Montefiore Semantic Data Lake Tackles Predictive Analytics


Semantic computing is becoming a hot topic in the healthcare industry as the first wave of big data analytics leaders looks to move beyond the basics of population health management, predictive analytics, and risk stratification.

This new approach to analytics eschews the rigid, limited capabilities of the traditional relational database and instead focuses on creating a fluid pool of standardized data elements that can be mixed and matched on the fly to answer a large number of unique queries.

Montefiore Medical Center is among the first healthcare organizations to invest in a robust semantic data lake as the foundation for advanced clinical decision support and predictive analytics capabilities.

Six months after introducing the concept to readers at HealthITAnalytics.com, Parsa Mirhaji, MD, PhD, has provided an update on Montefiore’s progress with a sophisticated, potentially revolutionary predictive analytics pilot program.

“The Semantic Data Lake is up and running, and it’s doing well,” said Mirhaji, Associate Professor of Systems and Computational Biology and the Director of Clinical Research Informatics at the Albert Einstein College of Medicine and Montefiore Medical Center-Institute for Clinical Translational Research. 

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“Right now, we are still in the middle of a pilot program that uses predictive analytics to flag any patient hospitalized at Montefiore Health System locations, who is at risk of death or of the need for intubation within the following 48 hours, which is the window of opportunity to complete an effective intervention for the course of events.”

As part of a collaboration with the Mayo Clinic, Montefiore is in the process of refining a predictive algorithm founded on retrospective data from more than 68,000 patients across the two institutions.  The data lake delivers real-time data for perspective surveillance on real patients, Mirhaji says, using actionable clinical data.

“It creates risk scores based on the patient’s likelihood of a major event within 48 hours,” he explained. “Then there’s another engine that kicks in based on those risk scores and other factors to determine what we can do for that particular patient to avoid the crisis.  It can send a personalized checklist of proposed interventions to the practitioner in charge of that case.”

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At the moment, the system is still in its pre-clinical validation stage.  The algorithm is working in parallel with the traditional care delivery process to test its capabilities, but clinicians are not currently receiving notifications for their patients. 

Instead, results are being sent to a group of clinical investigators who are comparing the predictive analytics with real-life patient care procedures to see how well the system is working.

“We are very happy with what we’re seeing right now, as the information is very sensitive and very specific,” Mirhaji added.

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