Real-world data and the patient journey

The RWD Win/Win: A Strategy that Delivers for Pharma, Patients, HCPs and More

Avatar for Mike Rousselle

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VP Data Product
OptimizeRx Corporation

Over the past decade, pharma has made significant investments in real-world data (RWD) and in analytics/insights generated from that data -- real-world evidence (RWE). At OptimizeRx, we’re applying this methodology across the full patient journey and product lifecycle, because we see RWD/RWE as some of the most integral inputs in a variety of patient-centric activities. RWD/RWE are becoming the go-to resource/method for identifying current gaps in care, designing interventional and observation studies, negotiating with regulatory agencies and payers, and engaging physicians about therapy options. 

I recently had the opportunity to join leaders in RWE from Janssen, Merck and ASC Therapeutics for a panel at the 2nd Real World Evidence and Market Access Symposium. While each of us focuses on a different area of the patient and product lifecycle, we all share a common goal – designing RWD/RWE programs that deliver a “win/win” for the relevant stakeholders, and ultimately improve patient choices and outcomes. 

Credibility From Transparency and Accuracy  


A win/win RWE strategy for OptimizeRx is one that increases the relevance, timeliness, and accuracy of the communications we deliver to HCPs. But RWE is used in diverse ways across the industry – for example, observational data that supports single-arm studies, or insight on improved patient outcomes that supports favorable formulary positioning. Yet in all these cases, what makes a true win/win is when
all stakeholders are getting value from the data – which means the data and insights must be both highly credible and mutually accepted. 

While the nature of what my fellow panelists and I are trying to achieve varies, we all agreed there were two key components in building credibility: transparency and accuracy. 

  • Transparency: Healthcare is a nuanced field with countless intricacies, which is why transparency of both the source data and processing algorithms is a key element in building credibility. Especially when applying an AI solution to real-world datasets, it’s critical to avoid a “black box” approach, and to be transparent about both the positives and drawbacks of our chosen methodology. 
  • Accuracy: RWD/RWE solutions are often used to de-risk decisions, and that means credibility also comes from the accuracy of their predictive power. While there are quantitative ways to demonstrate predictive accuracy (i.e. common model evaluation metrics like Precision, Recall, AUC, etc.), there are also important qualitative considerations that can’t be ignored. The recommendations and predictions from RWE are most accepted when they resonate with our experiences or expectations. This does not mean that models must always confirm our prior beliefs in order to be credible; but models must always yield some explanatory capability so they are not pigeon-holed into the black-box mindset.

A true RWD win/win occurs when all stakeholders get value and insight from the data, which means we need to work together to build credible, accurate, and mutually accepted solutions. 

 

Building Your Win/Win: Challenges and Opportunities 


Despite the strides our industry has made to build credibility for RWD/RWE solutions and insights, there are still challenges to overcome for wider acceptance:
 

  • Perception vs. reality of value: While many industry stakeholders claim to value evidence drawn from RWD, the reality is that they are often highly critical or skeptical of claims made based on the data.  
  • Lack of a “gold standard”: As a younger field in healthcare, there’s no single RWD/RWE model that’ is seen as the industry-wide gold standard, which means we don’t have an external benchmark we can use to assess our strategies. 
  • Data fragmentation and volume: The fragmented nature of our healthcare systems means that the data we seek is often split across multiple datasets. Alternatively, smaller populations in certain disease areas or for certain therapies limit the overall data available to us. 

As our panel discussed how to overcome these challenges, two key themes emerged from the conversation: the importance of stakeholder dialogue and education, and the need for improved data connectively.  

  • Stakeholder dialogue and education take many forms, whether that is earlier collaboration with regulatory bodies, internal knowledge-sharing within a pharma organization, cross-functional committees to build best practices, or even cross-industry discussions between companies. But the goal is one and the same – to develop a mutually agreed-upon, repeatable, and actionable framework for using RWD/RWE, from which credible, accepted insights can be drawn and applied. 
  • Data connectivity also requires a collaborative approach – whether the goal is integrating multiple data sets for a full picture of the patient journey, exploring solutions like federated hospital data networks, or investing in wider data-sharing infrastructure. Furthermore, as with RWD more generally, connecting and cleaning data for use must be transparent, accurate and credible. 

We’re just at the forefront of RWD/RWE, and what makes this technology so exciting is not just its potential, but also how we can work together to solve its challenges. As both our technology resources and human brainpower grow more sophisticated, we’ll find new ways to connect the dots across the healthcare landscape. I, for one, can’t wait to see how we realize that potential. 


VP Data Product
OptimizeRx Corporation