AI, patient monitoring to drop readmits

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Utilizing artificial intelligence and patient-monitoring tools may help predict the possibility of a readmission after a patient is discharged from the hospital, according to a new study.

The Veterans Administration-sponsored analyses gave 100 heart failure patients, across four hospitals, wearable biosensors from VitalConnect, following their discharge. Coupled with a smartphone, those sensors transmitted physiologic data back to the cloud.

Vendor PhysIQ used its algorithms and machine learning to analyze patients' vital sign patterns, and establish baselines, according to the study. Harnessing that data, PhysIQ analytics then looks for any signs that a possible acute event might trigger a rehospitalization.

Patients, who were an average age of just over 68, were readmitted for heart failure 33 times during the study period. Early results show that the wearable sensors did, in fact, provide accurate early detection of impending rehospitalizations, researchers found.

They're now looking to evaluate how the tech might help to manage post-acute patients across other environments, including skilled nursing facilities. This is a key consideration, they say, as more than 6 million in the U.S. are diagnosed with heart failure each year, leading to 1 million hospitalizations. About 20% of those patients are readmitted to the hospital within 30 days of discharge.

“Heart failure continues to be a major challenge in healthcare today,” Dr. Stephen Ondra, chief medical officer of physIQ, said in a release. “Sadly, there has been far too little improvement over the past 15 years with respect to our ability to proactively manage acute deterioration. As a result, healthcare has struggled to adequately improve patient quality of life and reduce the exorbitant costs associated with this chronic disease.”


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