While the ongoing Congressional debates have served to demonstrate how far apart Americans are regarding a variety of issues around healthcare, there is one topic all sides agree on: it costs too much. This point of view is supported by the data. The U.S. Department of Health and Human Services estimated that the total National Health Expenditure for 2016 was to be $3.35 trillion, a 5.8% increase over the previous year. That means that the per capita spending on healthcare would have exceeded the $10,000 mark – for the first time ever.
Worse, all signs point toward this trend continuing, with spending projected to grow by 5.8% on average each year through 2025. That’s 1.3% faster than the Gross Domestic Product (GDP) is expected to grow.
Where is all this spending occurring? In 2015, 32% went to hospital care and 20% went to physician care. What was the next-largest line item? Prescription drugs, at 10%. In fact, total sales of prescription drugs in the U.S. in 2016 were $448.2 billion, which matched the 5.8% growth rate for healthcare overall. Even more concerning is that in 2017, some payers told EXL that their drug benefit costs surpassed their medical benefit costs, the first time this has ever happened.
Clearly, this is an unsustainable path. To address it, one of the strategies payers are beginning to introduce is placing more focus on creating outcomes-based contracts with life sciences organizations.
Outcomes-based contracts defined
Whether you call it value-based, or outcomes-based, or any other name, an outcomes-based contract ties the life sciences company’s product to the benefits it produces in the real world – i.e., how it helps the patient’s issues (and by how much), along with how it drives down the overall cost of care. These two factors are interrelated.
In the last few years, the goals of healthcare have shifted from an almost exclusive focus on treating the sick to helping patients stay healthy, or at least healthier (in the case of those with chronic conditions). Not just because it’s better for the patient, but also because the healthier patients are, the less it costs. Even for the sickest among us. If you can keep them out of the hospital and shift the site of care to be primarily at home – the lowest-cost care setting – then you can begin to make a dent in the spiraling costs of healthcare.
This is why outcomes-based contracts today are beginning to tie payment to measurable outcomes. In other words, if a patient population doesn’t show an agreed-upon level of improvement (as measured by their total cost of care), the life sciences organization is obligated to give the payer a rebate or discount.
The result is that life sciences organizations are now assuming some measure of risk for the outcomes. Yet risk can mean different things to different organizations, so it is important to spell out exactly what the expectations are within the contract.
The assumption of risk impacts life sciences organizations in two ways. The first is that they not only must prove to payers that their drugs or devices are clinically effective, but that they are more cost-effective at delivering the desired outcome than those of a competitor. Not just now, with the current population, but also projected against trends for the future as determined through predictive and prescriptive analytics. The ability to demonstrate improved outcomes and trendlines in hard numbers is a decided competitive advantage for commercial effectiveness teams, as well as important guidance when negotiating the contract.
The second and more challenging factor is that the life sciences company must demonstrate the ability to improve compliance. As former Surgeon General C. Everett Koop said, “Drugs don’t work in patients who don’t take them.” Life sciences organizations that show payers that they have a plan for increasing compliance gain a competitive advantage over those who simply sell products and leave. Improving compliance is an important step toward fulfilling the metrics outlined within the outcomes-based contract. And fulfilling the metrics creates a lead-in to a next-level conversation about who is the next group that can be impacted.
The only question then is can payers trust the outcomes results they are being shown? This is where it helps to have the analytics performed and verified by an objective third-party expert. If the life sciences organization performs the analysis internally, there will often be suspicion that the analytics were set up to generate the desired output. If they are performed by a credible third-party expert, however, the numbers will be viewed as more genuine and, therefore, will be more trusted.
A triple win
When outcomes-based contracts are constructed with a solid foundation of data, everyone wins.
Life sciences organizations gain an ability to prove the value of their drugs or devices based on patient outcomes in the real world – both current and projected. They can also expand their market by demonstrating an ability to treat a larger, risk-stratified population. Rather than simply treating the sickest or most obvious patients, they can use the data and analytics to show how they can also help patients who may have a risk of declined health and increased cost of care. This risk stratification not only will show who has the greatest risk, but also which patients have the highest impactability scores, (i.e., will benefit most from closing care gaps).
Through the unbiased data, they can demonstrate how they can impact payers’ success rates and ROI, leading to stronger relationships with them. Maintaining a strong, active focus on outcomes demonstrates a patient-centric mindset as well. This perception can be invaluable in encouraging patients to ask their physicians for a particular brand of drug or device, especially as patients share their experiences with one another through social media.
