and the conditions and risks of each patient in the population... For too long, care managers received patient lists most often highest cost (and deemed highest risk ), without predictions. Other than their clinical experience, no insights were available to prioritize the lists, care for the patients, or achieve improved outcomes. Predictions are now used to surface those patients (in a larger population) whose risk is due to chronic conditions, and often to identify how much of the patient s future risk is driven by the chronic condition. Because the predictions look forward, and provide insights on what is likely to happen without interventions, Care Managers who receive patient lists with predictions now know which patients are most at risk for costly events (e.g., Inpatient Admissions/Readmissions, ER Visits) in the future. Advanced predictive analytics reveal the underlying risks meaning, what is actually driving the risk from a cost perspective, for the next 12 months? And, how do I prioritize the care for each patient? By understanding that Patient A s future risk is driven by renal failure and Patient B s risk is driven by cardiac issues, a Care Manager can make the best decisions and recommendations of care for a patient. 7

9 Analytics for provider performance and reimbursement decisions turn away from the future, looking more at the present and the past x period (typically quarterly or annually). Less importance is given to which chronic conditions are plaguing a population, but rather, what is the overall average risk of the population, relative to the physician panel and to external benchmarks. These risk models are often referred to as Concurrent Risk and Case Mix (respectively). Provider Evaluation/Profiling is comparative in nature, relying on severity adjustments to normalize the statistics, so that fair analysis can be made on costs and outcomes for each provider. Analytics that look backward, at the present and the future are all critical to the evolution of care management and provider profiling... Using provider based risk indicators, analysts are able to assess the ratio of expected resources (e.g., visits, lab, RX, acute care) used to care for the patient (or population) versus the actual resources used, thus determining the overall efficiency of care. This overall efficiency, along with the other severity adjustments can all be used in calculating provider reimbursements, which are typically value based. Using provider based risk indicators, analysts are able to assess the ratio of expected resources (e.g., visits, lab, RX, acute care) used to care for the patient (or population) versus the actual resources used... Analytics that look backward, at the present and the future are all critical to the evolution of care management and provider profiling. Knowing which analytics to use when, and in which circumstances, drive the best results and outcomes of patient health and optimum reimbursement. 8

10 For More Information: Call or visit About LexisNexis Risk Solutions LexisNexis Risk Solutions ( is a leader in providing essential information that helps customers across industries and government predict, assess and manage risk. Combining cutting-edge technology, unique data and advanced analytics, Risk Solutions provides products and services that address evolving client needs in the risk sector while upholding the highest standards of security and privacy. LexisNexis Risk Solutions is part of Reed Elsevier, a leading global provider of professional information solutions across a number of sectors. Our health care solutions assist payers, providers and integrators with ensuring appropriate access to health care data and programs, enhancing disease management contact ratios, improving operational processes, and proactively combating fraud, waste and abuse across the continuum. Due to the nature of the origin of public record information, the public records and commercially available data sources used in reports may contain errors. Source data is sometimes reported or entered inaccurately, processed poorly or incorrectly, and is generally not free from defect. This product or service aggregates and reports data, as provided by the public records and commercially available data sources, and is not the source of the data, nor is it a comprehensive compilation of the data. Before relying on any data, it should be independently verified. LexisNexis and the Knowledge Burst logo are registered trademarks of Reed Elsevier Properties Inc., used under license. Other products and services may be trademarks or registered trademarks of their respective companies. Copyright 2015 LexisNexis. All rights reserved. NXR