Mount Sinai deploys analytics and artificial intelligence to take on congestive heart failure
Mount Sinai Health System announced that it has tapped CloudMedx to help pinpoint people at risk of congestive heart failure as part of its emerging program dubbed HealthPromise.
The plan is to harness CloudMedx predictive insights for evidence-based care interventions to reduce readmissions and, ultimately, improve patient outcomes.
"We are passionate about bringing advanced analytics to the forefront of managing our chronic patients and improving our patient well-being," Ashish Atreja, MD, chief technology innovation and engagement officer in medicine at Icahn School of Medicine at Mount Sinai, said in a statement.
[EHRs getting better? Readers rank vendors higher than last year in new survey]
"As an industry, we do not have a sufficiently sophisticated tool to predict certain things such as disease progression and resulting readmissions in hospitals,” Atreja added. “We are working with CloudMedx to use new guidelines and algorithms, using clinical data to determine these risks and predictors.”
Atreja explained that the CloudMedx AI platform can ingest and process large amounts of data and compute big data analytics. It can also perform natural language processing on unstructured notes to surface patient risk profiles in real time, all of which reduce time, effort and expense.
The technology also enables Mount Sinai to determine at-risk patients so it can then enroll them in specialized connected health programs, like HealthPromise, to better manage their conditions and symptoms.
According to Medicare, some 20 percent of Medicare patients being discharged all across the country get readmitted within 30 days of discharge with an estimated cost of unplanned re-hospitalizations tallied at more than $17.4 billion.
Artificial Intelligence will be among the topics at the HIMSS and Healthcare IT News Big Data & Analytics Forum in Boston, Oct. 24-25. What to expect:
⇒ Charlotte hospitals analyze social determinants of health to cut ER visits
⇒ Big Data: Healthcare must move beyond the hype
⇒ Tips for reading Big Data results correctly
⇒ Small hospital makes minor investment in analytics and reaps big rewards
⇒ MIT professor's quick primer on two types of machine learning for healthcare
⇒ Must-haves for machine learning to thrive in healthcare