HEALTH PLAN IQ

AUTHORS


Florent_Moise_headshot_small
Florent Moise 
Principal,
ZS

Peter_Manoogian_headshot_small
Peter Manoogian
Principal,
ZS

Jeff_Traenkner_headshot_small
Jeff Traenkner 
Strategy Insights & Planning Manager,
ZS

Colin_Russi_headshot_small
Colin Russi 
Strategy Insights & Planning Manager,
ZS

Shreesh_ Tiwari_headshot_small
Shreesh Tiwari 
Principal,
ZS

Harbinder_Raina_headshot_small
Harbinder Raina 
Associate Principal,
ZS

Latest Posts

Creating Value for Health Plans and Pharma Manufacturers Through Real-World Evidence

Posted by Colin Russi on Wed, Sep 25, 2019

3721_3685_HEOR_BlogA familiar narrative plays out with every product launch: Company X has brought a drug to market that offers any number of advantages over the current standard of care. It’s got FDA approval, and the clinical trials have resulted in promising outcomes, with ample data to support the drug’s benefits and a price tag to match these promised benefits. In this post, we’ll take a closer look at how health plans can utilize real-world data to derive the insights needed to validate or challenge the results of these initial trials, and to create effective interventions to help influence provider pharmaceutical prescribing behavior. When designed correctly, these interventions can help achieve a health plan’s vision of higher-value care through a combination of reduced cost and improved patient outcomes. 

Shortcomings of Clinical Trials

Clinical trials provide a peer-reviewed source of important information on drug safety and efficacy. In fact, upon a drug receiving FDA approval, the clinical trials will likely be the only source for clinical evidence. At one time, this evidence may have been good enough for a physician to see the value in making treatment changes. But now, faced with mounting pressures around cost of treatment to patients, access and litigation prevention, health plans are compelled to look at clinical trial data alone with a healthy dose of skepticism.

There are two primary reasons for this skepticism. First, as the FDA continues to back away from funding clinical trials, manufacturers themselves have taken up the role of paying for these trials. This situation results in the perception (and sometimes the reality) that trial selection is tightly controlled to ensure that the results support the product narrative. Without additional data for verification, study results can therefore seem unreliable.

Second, clinical trials are limited in their scope. Predominantly conducted under ideal conditions, they don’t necessarily represent real-world effectiveness. For example, the number of therapy combinations tested is limited, and trials are often limited to short-term impact. Additionally, the trial population is limited and not always sufficient to observe differences in efficacy due to comorbidities, gender, race, age and other factors. Research published in 2018 states “a disadvantage is that participants may fail to reflect the actual clinical site sufficiently to represent the entire population.”

What health plans need, then, are broader and more meaningful approaches for assessing drug value so that health plans can more effectively derive the insights needed to create effective interventions to help influence provider pharmaceutical prescribing behavior.

Potential Solution Through Innovations in Real-World Evidence

Physician behavior is driven by a mix of an individual’s experience and the evidence that they are exposed to. Health plans have an opportunity to influence this latter component. Put simply, “The growing industry need for broader information on real-world effectiveness and safety—both of which will impact the eventual reimbursement and utilization of new products—is driven by regulators, public and private payers, and prescribers, all of whom seek to better understand the impact of a new product in a real-world setting” (emphasis mine). We need to look to real-world evidence to address this need. 

Fortunately, recent innovations have begun to close the evidence gap. Data science has advanced rapidly, developing new techniques for tackling large and complex data sets (such as advancements in AI, natural language processing/BERT, etc.), becoming more accepted in the medical community and becoming a career choice for many, creating a rich talent pool.

The impact of these advancements has been powerful. Overall, data richness has increased with the ability to link claims with electronic medical records and social determinants of health data. In addition, data providers now offer extremely large patient data sets. As a result, small and medium plans could now have access to the same data quantity that historically was reserved for only the largest of health plans. Moreover, infrastructure and tools are now available to consume and derive insights from these large data sets. For example, AWS provides low-cost and scalable cloud storage and processing capability.

These advances in data technology allow health plans, for the first time, to conduct meaningful and cost-effective RWE studies. Leveraging these studies, plans can:

  • Measure the real-world value of a drug, including elements such as reduced hospitalizations, increased adherence, etc. Leveraging this “total value generated” can help health plans prioritize which drugs are classified as “preferred” as well as inform interventions designed to influence provider prescribing behavior towards these preferred drugs.
  • Design value-based reimbursement plans. RWE can provide the evidence necessary to quantify drug value and thus assist in the design of value-based reimbursement strategies between health plans and pharmaceutical manufacturers. Further, the availability of third-party data would allow for independent evaluation of the drug efficacy per the metrics established in the contract, thus providing a trusted source of truth for calculating VBR payments.
  • Determine the relative effectiveness of poly-therapies. A recent ZS study utilizing data from Symphony Health Solutions showed that Type 2 diabetic patients who added an SGLT2 or GLP-1 on top of metformin saw a more significant decline in HbA1c as compared to patients who added DPP4 to metformin. Therefore, as a health plan it would likely be beneficial to establish programs to encourage HCPs to prescribe SGLT2 or GLP-1 as opposed to DPP4 to a patient’s metformin regimen.
  • Predict patient outcomes and behavior. Using the available social determinants of health, EMR and clinical data, health plans can predict a specific patient’s progression on therapy and assist providers in the selection of drugs that have been shown to generate the most value for patients whose profiles and disease state is similar.

A Vision of the (Near) Future State

ZS predicts that RWE will be increasingly impactful for a health plan’s business and the development of advanced RWE capabilities utilizing internal and external data, and partners will be a source of competitive advantage for plans. However, while the data and techniques now exist to meet the health plan’s needs, the challenge for health plans will be in developing a data science team (either internally or externally) that can effectively utilize this new data, apply statistical techniques and, most importantly, be able to leverage a deep understanding of U.S. healthcare so that insights generated (similar to the examples above) are meaningful and actionable.


RELATED CONTENT 

BLOG POST: The Three Value-Based Reimbursement Design Flaws That Frustrate Providers the Most

BLOG POST: How Providers Experience Value-Based Reimbursement, and What It Means for Health Plans


 

Topics: Analytics

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