Rethinking the Use of Real-World Evidence to Improve Policy Decisions

Randomized controlled trials (RCT) remain the gold standard for evaluating the safety and efficacy of drugs before making them available to patients. However, drug development is increasingly shifting towards specialty medications with narrow indications and smaller patient populations. Under these conditions, clinical trials may not be able to recruit enough patients and can take too long to complete.

Observational studies using real-world evidence (RWE) provide significant opportunities to gain insight into treatment patterns and outcomes in clinical practice. RWE is healthcare data collected outside of formal clinical trials, including electronic health records (EHRs), billing and claims databases, and disease registries. RWE complements clinical trials by capturing data on the day-to-day usefulness or performance of drugs that can be used to support both regulatory and policy decisions.

The FDA has issued guidance describing how RWE may be used to support a variety of FDA regulatory decisions. This guidance has opened the door for manufacturers to utilize RWE to conduct pre-approval and post-approval studies, allowing researchers to discover patterns that may not be visible in smaller sample sizes. This is critical to help support new indications for medicines and improve patient care.

Gathering New Data Points to Answer Questions

The growing adoption of RWE in the regulatory process presents an exciting opportunity to rethink how we use data and clinical end points to make policy decisions. Payers and other healthcare professionals have experience with real-world data, having long used claims data such as adherence, persistence, and time on treatment, to make decisions about access, coverage, reimbursement and formulary placement for treatments. While this data provides relevant information, they do not represent true clinical outcomes. For example, a patient may be 100% adherent to her diabetes medication, but her A1C keeps going up because she should be on insulin. In this case, adherence does not always equal desired clinical outcome.

In order to demonstrate how a drug may impact patients and healthcare costs in real-world settings, we have to be able to answer such questions as “How does one drug perform versus another in terms of treating a particular condition?” or “How does one drug perform against previously approved medications?” or “What are the observed benefits of the medication as it is used by doctors and patients in real clinical settings?”

Standard Data Points Provide Perspective

However, meaningful outcomes information needed to answer these questions is not available solely from claims data. As an industry, we have the opportunity to establish a standard set of data points with common definitions that are critical to applying the benefits of RWE to value-based decisions. A number of key clinical data points that will provide a better understanding of the impact on patient populations and result in better, more informed payment or formulary decisions are:

• Disease staging
• Histology
• Biomarker testing and results
• Line of therapy
• Performance status

While payers typically don’t have this type of clinical data, it is readily available through robust EHRs like McKesson’s iKnowMed℠ oncology EHR, which provides real-time tracking of clinical and reimbursement data. With the ability to track more patients over a longer period of time, RWE can help provide a more comprehensive view of patient response to medications that can be used to inform clinical decisions, as well as economic analyses. Following are examples of how adding these clinical data points can provide perspective on reimbursement data without having to infer based on incomplete or ambiguous results:

Segmenting Patient Populations: There are significant drug development efforts associated with non-small cell lung cancer (NSCLC); however, there is no International Classification of Diseases, Tenth Revision code for NSCLC. It shares a code with SCLC. With claims data only, it is difficult to segment between the 2 patient populations, which can have a significant impact on how payers understand treatment effectiveness or make decisions about access. Disease staging can also have significant impact on treatment decisions. For example, a patient diagnosed at stage 2 who has progressed to stage 4 is very different than a patient diagnosed at stage 4.

Drug Performance: If a new drug adds 18 months of progression-free survival over the existing drug on the market, it’s an easy decision to make changes to coverage or formulary policies. However, if that performance improvement is only one month, was it due to the drug or were there environmental factors that impacted the effectiveness of the treatment?

There is increasing interest among payers and biopharma companies in value-based reimbursement, where coverage and reimbursement levels are linked to the performance of the drug. For this model to be effective, payers must have the ability to monitor performance and ensure that reimbursement levels are correct based on actual outcomes matching expected outcomes.

Better Data Equals Better Care

Ultimately, the ability to blend claims and reimbursement data with clinical evidence will provide better understanding of the utilization and effectiveness of medications in the real world. With greater perspective, payers and healthcare executives can make more informed decisions about access and formulary inclusion, which will help improve patient outcomes, especially when it comes to chronic or rare disease states.