By Manish Sharma and Sandeep Joon
The 21st Century Cures Act (the Act) – and its $6.3 billion in funding provisions – is said to be the most important legislation passed since the Affordable Care Act (ACA). This sweeping piece of legislation has a broad set of goals intended to stimulate innovation that will ultimately improve patient care outcomes. The pharmaceutical industry is a key focus area of the law, with provisions that promise to modernize clinical trials and streamline new drug approvals. Of keen interest is the Act’s specific provisions meant to encourage the use of Real-World Evidence (RWE). But what’s the best approach for this relatively new arena of Big Data and analytics?
Data gathering technology that enables RWE has exploded in recent years, hence its inclusion in the Act. According to the Act, RWE is defined as “data regarding the usage, or the potential benefits or risks, of a drug derived from sources other than randomized clinical trials…The sources of real world evidence, including ongoing safety surveillance, observational studies, registries, claims, and patient-centered outcomes research activities.” In other words, there are vast amounts of tremendously valuable data to the effective development and ongoing management of new medications that fall far outside tightly controlled clinical trials. This includes data in disparate formats currently housed in electronic medical records (EMRs), disease registries, public health records, claims and patient wearable devices.
Pharmaceutical companies see the enormous potential for RWE to provide much-needed data and insight for analyzing and comparing the effectiveness of drugs. Other possible insights include additional uses for specific drugs, identifying and monitoring side effects and adverse drug events, making appropriate pricing decisions and expanding into or targeting specific markets. But what is the best way for companies to take advantage of RWE – take a narrow, case-by-case functional approach or implement a broader enterprise strategy?
Functional Focus: A Deeper Dive
Because the tools to use RWE are still evolving, many organizations have employed a use case specific approach, building a specific application for each new project. While each project may achieve its goals at a reasonable cost for tapping, analyzing and applying RWE for a specific use case, there are significant challenges from a broader perspective.
The data collected through clinical trials is managed by very strict protocols, so data is consistent and the quality is very high. RWE is quite different. It exists in many formats – codified data, images, and free text – comes from many different sources, and may be purchased or accessed by subscription. Therefore, quality widely varies.
Additionally, there are further challenges when groups within a company may be accessing the same data, but from a siloed point of view. Without enterprisewide collaboration around the purchase, management, and sharing of data as well as the insights gained, opportunities will be missed and costs will rise.
Enterprise Strategy: A Deeper Dive
Understanding that most organizations have just scratched the surface of the impact RWE will have on their business, many are taking an enterprisewide platform approach. By centralizing data management, storage, access, and analytics, new benefits emerge:
- Data sets can be used and reused across many projects for different use cases while fees and subscriptions are managed centrally.
- The framework and processes for data ingestion, processing and quality assurance can be standardized and optimized.
- Tools for advanced analysis, such as Natural Language Processing (NLP), can be used by multiple groups.
- Tools for communication and collaboration can make each project’s results, publications, guidance and insights available to other groups to be applied in new ways on future projects.
Time and Cost Savings Multiply Over Time
While a functional or use case based approach initially appears to cost less, the benefits of a broader enterprise platform strategy multiply over time. A platform can be built out and expanded incrementally. For example, starting with a limited number of use cases, the organization can build out the foundation for data ingestion, processing and quality assurance along with the presentation, visualization and analytics capabilities. Over time, new data and new tools can be incorporated into the existing framework to address new use cases – without duplicating ramp-up time and costs.
The benefit becomes especially clear when organizations consider RWE data from multiple sources in multiple countries. For example, if the use case calls for an observational study in Europe, the data may be in multiple disparate formats and text-based data may be in different languages. With a platform approach, the data ingestion framework is in place and can be more efficiently extended to support localization without starting from scratch. The return on the initial investment grows exponentially as use cases are added and results are shared and reapplied across the organization.
Building an RWE Platform – Things to Keep in Mind
First and foremost, avoid undertaking RWE projects in isolation. Many organizations choose to partner with a company with specialized expertise in clinical data analytics and RWE. With that guidance, it is best to take an organization-wide view to determine a long-term strategy supported by short-term, incremental goals. In other words, begin with the end in mind – what is the vision for applying RWE, what data sources will be required in the short and long term, and who will be the ultimate consumers of the information?
It is important to be certain the data sources of interest include the right data elements. Start with a variety of research questions and work backwards to be sure the right data has been identified. It is also important to work with partners who are “data agnostic,” so as not to be limited to one data set or one data format. Because RWE data varies so widely, organizations need to be able to manage and analyze data from many sources.
With all that in mind, there is no need to undertake a huge implementation all at once. Rather, select one or two high-value use cases as the springboard for building the minimum viable solution for using RWE across the organization. From there, add projects, data sources and analytics tools incrementally. Although the initial cost may be marginally higher, the cost impact will decrease over time as more use cases are added and economies of scale are realized. In the growing field of RWE, there is no time like the present to get started.
Manish Sharma is the Senior Vice President, Life Sciences at CitiusTech.
Sandeep Joon is the Assistant Vice President of Consulting, Life Sciences at CitiusTech.