The news: Mayo Clinic and Mercy announced a 10-year agreement to analyze a treasure trove of de-identified patient data to boost early disease detection.

The partnership will power personalized, predictive medicine by using data analytics to predict disease outcomes and form the best treatment plan for individual patients.

The problem: As early adopters of electronic health records (EHRs), Mayo and Mercy have collected a long history of treatment outcomes and clinical data.

However, healthcare providers struggle to manage unstructured data like lab reports, medical images, and physician clinical notes to make informed clinical decisions.

The solution: Cloud-based AI and machine learning (ML) tools are making data mining through EHRs possible, while allowing doctors to spot disease patterns earlier and plan the best patient treatment.

• Mayo and Mercy physicians will use ML algorithms trained on their treasure trove of data to decide the best treatment for patients.
• Mayo’s data science expertise comes together with Mercy’s expertise in care delivery in diverse communities and its deep clinical data. Mercy aims to analyze data patterns from more than 500 million de-identified patient interactions.
• Similarly, last year AdventHealth and AI analytics company Sema4 launched a data-driven precision medicine program to better detect, treat, and prevent diseases in patients.
• Meanwhile, pharma companies like Pfizer use the Truveta Platform to cull through multiple health systems’ de-identified data, including EHRs, medical bills and claims data.

Key stat: The AI in healthcare market size will expand at a compound annual growth rate (CAGR) of 38.4% from 2022 to 2030, according to Grand View Research.

The big takeaway: By teaming up to create de-identified platforms, health systems access data more seamlessly rather than outsourcing to data analytics companies that don’t specialize in healthcare.