Five Characteristics You Should Consider When Evaluating the Right Kind of Real-World Data

Real-world data, or RWD, is gaining traction throughout the health care industry as it offers tangible benefits to all stakeholders.  

1. The United States Food and Drug Administration (FDA) incentivizes life sciences organizations to use RWD as a complement to clinical trials (e.g., with external control groups), to identify gaps in care and can potentially market the medicines most likely to fill them.
   
   

2. Providers, including specialist oncology practices, need RWD at the point of care to create precision medicine solutions for individual patient cases, drawing from data concerning patient populations, biomarkers, comorbidities, cancer variations, and more.

3. RWD from different parts of any specialty organization can be collected and interpreted into big-picture information needed to manage costs, engage patients, and improve health outcomes. Congress has also mandated increased use of RWD to inform new health care regulations.

The value of RWD is clear, but not all data is equally useful. The FDA defines RWD as “data regarding the usage, or potential benefits or risks, of a drug derived from sources other than traditional clinical trials.” That can encompass a broad scope of data, and you want to focus on the types relevant to your specific goals. With that in mind, here are five factors to keep in mind when evaluating RWD:  

1. Multiple sources

Research has shown that different RWD sources have distinct strengths and limitations when applied to different purposes or specialties. For example, RWD for oncology involves layering sources such as electronic health records (EHRs), administrative claims data, and cancer registries to get a clear picture of how a particular treatment has performed in the real world. 

2. Structured and unstructured

EHRs are an example of structured data, easily machine-readable in database format, but most of the world’s data is unstructured; think of all the different media files that make up the notations, imaging scans, claims documentation, etc., handled by various stakeholders. To truly understand the real-world impact of treatment and make predictions on its future, it is crucial to organize and analyze unstructured data. AI-powered tools using natural language processing can make this feasible in a faster time frame.

3. Integrated

Simply considering data from different sources is not enough. RWD must be amassed and displayed to make informed decisions using sophisticated data processing and analysis tools that can interpret relationships between datasets. A dedicated healthcare analytics platform is ideal for this process, preferably one aligned with your specialty, and can provide various and detailed visualizations of aggregate data.

4. Accessible

Often, access to data is constrained to a silo based on authorizations, technology platform access, or a single department in a healthcare organization. But imagine how much more useful it could be as part of a multiple-source, integrated approach across all parts of the organization. You can easily make your RWD accessible to all relevant stakeholders via a specialized health care data management platform, ensuring compliance with HIPAA (The Health Insurance Portability and Accountability Act) security and privacy regulations.

5. Complete

Use your RWD to find patterns in data and gaps that need closing. For example, is your oncology practice accounting for equity in access to healthcare or the underreporting of subjective patient symptoms? Incomplete data can mean missing key valuable insights or drawing mistaken conclusions. Select a data system that can produce a complete picture of what’s working and what can be improved.