CHAPTER FIVE Infogix Healthcare e book PREDICTIVE ANALYTICS IMPROVES Payer s Guide to Turning Reform into Revenue
30 MILLION REASONS DATA INTEGRITY MATTERS It is a well-documented fact that when it comes to healthcare spending, less is more. In fact, according to the Medical Expenditures Panel Survey (MEPS), more than 20% of the cost of healthcare is expended on a mere one percent of the U.S. civilian population, while 5% of the population accounts for nearly half of all spending. In the past, payers have been able to avoid bearing some of that burden by denying membership to patients with pre-existing conditions. The Affordable Care Act (ACA) eliminated that option, creating a whole new market of high-cost members that effectively blew up the existing actuarial tables. Additionally, during enrollment a significant percentage of the healthy portion of the eligible population opted not to purchase health insurance even in the face of potential fines, further skewing the scales toward the sickest patients. This is the reason the ACA included a provision for risk adjustment payments to lessen the influence of risk selection on the premiums plans charge as well as the financial burden that is created by accepting sicker members. While risk adjustment payments are helpful to mitigate short-term costs, they are merely treating the symptom. A better way to reduce those costs and the associated risk while improving outcomes for members is to prevent the events that drive risk from happening. In other words, keeping patients who are currently relatively healthy from becoming part of the 5%, or at least delaying their entry into that strata by a few years. The challenge is to find methods and target the at risk members to improve health and lower the burden on healthcare costs. That is what predictive analytics tied to population health management (PHM) brings to the equation. It is a sea of change for payers, and for the entire healthcare industry. But it is also the foundation for the value-based care of the future.
FIGURE 1. CUMULATIVE DISTRIBUTION OF PERSONAL HEALTH CARE SPENDING, 2009 100% 100.0 90% Top 1% of spenders account for >20% of spending ($275 billion) Cumulative Percent of Total Spending 80% 70% 60% 50% 40% 30% 20% 10% 0% Top 5% of spenders account for almost half of spending ($623 billion) 15.4 0 10 20 0.0 0.1 0.4 1.3 Total Personal Health Care Spending = $1.259 Trillion $36 Billion 2.9 $1,223 Billion 5.6 10.4 18.8 34.8 50.5 78.2 95 99 30 40 50 60 70 80 90 100 Percent of Civilian Non-Institutionalized Population Ordered by Health Care Spending A better way to reduce those costs and the associated risk while improving outcomes for members is to prevent the events that drive risk from happening. In other words, keeping patients who are currently relatively healthy from becoming part of the five percent, or at least delaying their entry into that strata by a few years.
Moving away from the traditional model Ever since the days of Hippocrates, healthcare has been focused on treating the sick or injured in front of the healer. Those with immediate needs received attention, while the rest of the patient panel was largely ignored. using PHM technology. Programs developed by the Centers for Medicare and Medicaid Innovation (CMMI) as well as commercial payers are creating the financial underpinnings for these efforts. It wasn t a question of not caring about those relatively healthy patients, instead it was a matter of time and resources. It made more sense to attend to patients who had immediate problems than those who didn t, especially for physicians with large patient panels. The financial model was also set up that way. In a fee-forservice world, providers are reimbursed for rendering services, not preventing services from being required. Still, they are focused primarily on those who are already sick. The opportunity for payers is with the next level down: those who are currently healthy but are trending toward becoming part of the group consuming an inordinate percentage of healthcare resources. NATIONAL HEALTH EXPENDITURES AS A SHARE OF GDP, 1980-2040 This thinking has led to the current healthcare crisis, where costs are spiraling out of control. According to the Centers for Medicare and Medicaid Services (CMS), healthcare spending in the U.S. accounted for 17.4% of the gross domestic product (GDP) in 2013. That figure is projected to rise to 19.6% by 2020. In other words, by 2020 nearly one in five dollars being spent in the U.S. will be spent on healthcare. For payers, that could be a huge blow to profitability. Hence the urgency to develop a solution. Percent of GDP 40% 35% 30% 25% 20% 15% 10% 5% Projected Providers began to address the issues of their most costly patients the so-called frequent flyers by 0% 1980 1984 1988 1992 1996 2000 2004 2008 2012 2016 2020 Source: CEA calculations. Providers have begun to address the issues of their most costly patients the so-called frequent flyers using PHM technology. Programs developed by the Centers for Medicare and Medicaid Innovation as well as commercial payers are creating the financial underpinnings for these efforts.
