As the applications of real-world evidence (RWE) in pharmaceutical manufacturing expand, so are the uses of RWE for payers in a value-based landscape. Horizon Blue Cross Blue Shield of New Jersey is employing RWE in a range of analyses that are delivering actionable, real-time insights for the payer, providers, patients—and pharma. Saira Jan, M.S., Pharm.D., director of pharmacy strategy and clinical integration for Horizon and clinical professor for the Ernest Mario School of Pharmacy at Rutgers University, shares Horizon’s experience with RWE analyses to design interventions, improve members’ health, and support outcomes-based contracting.
Q: What kinds of information must payers have to function in a value-based health care system?
A: Payers need actionable real-world data (RWD) to stratify populations and identify specific interventions that can both bring value to members and improve outcomes. And payers want that information to be specific to their own members; I’m interested in my population.
A well-designed clinical study may identify a response rate for an outcome, but results for its trial population, based on inclusion and exclusion criteria, may not be the population that we would see in real life. Our Horizon-specific population may be younger or have a different comorbidity. With real-world data, we can look at our own population to identify high-risk members who would benefit from a specific intervention.
We can also perform real-time and long-term monitoring of our population with RWD, taking into account their current comorbidities and risk factors, to generate actionable insights. We can refresh the data every quarter to identify new opportunities that we can act on in real time. Then, every quarter we can give providers actionable insights—increasing adherence for specific drugs, for example—that we know will improve outcomes in specific populations among our members. When working with providers in value-based health care, it is essential to have timely data on which to act.
Q: Historically, what have the barriers been for payers using real-world evidence in identifying opportunities to intervene or in coverage decision-making?
A: Time and transparency. Currently, most payers evaluate drugs for their formulary based on the clinical literature. All we can say with those evaluations is that this drug shows better results in RCTs and/or observational studies compared to the other drug, and should probably have preferred status on the formulary. Occasionally, we will do a manual drug-utilization evaluation to determine if a drug should be added to the preferred queue. But that kind of analysis can take six to eight months, and can’t be done for every therapy class.
If all of the drugs have the same response, then the cost comes into play. But to do value-based contracting or a risk-sharing arrangement with a pharmaceutical company, a manual evaluation offers little transparency for pharma in how we evaluated the drug for our population.
In general, identifying high-risk populations based on real-life utilization combining predictive modeling and a sound clinical algorithm that constantly updates with current utilization data, doing specific interventions in a targeted manner, and then tracking outcomes is very challenging. The process requires clinical and analytical investment and academic rigor.
Q: How do real-world studies address the challenges of time and transparency to support outcomes-based contracting?
A: With real-world data and rapid-cycle analytics, we can run algorithms in six to eight weeks to find out which drug shows the best outcomes in our population. We can show that Drug A and B are better drugs for our members and why we want them preferred in our formulary. That means instead of a pharmaceutical manufacturer coming to us to propose outcomes-based contracting for their drug, I can choose the drug that shows better results in my population. Then I can go to pharma and say, “Here are the metrics, the logic, the goals that we used to find that outcome.” Because we can’t do outcomes-based contracting based on what we think. To sit across the table from pharma, we need transparent, validated outcomes that help us position the drug on the formulary effectively for our members. This is a joint collaborative exercise between pharma and payer to develop and agree on metrics.
Real-world evidence enables us to understand outcomes for multiple drugs and multiple endpoints; certain drugs may actually have better outcomes in a certain subpopulation. Historically, a lot of outcomes-based contracts are about improving adherence, and the outcomes are dictated by pharma. But now we can do outcomes-based contracting based on mortality event reduction, hospitalization reduction, and other outcomes that can be tracked in real time. This aligns with value-based contracts that we have with health systems and focuses on outcomes—and that is a win for all stakeholders.
Q: Different kinds of real-world data offer different information. What can you learn by integrating data sources?
A: Payers always struggle with integration of medical and pharmacy claims data. We may have a medical benefit, for example, but not a pharmacy benefit. So, working with Aetion, we were able to integrate the two kinds of databases to show a reduction in the medical cost of an intervention even as the pharmacy cost goes up. As new high-cost medications come into the market, it’s increasingly important to show the whole story—yes, our pharmacy cost will go up, but our medical cost will go down.
