When ads appear in a web browser with an uncanny match to the viewer’s interests, it’s a result of predictive analysis through artificial intelligence (AI) and machine learning technology. CareCentrix, a home Health coordination company that works with payors and providers to create managed care networks, is using the same science to collect and assess data to reduce home health care costs and enable seniors to age in place.
“We’re right at the edge of a revolution with artificial intelligence and machine learning, particularly it’s use in the health care space,” Steve Wogen, CareCentrix chief growth officer, told Home Health Care News. “Our goal is to create a world where anyone can age at home.”
CareCentrix’s AI-driven care coordination platform, Homebridge, is fueled by a database of information, including 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 create customized health care plans for individual patients.
Bridging the Gap
Through this process, HomeBridge identifies potential issues or gaps in care and alerts clinicians so they can develop a post acute care plan for home health care providers that will prevent rehospitalization. For instance, 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 do not typically 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 cannot be underestimated, but AI learning allows us to close gaps in information and leverage limited nursing resources,” Wogen said.
These gaps in information cause lower quality post acute care, which can lead to increased rehospitalizaiton and health care costs, Wogen notes. CareCentrix analyzed HomeBridge data and found that hospital readmission rates dropped 38% over the 90 days following discharge as a result of interventions based on the data.
In addition to establishing a unique health plan for each home health care patient, HomeBridge builds smart networks so hospital clinicians can match the patient to the best post acute care provider. Some home health care providers may excel in different areas, such as rehabilitation or wound care. HomeBridge uses AI and machine learning to build a patient-to-provider matching recommendation, so doctors can find patients the best-fitting home health care agency.
HomeBridge also projects 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 example, 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’s 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.”
Written by Elizabeth Jakaitis