When advertisements appear in a web browser with an uncanny match to the viewer’s interests, it is an outcome of predictive analysis through artificial intelligence (AI) and machine learning technology. CareCentrix, a home health coordination company that works with payors and providers to developed managed care networks, is using the same science to collect and assess data to decrease home health care costs and enable seniors to age in place.
“We are right at the edge of a revolution with artificial intelligence and machine learning, particularly its use in the health care space,” Steve Wogen, CareCentrix chief growth officer said. “Our aim is to develop a world where anyone can age at home.”
CareCentrix’s AI-driven care coordination platform, HomeBridge, is fueled by a database of information, involving clinical information that is typically available in discharge orders, claims data, patient demographic details, retail purchasing habits of the patient and more. The smart analytics tool then uses algorithms to process this data and establish customized health care plans for individual patients.
Bridging the Gap through Artificial Intelligence
Through this procedure, HomeBridge identifies potential problems or gaps in care and alerts clinicians so they can establish a post acute care plan for home health care providers that will prevent rehospitalization. For example, the technology can look at a patient’s home location and compare its proximity to grocery stores, and alert home care providers to the fact that the patient may not have access to resources for a recommended diet.
Doctors and nurses don’t generally have the time or resources to make this type of deduction, but when it is noted in HomeBridge, they can include it in the health care plan they pass on to home health providers.
“Clinical judgement can’t be underestimated, but artificial intelligence learning permits us to close gaps in information and leverage limited nursing resources,” Wogen claimed.
These gaps in information cause lower quality post acute care, which can lead to increased rehospitalizaiton and health care charges, Wogen notes. CareCentrix analyzed HomeBridge data and discovered that hospital readmission rates dropped 38 percent over the 90 days following discharge as a result of interventions based on the data.
In addition to develping a unique health plan for each home health care patient, HomeBridge builds smart networks so hospital clinicians can match the sufferer to the best post acute care provider. Some home health care providers may excel in different areas, like rehabilitation or wound care. HomeBridge uses AI and machine learning to build a patient-to-provider matching recommendation, so doctors can find sufferers the best-fitting home health care agency.
HomeBridge also secures the probable number of followup primary care physician appointments home health care patients are likely to require.
The company is working to expand the analytics tool as the technology advances. For instance, HomeBridge has been integrated with Amazon’s intelligent personal assistant Alexa, and CareCentrix plans to integrate with Apple’s Siri within the next year.
“Health care has lagged behind in AI technology, but there is more critical and relevant data in this industry than any other at this time,” Wogen said. “Using it will advance providers’ care practices and lead to new innovations.”
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