The opportunity Horizon has with Health Sphere, our information exchange hub for EHR, medical, and pharmacy claims data, helps take the rapid analytics to a new level. Now we can support stratification combined with integrated health care data as well as other social determinants to guide customizing interventions to improve outcomes.
As more drugs are billed through medical benefits, tracking the outcomes of those drugs by defining the population where these drugs should be used, and doing targeted interventions, is every clinician’s dream. It’s critical information at the payer level, the provider level, and even at a member level because members also have cost shares. If I’m a member and I have co-insurance, I might be paying 25 percent of a $1,500 drug or 50 percent co-insurance for high-cost drug. That’s a lot of money. As a member, I would want to be sure that I’m taking the right drug that will prevent hospitalization or an adverse event. Employer groups, providers, and payers are in the same space, and the clinical world is aligned for the first time.
So, while integration of these kinds of databases isn’t completely new, combining integration with rapid analytics, intervention, outcomes-based contracting, and physician education to change prescribing patterns is new.
Q: Horizon recently completed a pilot real-world evidence study looking at SGLT2 inhibitors in your diabetic population. What did you learn?
A: We focused on diabetes because it is an epidemic, the drugs are all high cost, and there are a lot of drugs and different therapy classes coming out. And we don’t know if a member on metformin needs to be started on a DPP-4 or SGLT2 inhibitors to show real event reduction. Collaborating with Aetion, we performed an analysis that showed SGLT2 inhibitors were the better product to reduce cardiovascular events in our high-risk cardiovascular comorbidity members, unless there is a contraindication. We learned you can intervene with specific interventions in a stratified population to improve outcomes significantly.
Q: How can this kind of analysis influence providers and prescribing patterns?
A: With transparent results, we can educate providers in a way that will encourage behavior change. Today, providers’ behavior tends to be guided by clinical literature. But if we can give them evidence specific to their patients, their populations, they will act on it. With this kind of analysis, Horizon can say to an internist or family physician, “Here are your ten high-risk patients and here are two opportunities for intervention that can show better outcomes by event reduction.” That’s far more valuable, actionable information for physicians than reading a study published in the New England Journal of Medicine that says diabetics with comorbidities have higher risk, but may or may not reflect that provider’s own patient panel. Provider feedback on these analyses and capabilities has been phenomenal, and the buy-in is extraordinary because of the academic rigor and transparency of the process.
Q: What were the actionable insights your diabetes pilot study generated? What formulary or process changes is Horizon making now as a result of that analysis?
A: For every line of business—Medicare, commercial, and Medicaid—we have identified members who are high risk due to cardiovascular comorbidities and other risk factors. Now we are providing that information to our providers, saying these are your patients who should be started on SGLT2 inhibitors, and these are your members who already are on SGLT2 inhibitors but are non-adherent and require education. Our pharmacy staff are also reaching out directly to those members to help them become more adherent to the therapy. With outcomes-based contracting in place, we are doing further analysis to identify more opportunities to manage the risk pool.
Q: Following that diabetes pilot study, what other therapeutic areas will you look at?
A: Over the next two years, we want to look at other major cost drivers in the medical and pharmacy spaces: ulcerative colitis and Crohn’s disease, COPD and asthma, rheumatoid arthritis, psoriatic arthritis, psoriasis, hereditary angioedema, oncology, and hemophilia. Particularly, therapeutic areas with key drug classes that drive cost and that have multiple drugs as choices. These are the areas in which we need a constant outcome measure to know our return on investment. We want to see where else real-world evidence and rapid analytics can be utilized to intervene earlier by infusing predictive modeling.
Q: What else do you foresee being able to do with real-world evidence?
A: I’d like to use predictive modelling and rapid analytics in our patient population to understand the early onset of a condition and prevent patients from ever becoming high risk. If we can predict what populations will become high risk, we can develop interventions to reduce that risk. By looking at drug utilization behavior and outcomes, we can engage pharma and share valuable information in the early phase of drug trial design that is more applicable to real-life data. We can educate prescribers to intervene at a different point or on a different level in their population. We can share information on outcomes with CMS and employer groups to socialize results. Engaging consumers and empowering members is another opportunity. The opportunities are unlimited and every use case will be different. The future of pharmacy is moving faster than any other area in health care and innovations like this are critical to driving success.