Why Payers Have the Advantage The advantage payers have in looking at longer-term trends is their access to claims data. Interoperability remains a hot-button issue among providers. Even providers who are part of the same organization may not be able to exchange or aggregate data within their health system due to incompatibilities between Electronic Health Record (her) and other technologies. This is an issue because patients rarely see only one physician. A typical adult female, for example, will have a minimum of a primary care physician (PCP) and an OB/GYN. She may also have seen a specialist or two, an optometrist and/or a dentist. If she has been to a hospital, whether as an inpatient or in the emergency department, or has been to an urgent care center, she has seen additional clinicians. Here s an example. When claims data for one member is compared to population data of other members who are part of a similar demographic, they show a pattern that is consistent with pre-diabetes. A review of clinical data confirms that the member s HbA1c levels rose over the past few years. This information is then applied against additional population data, and a clearer picture emerges. Members of this patient s ethnic group are shown to historically have a higher incidence of diabetes than the general population. Additionally, members who live in a particular area are also shown to have a higher rate of diabetes than the norm. Individually, these data points may not elicit much more than raised eyebrow. Taken together, however, they provide a strong indication that the member is likely to develop diabetes within the next 1-3 years. Since diabetes is expensive to treat, if the PCP is armed with this information from the payer and can prevent or delay its onset, it is better for everyone involved. Especially the member. All of that information may not be available in her EHR. But it will all appear in her claims data. By applying sophisticated predictive analytics to this data, payers can begin to understand the holistic health profile of the individual. As additional data sources are added, the profile becomes more complete and personalized. Hospital Inpatient Specialist PCP ED PAYER Demographic Socio- Economic Financial
Applying machine learning Where predictive analytics really begin to provide value is when using predictive modeling. The typical PHM analytics application makes its evaluations on a preprogrammed set of parameters. While valuable, it also means that its effectiveness is limited to whatever the clinicians who designed the models understood at the time. With predictive modeling, the system is constantly evaluating the data and learning like a human would if he or she only had time to learn as much as a computer that thinks with billions of calculations per second. The difference when the computer is learning is it can evaluate hundreds of thousands of possible models and make adjustments between each, doing all of this in hours or days. Going back to the above example, as long as the data indicating where someone lives is provided to the model, the machine learning allows it to look for correlations and adds them to the parameters it uses to predict members health trends. The model will learn whether or not a member s geographic residence will predict their likelihood to be diagnosed with diabetes within a specified timeframe. then select the ones that have the highest correlations. As it continues to learn, its predictions become richer, more accurate and more specific. While predictive modeling can be applied with a limited number of data sources, the more that can be incorporated the better it gets. As data sources grow, however, it becomes exponentially important to analyze the data with automated business rules to ensure the application is using clean, quality data. The business rules should be applied as the data enters the organization, and continue as it is transformed or moved from one system to another so it s easy to identify immediately if data has been dropped or transformed incorrectly. Having a strong data governance process in place will set the payer up to perform good analytics and ensure that it is receiving the full value of its investment. The models themselves can have thousands of potential attributes; the analytics application will With cognitive computing, the application is constantly evaluating the data and learning more the way a human would The models themselves can have thousands of potential attributes; the analytics application will then select the ones that fit. As it continues to learn, its predictions become richer, more accurate and more
Confirming the effectiveness Most of the discussion around predictive modeling to date has been speculative. It sounds good in theory, but how does it work in real life? To answer this question, Infogix took five years of a payer s historical claims and other data and ran the first three years worth of it through our predictive analytics engine. We then compared the predictions it developed to the actual claims for the two most recent years. The result was an 88% accuracy rate in predicting which members would develop a chronic condition requiring a higher level of services (and reimbursement). Adding in more data sources and a longer period of time for modeling could only make the predictions more accurate. According to a fact sheet from the CDC, in 2013 more than one third of all Americans 117 million people already had one or more chronic conditions. Preventing or delaying these conditions could improve member s quality of life and save billions of dollars in reimbursements, which is a pretty powerful incentive for looking into the power of predictive analytics. Helping patients stay healthier in such a visible way is also an important contributor to member satisfaction. In Chapter 4 we discussed the importance of engaging members as an element of managing the member lifecycle. Helping them stay healthier and avoid costly interventions (including pharmaceuticals) can have a strong impact on their perception of the payer and whether they wish to renew. If they perceive a high level of value due to the health programs of their current payer, a small cost difference becomes less of a factor in the decision. Even with the smaller dataset, however, the analytics were extremely effective in predicting who among the payer s entire population of millions of members would become more costly over the next two years. Now imagine the savings that could be created by reversing or delaying those trends across millions of members. The numbers are staggering, especially for high-cost chronic conditions such as diabetes, hypertension, asthma, chronic obstructive pulmonary disease (COPD), obesity, heart disease and cancer. in 2013 more than one third of all Americans 117 million people already had one or more chronic conditions. Preventing or delaying these conditions could save billions of dollars in reimbursements, which is a pretty powerful incentive for looking into the power of predictive analytics. 5 YEARS CLAIMS DATA = 88% ACCURACY
Working with providers and members Of course, payers can t make these changes alone. It takes a combined effort between payers, providers and members. Subsequent to n the predictive modeling, members will be ranked from the most sick to the least sick, to help prioritize intervention based on urgency. The payer then determines the method of intervention they wish to use for each condition and each patient. The predictive modeling can help with determining the method of delivery as well. Once completed these results need to be operationalized to automate the outreach. Another way payers can help improve care quality (and generate revenue) is by informing clinicians of care gaps. Payers can use claims data to create a list of members from the PCP s patient panel who are overdue for a physical examination, lab work, preventive screenings, etc. PCPs can then determine what actions should be taken and work with payers to encourage members to set up appointments. Payers can also provide education and incentive programs to encourage members to follow the recommendations of their providers. While some larger health systems are doing many of these things on their own, many other providers lack the technology and resources to deliver PHM on this scale at this time. Payers can demonstrate their value-add to providers as well as members by assisting in this effort. They can also add value by making professional care managers, registered dieticians, pharmacists, social workers and others available to members through their PCPs. Working together, payers and providers can address the needs of members not only to keep more of them from becoming part of the 5%, but to help the majority remain on the low end of the health resource consumption and cost scale throughout their lives. The tools and technology are already in place, and they are improving every day. Payers who invest now will gain a competitive advantage in keeping members healthy, fostering loyalty which in turn drives both retention and attracts more new members through the ACA exchanges. It will also enable them to establish an important beachhead as healthcare continues its transformation from fee-for-service to value-based care. PAYER Engagement Health Program Inform Verify Intervention MEMBER/ PATIENT Intervention Improved Outcome PROVIDER Of course, payers can t make these changes alone. It takes a combined effort between payers, providers and members.