AI: Leveraging Wearables and Other Patient-Generated Data in Research

Luca Foschini, PhD, is chief data scientist and co-founder at Evidation Health. In a recent interview he discussed how artificial intelligence interacts with patient-generated data to create advancements in clinical studies for pharmaceutical and biotech companies.

Smartphones and smartwatches are used daily around the world to collect data on various health statistics like heart rate, blood pressure, and use of medication. Foschini described this as an advancement for patient health, considering that “most people, if they are lucky, spend less than 1% of their time visiting a doctor.” Patient-generated health data helps develop a more realistic representation of health indicators like blood pressure, which may spike when a patient visits the clinic, for example.

Foschini said that patient-generated data can be hard to interpret at times, but there are ways to work around that issue. This is where AI comes in, scouring over long streams of patient data to find anomalies. Machine learning can detect deviations from the standard pattern and provide an alert. He also says that asking patients for context regarding their data is a great way to determine why an outlier is visible, and what it means.

Evidation is a company that connects individual patients who are looking to share their health data with organizations. Its platform promotes full transparency about the destination of consumers’ data and how it’s used. Evidation’s Achievement app has multiple forms of engagement available—some for consumers like rewards for healthy behavior, and others for enterprises joining research studies.

There are a lot of possibilities for programs like the one provided by Evidation. Foschini gave an example of one in which the company alerted participants who had developed flu-like symptoms and informed them of a clinical trial for a new flu treatment.

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