Payers benefit by realizing measurably lower total cost of care for their members. Closing care gaps and keeping members healthy costs far less than paying for expensive procedures later. For example, ensuring diabetic members have regular HbA1c tests and yearly eye and foot exams costs far less than amputating a limb, and results in a better quality of life for the member. From a business perspective, outcomes- based contracts tie the price being paid for drugs and/or device to an actual, measurable value, with risk being spread across both parties.
Patients also benefit by staying healthier as a result of a greater focus on preventive rather than reactive care. This not only improves their quality of life; it reduces their out-of-pocket costs for healthcare. That will be good news to patients. According to the Centers of Medicare and Medicaid Services, the out-of-pocket spending for healthcare by US consumers grew to $338 billion in 2015, an increase of 2.6% over the previous year. With the expansion of high-deductible health plans (HDHPs) as a strategy to reduce high premium costs for health insurance, this number is likely to continue growing for the foreseeable future.
Showing patients how to stay healthier and enjoy a better, more active quality of life, while spending less of their own money on care, is a double benefit. Just as important, using analytics to show what it will cost them in terms of money and quality of life if they don’t follow the plan of care as outlined by their physician is a powerful way to drive compliance.
Key success factors
Those are some of the many reasons for creating outcomes-based contracts between payers and life sciences organizations, as well as the benefits that can be achieved on all sides. But what does it take to be successful in developing these contracts?
The two most important factors, without question, are commitment and trust between life sciences organizations and payers. Both sides must commit the time and resources for the design, implementation, and monitoring of outcomes-based contracts. As mentioned previously, using independent, third-party analytics both sides agree on to create the patient model removes any doubt about the outcomes being “self-fulfilling,” which helps with generating trust.
Next is having clearly defined metrics. Again, both sides must agree on what the endpoint is and what an “acceptable” level of success is. One thing to keep in mind, however, is that while it can be tempting to go after big, complex issues, for at least the first year it is better to keep things simple. Choose products where the outcomes aren’t so dependent on many moving parts or complex interactions of a number of factors that increase the chances of failure. Once both sides have some experience with outcomes-based contracts you can move together into larger, more complicated issues. Finally, choose drugs or devices that lend themselves more readily to outcomes-based contracts. Not all conditions or products are good options. Rare diseases, for example, are typically more difficult to manage and, thus, less attractive candidates. The best choices, especially in the beginning, are conditions and products that have endpoints that can easily be obtained through the patient model, and that can be reliably and objectively measured quantitatively rather than subjectively within a specific, reasonable timeframe.
Uncovering hidden opportunities through patient personas
Much of the focus in healthcare is on the patients in front of physicians. Today, we are able to gather all types of data, including clinical, medical and pharmacy claims, laboratory, socioeconomic, demographic, behavioral, and even consumer-generated information. We can then use advanced analytics to create an amazingly accurate picture of who those patients are, where they stand on the risk scale, and whether their health trends are getting better, worse, or remaining the same.
Yet this visible patient set is like the proverbial tip of the iceberg. Many others in a provider’s patient panel or a payer’s member database lurk below the surface, unseen yet presenting a danger that can sink efforts to move to a value-based care model. Life sciences organizations can help uncover those undiagnosed patients who are candidates for a particular drug or device through the development of personas. Essentially, a persona is a model of a group of people who share similar key characteristics such as their age, gender, level of education, where they live by Zip+4, level of income, and other factors. By analyzing data about known patients, life sciences organizations can build models that are representative of them in the aggregate. They can then analyze a payer’s member database or a provider’s patient panel to segment those who are not already known into the appropriate persona, which helps uncover high-risk patients who have not previously been identified.
A well-designed persona model does more than simplify bring them to the surface, however. It also uses data to determine which of these undiagnosed, high-risk patients have the highest impactability – those for whom closing multiple care gaps will yield the best results. Additionally, it will highlight which care pathways have been most effective for patients/members who fit specific personas. And it will use behavioral data and analytics to highlight the patients with the greatest intervenability (i.e., those who are most willing and able to listen to messages about their need for interventions, especially when they involve a look ahead to the price of non-compliance, and then act on them).
One additional parameter that must be built into the persona model to make it effective is a prediction of which messages and types of communications will resonate best with patients/members who fit a particular persona. This information is gathered through behavioral data, beginning with what has been most effective in communicating to the patients already being treated.
The data can also unearth the reasons a willing patient may not be compliant. For example, a diabetic patient with no access to transportation may live in a neighborhood where quick service restaurants are abundant and grocery stores selling healthy options are rare. The patient is willing to eat healthier, but current circumstances prevent it. Finding a way to address this issue is important to driving adherence and compliance, but won’t show up in a traditional electronic medical record (EMR).
