Linking Real-World Data is Like Making a Fine, Blended Wine

In the healthcare and life sciences industries, developments like the explosion of real-world data, new regulatory programs and frameworks, and a focus on AI and machine learning are fueling a transformation. Our podcast series, “Between Two Scientists,” explores why these changes are happening, and how the industry can respond, by interviewing experts in the field.

For our first podcast, our Executive Director of Solutions, Dr. Meg Richards, chatted with Panalgo’s Erik Maul about why the right blend of linked data is like a fine wine, and how it can help companies conduct the analyses they need to plan for and adjust to shifts in the healthcare landscape.

Like crafting a sophisticated Bordeaux or Merlot, linking data is about finding the right “grapes” and “flavor notes” to create a pairing that optimizes or enhances your analysis. It means looking at the pros and cons of the data sets, what gaps exist in the data, and what variables are critical to answer your research questions. For example, you need body mass index (BMI) or waist circumference to study an overweight or obese patient population. BMI and waist circumference will only be present in an electronic medical records (EMR) database, and although you could use the EMR alone, if you also want financial information from claims, you’ll need to create a linked EHR-claims dataset.

Linking data is a complicated process, and blending the right datasets together in the right way is crucial. In so doing, your company can create strategies to adjust to the ever-shifting healthcare and life sciences landscape. Moreover, protecting patient privacy via expert determination of the linked data is an imperative final step before the linked data can be interrogated (or shall we say, “before the wine can be sipped”?).

Check out the “Between Two Scientists” podcast to hear Erik explain the data linkage process, and to find out more about how we can help you link and analyze real-world data, contact us.