Defining, governing and analyzing outcome-based contracts
Defining, governing and analyzing outcome-based contracts
Defining, governing and analyzing outcome-based contracts
How payers and life sciences organizations can improve patient care and bottom-line performance together
As the cost of healthcare continues to rise, Outcome-Based Contracts (OBC) are gaining momentum in the industry, especially in the area of pharmaceutical drugs. OBC’s, a type of Value Base Contract (VBC), are essentially risk-sharing agreements between the manufacturer and the payer in which the reimbursement for a drug is based on its observed outcomes in a real-world population. Health plans, consumers and the government are all seeking validation of value in healthcare spending. And, as the effects of the COVID-19 pandemic continue to play out, the trend toward increased transparency, patient engagement and provider accountability is driving many benefits managers to negotiate pricing based on measurable outcomes.
According one recent report, 59 percent of payers executed an OBC in 2019, compared to 24 percent just two years earlier. 1 The same report stated that 31 percent of plans reportedly have more than five OBCs in place, up from 12 percent over the same period. Indeed, 60 percent of all payers using OBCs cited “cost savings and clinical improvement” as the top reasons for having OBCs.
The industry’s emphasis on cost control and clinical improvement are only expected to grow more intense as the decade unfolds. Fortune Business Insights™ predicts that the prescription drug market will reach 1.56 trillion by 2026, exhibiting a compound annual growth rate of 8.9 percent in that time. 2 With approximately 75 percent of national healthcare spending covered by insurers in the U.S, the true cost of healthcare is hidden from consumers, causing higher-than-optimal levels of use and waste – and a growing need for accountability among contracting partners. 3
1 “More than Half of Health Plans Use Outcomes-Based Contracts,” by Alavere Health, Oct. 1, 2019.
2 “Prescription Drugs Market is Expected to Grow with a CAGR of 8.9% Between 2020 and 2026,” by Fortune Business Insights™, July 20, 2021.
3 “The Why, What, Where, and How of Value-Based Contracts” by Patrick V. Bailey, MD, MLS, FACS, June 3, 2021.
Outcome-based contracts defined
To reiterate, OBCs represent an agreement between a payer and drug manufacturer, wherein the payer pays different prices for the same drug depending on how well that drug performs in real-world patients, with net payments being significantly lower (or even zero) for patients having poor outcomes. In an OBC, the purchasing price of the drug is no longer related to volumes, but instead on the drug’s ability to improve the health of the patient. There are many types of OBCs, but generally, the payer and drug manufacturer will contract on the initial cost of the drug. The drug manufacturer will also commit to a health outcome measurement as part of the contract (e.g., reduces the number of times the patient visits the hospital by 25%). The contract will then stipulate discounts that the payer will receive in the form of rebates if the health outcome measurement is not met.
What’s at stake
Under an OBC, life sciences organizations must assume 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.
This 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 a 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 an outcomes-based contract. And, fulfilling the metrics leads to a conversation about who is the next group that can be impacted.
The only question then is, can the life sciences organization trust the outcomes they are being shown? This is where it helps to have the analytics performed and verified by an objective, third-party expert. If the payer 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 trustworthy.
A foundation for success
In light of the challenges, there are several key success factors that should be considered foundational in creating useful and effective OBCs. They are:
Commitment and trust
Both parties must commit time and resources to the design, implementation, and monitoring of the OBCs. With both parties duly vested in the process, having an experienced, third-party analytics team creating the patient model removes any doubt of either side being “self-fulfilling.”
Clearly defined metrics
Both parties must agree to the outcomes or “endpoints” that will be monitored over time, as well as what constitutes an “acceptable” level of success.
Selection of conditions and products
Keep in mind that while it can be tempting to go after big, complex issues at the start, it is best to keep things simple to begin. Choose products where the outcomes aren’t so dependent on moving parts or complex interactions to increase your chances of success. Once both sides have some experience with OBCs, you can then move into large, more complicated issues together.
Choose drugs or devices that lend themselves more readily to OBCs. Not all conditions or products are good options. Rare diseases, for example, are typically more difficult to manage; focus instead on conditions and products that present easily obtained endpoints within a specific, reasonable timeframe and can be objectively measured quantitatively rather than subjectively.
So much focus in healthcare is on patients visiting physicians, however, today, we can gather all types of data, including clinical, medical and pharmacy claims, laboratory, socioeconomic, demographic, behavioral, and even consumer-generated information from shared and stored data sources. 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 only the proverbial tip of the iceberg. Many other pieces of useful information in a provider’s patient panel or a payer’s member database can be found below the surface, presenting a clearer picture of what can make or break 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 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 these groups in 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 simply bring appropriate patients to the surface. It also uses data to determine which of these undiagnosed, high-risk patients have the highest chance to succeed in the program – 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).
Work the numbers
With all 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 OBC founded on realistic expectations and quantifiable results.
When OBCs are constructed on a solid foundation – and there is reliable data available on which to gauge success – 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 stand the greatest probability of benefiting from the treatment.
Through 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, OBCs tie the price being paid for drugs or device to an actual, measurable value, with risk being spread across both parties.
Patients also benefit by staying healthier because of the 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 is good news to patients. According to the Centers of Medicare and Medicaid Services, out-of-pocket spending for healthcare by US consumers grew to $406.5 billion in 2019, an increase of 4.6 percent 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 into 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.
Use case: big pharma
EXL recently developed a proof of concept using benchmark medical and pharmacy claims data to demonstrate an OBC grounded in facts, focused on results and crafted to benefit both parties and patients served. The drug featured in this case was a medicine for patients suffering from moderate to severe rheumatoid arthritis (RA). EXL’s approach was to:
- Define the RA and product-level patient cohorts
- Risk-stratify the patient populations
- Calculate the health outcome metrics
- Develop a statistical model to define the composite patient score
- Segment the patient population by risk and outcomes
- Apply financial implications based on outcomes
EXL used patient-level, benchmark medical and pharmacy claims data to establish the basis for the model, spanning a 12-month timeline.
Several clinical measures, including drug compliance, drug switching, RA therapy escalation, steroid interventions, emergency room events and inpatients events, were calculated into a single patient outcome composite score. Four financial impact scenarios were presented, covering variable rates of success to illustrate the payback or lack thereof depending on the program’s performance.
Based on predictive analytics, these scenarios showed payouts ranging from $16,488,890 to the manufacturer for “success” (as agreed to by the parties), to a savings of $10,998,613 to the payor for not meeting the program goals.
Defining, monitoring and analyzing OBCs requires ready access to reliable data, careful attention to details, and a broad view of the case landscape to ensure mutual acceptance of terms and maximum chance for success. EXL has the experience and capabilities to assist.
Acting as an independent third party to define patient health outcomes, we can stand in the gap as an objective arbitrator to ensure that the interests of both parties are clearly communicated and honored. Our internal benchmark databases can be used to compare patient populations to a payer’s member population and in crafting contract designs. Once a contract is signed, we can further perform and verify ongoing outcomes with trust, providing transparency to both organizations. What’s more, we can uncover additional insights as part of our analysis, such as understanding patient drug adherence and switching, through predictive patient analytics. In short, we have the experience, tools and capabilities to shorten your timeline to deriving health outcomes, thereby accelerating results.
To learn more about our process, please contact EXLservice.com