What really happens to your patient data? A chief medical officer explains

We receive patient-level data, which is data about an individual person who’s using one of our medications, and we also get aggregated data, which is a consolidation of data related to groups of people who use our treatments. It comes from insurance providers, as well as information that gets entered into electronic health records when people visit healthcare providers, like physicians and nurse practitioners.

Patient privacy is of utmost importance to us, which is why the data we receive are de-identified so we can’t see anything that would allow someone to recognize the person. Sometimes we see ages or a zip code, but there’s never a name, date of birth or Social Security number. We can’t and don’t try to identify people from the data. For us, it’s literally just information.

We look at data to learn how our products are performing: Are people benefiting as much as we saw in clinical trials? Are there patient populations who are benefiting even more, or less, than we expected? Are there side effects that we didn’t know about but are now seeing that we need to alert physicians about?

We also do what we call predictive modeling, which is trying to understand if some patients are responding the best or benefiting the most, and which groups might or might not be developing more side effects with the medicine. Real-world data can help us better predict outcomes for individual patients, thereby maximizing the benefits of a therapy and minimizing the risks.

We rely on clinical trials for information before products are approved, but we rely on data after a product is approved to really understand its benefits in the “real world.” The data are complementary.

You might have hundreds or thousands of participants in a clinical trial. Real-world data, on the other hand, could come from millions of people. Also, when we discuss results from clinical studies, we talk about averages—the average treatment response or the percentage of patients who had something happen, for instance. However, the world is not really about averages, right? The average height could be 5’6”, but that could mean you have a 5-foot person and a 6-foot person in the study.

That’s the reason we do predictive modeling—to try to go beyond just averages. With big data, you can do predictive modeling with even more power because you can look at many more people with different backgrounds and baseline risk factors. This then enables you to look at the effectiveness and the safety of products among a diverse population.

The more real-world data we collect, the better we can arm physicians with knowledge about people who are just like the person sitting in front of them.

Say you have a history of diabetes or cardiovascular disease, for instance, and we find out through predictive modeling that people with those histories respond to a certain treatment differently from people who don’t have those risk factors.

Instead of telling you, “the average patient has a response of this,” your doctor would be able to say that for a patient like you, real-world evidence shows you personally have a better chance of response to a medication, or a higher chance of a side effect. That means you both can better understand the potential risks and benefits and tailor treatment appropriately.

We learn what we can from our own data, but there’s a lot more that could potentially be understood about it. So rather than keep it to ourselves, we share it with other scientists through YODA in case there are other important medical questions that it can answer. By sharing, we want to do our part to not only help improve health outcomes for the greatest number of people, but also pave the way for better healthcare.

For example, a recent study using our data allowed outside researchers to look at gender differences in weight gain in patients with inflammatory bowel disease treated with one of our medications. Another study conducted by the World Health Organization with our data compared different therapies used to treat multidrug-resistant tuberculosis, including one we produce, to inform global treatment guidelines.

Our ultimate goal—both with big data and in everything we do at Johnson & Johnson—is to expand our understanding of diseases and advance treatments and cures that can change the lives of people around the world. Sharing clinical trial data is one important way we can work toward that mission.