With all of this data-driven evidence in place, life sciences organizations can reach out to payers to show not only how their drugs or devices improve outcomes when used, but how the organization can expand the benefits to a larger population and drive adherence and compliance. All of which help improve the quality of care while driving down the total cost.
By continuously adjusting and improving these persona models as more volume and types of data become available, having these capabilities make the life sciences organization an infinitely better business partner than one that simply sells drugs or devices. It also helps both sides agree to an outcomes-based contract based on realistic expectations and quantifiable results.
All of this sounds good in theory. The real question is how it can be applied in the real world. To answer that question, here is a sample case study of how advanced predictive and prescriptive analytics could be used to guide the implementation and monitoring of an outcomes- based contract for a drug to help patients/members who were at risk for congestive heart failure (CHF).
It began with EXL selecting a 4,281 records of patients with a history of CHF from its extensive database of patients/ members. EXL then applied its CHF risk scoring algorithms to them. Of that population, 3,640 were placed in the high-risk category, while the other 641 fell into the medium-risk category.
Additional factors were also identified for the high-risk group. For example, 2,726 of them had one or more co-morbid conditions such as diabetes or chronic pulmonary obstruction disease (COPD), while 2,347 were using an ARB (angiotensin II receptor blocker) or ACE (angiotensin-converting enzyme) inhibitor. Finally, the case study looked at two competing drugs, labeled Product A and Product B. The baseline analytics held no surprises. The highest-risk patients generated 10X the per member per month (pmpm) costs of the medium-risk patients. Much of that cost (71%) was the facility cost (i.e., hospital visits), although physician and prescription costs were also significantly higher for the high-risk patients versus those with medium risk.
What’s important is the advanced analytics EXL uses go beyond the raw numbers to enable the life sciences organization to understand who each of these patients is, including where they live down to a Zip+4 location, and assign a specific persona to them.
When all of the data (including costs to care for the patients) was aggregated, it showed that the total pmpm cost of care for high- and medium-risk patients on Product A was significantly lower than the total pmpm cost of care for those on Product B. In fact, the facility costs for patients using Product B were slightly higher than the overall facility cost, and 75% higher than for those using Product A. This is the type of unbiased, third-party data any marketer would be delighted to supply to the commercial effectiveness team.
CONGESTIVE HEART FAILURE - PATIENT ANALYTICS STUDY
Another way to slice the data is to compare Product A without an ACE or ARB, Product B without an ACE or an ARB, and an ACE or ARB alone. Once again, it shows that Product A alone is more effective than either of the other two solutions alone in driving down the cost of care. Or, you can look at the services being utilized by the total CHF population as a baseline, then break those down into those who use Product A, those who do not, and patients with one or more comorbidities. The data again shows the effectiveness Product A is having in reducing utilization, especially in the most expensive care settings – the emergency department and inpatient acute care.
Armed with this data, life sciences organizations can work with payers to create outcomes-based contracts that make sense and that are demonstrated to be achievable. The critical message is that they are not just there to promote more products, but to improve the health of patients/members and help them drive down the cost of care.
Until recently, there has been little interest in the U.S. in tying the cost of care to outcomes. That is now changing, especially as the industry moves to a value-based care model and consumers are feeling more of the pain for the skyrocketing cost of care.
Yet it’s one thing to talk about it. It’s another to make it happen. All healthcare stakeholders are frantically searching for answers. By taking advantage of advanced analytics, life sciences organizations can begin providing those answers, while preparing themselves to enter the world of outcomes-based contracts.
About the Author
Vice President, Global Sales, Life Sciences
John is responsible for leading the commercialization efforts of EXL’s SaaS solution suite and advanced analytics within the US Life Science vertical. He has more than 15 years of sales, marketing and technology experience, with particular expertise in quantitative analytics and SaaS solutions for Life Sciences.
Prior to joining EXL, John was SVP Sales & Marketing for Optivara where he positioned the organization around a SaaS based model collecting and analyzing information around the future state of a therapeutic area. Prior to Optivara John was SVP Sales for SteepRock, who pioneered the Opinion Leader Management System for Global Medical Affairs teams. Prior to SteepRock, John was with Qforma, Inc., a provider of advanced analytics for the health sciences industry, and was instrumental in Qforma’s impressive market success which positioned it for successful sale in 2013.
John holds a BSBA with concentration in Accounting from Suffolk University. While always willing to talk about EXL’s industry changing solutions, John also enjoys coaching youth hockey and is an avid fan of the many great New England professional sports teams.