Mayo Clinic-Mercy Data Collaboration Aims to Drive Improved Outcomes

Mayo Clinic-Mercy Data Collaboration Aims to Drive Improved Outcomes. Two business people shake hands with their hands appearing inside a digital cloud icon with medial data analytics overlaid across the rest of the image.

Advances in artificial intelligence, machine learning and secure cloud-based environments have helped researchers make better use of aggregated data to enable earlier detection of disease and better treatment options. Mayo Clinic and Mercy will take advantage of these greater capabilities and data science, as well as their immense volumes of de-identified data collected over the years, to partner on finding diseases earlier and determining the best treatment paths.

The 10-year collaboration agreement between the health systems will eliminate barriers to health care innovation by bringing together data and human expertise in a new way of working together, notes John Halamka, M.D., an emergency medicine physician and president of Mayo Clinic Platform.

The collaboration’s success rests on having each organization share its strengths.

Mayo’s expertise in highly complex care and extensive investment in data science platforms together with Mercy’s two centuries of innovative care delivery in diverse communities and vast clinical information, including more than 500 million de-identified patient encounters, will provide the opportunity to develop high-value solutions and algorithms leading to more optimal care for patients. Additionally, Mercy’s and Mayo’s different populations and geographic locations will improve accuracy, reduce model bias and create more diverse, and therefore stronger, treatment recommendations.

Two Initial Patient Outcome Focuses for the Alliance

1 | Information Collaboration

The organizations will use a distributed data network that enables Mayo and Mercy to work with de-identified data assets without extracting or transferring any data between them. Instead, each organization will retain control over its data and enable more effective interventions. The goal is to help data scientists analyze patterns of effective disease treatment and prevention based on longitudinal data review over prolonged periods.

2 | Algorithm Development and validation

Algorithms and machine learning models that come from the research will help indicate proven treatment paths based on years of patient outcomes. The evidence eventually could be made available to other providers to help them deliver more proactive and predictive care.

The alliance is similar to that of Truveta, the clinical data analysis startup launched in 2021 by health systems including Tenet, Providence and CommonSpirit. Truveta currently has more than 20 health systems partnering in its venture.

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