Sharecare Develops New Crowdsourced Machine Learning Model to Predict Allergy Symptom Burden

• Results From Study Conducted Through Omix Platform Present Opportunity for Clinicians to Better Predict and Prevent Environmental Allergy Symptoms

What if you could develop a forecast for your allergies, just as you check the weather on your phone before heading out the door? While there may not be an app for that yet, new research from Sharecare, the digital health company that helps people manage all their health in one place, lays the groundwork for future efforts to map when allergies might flare up so individuals can live happier, more productive lives. The study, originated by doc.ai prior to its acquisition by Sharecare, was recently published in the Journal of Asthma and Allergy.

Using Omix, a proprietary digital clinical research platform powering mobile research studies, the Sharecare team developed and trained a machine-learning algorithm to predict the emergence and severity of symptoms related to allergic rhinitis. Commonly known as environmental allergies, allergic rhinitis is responsible for nearly 15 million clinic visits, 3.5 million days of missed work, and $24.8 billion in direct costs incurred in the U.S. every year.

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Sharecare’s study enrolled more than 2,000 participants from across the U.S. to gather real-world symptom and environmental data, such as geo-coded pollen counts. Smartphone sensor data including daily physical activity and geolocation were collected, and participants logged their symptoms in an e-diary. Using these inputs, the researchers’ machine-learning algorithm was able to predict participants’ allergy burden with greater than 80% accuracy. The study findings augment traditional allergy treatment regimens, which focus on avoiding triggers and can add tailored actionable insights that are meaningful to an individual in their unique environment.

“To date, machine learning models in healthcare have focused on clinicians with decision support or administrators with forecasting, rather than emphasizing patient-facing direct care models,” said Nirav R. Shah, MD, MPH, chief medical officer of Sharecare. “When predictive models like those in our study are capable of real-time personalized predictors and offer a strategy for tailored clinical care and prevention, they will be integrated into care delivery.”

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“Today, anyone with a smartphone can be a meaningful contributor to important research,” said Sam De Brouwer, chief strategy officer at Sharecare. “Sharecare’s Omix allergy study has opened up a secure way to contribute to preventive relief for high-risk allergic rhinitis patients. Following additional clinical external validation, this model could be packaged into consumer offerings providing allergy sufferers with real-time personalized recommendations for their symptoms that are refined on an ongoing basis as more people o***** to sharing their data.”

Digital health studies conducted through Omix use all smartphone capabilities – including voice, photos, video, and text – to capture health data seamlessly with user permission. The participant-centric approach of Omix empowers individuals by providing easy data capture and tracking, points redeemable in the Omix marketplace, and an individualized end-of-study report.

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