In recent years, the U.S. health care system’s shift toward value-based reimbursement has given home health providers cause to test all sorts of approaches aimed at reducing hospital readmission rates or preventing unnecessary trips to the emergency department.
Predictive analytics-driven telehealth outreach programs that risk-stratify patients based on medical vulnerability and other factors can be particularly impactful, findings from a recent study suggest.
“A coordinated approach to analytics may help payers and providers realize efficiencies in care and direct potentially timely interventions to patients in greatest need,” researchers noted in the study, published earlier this month in the Journal of Health Economics.
To help gauge how well telehealth and predictive analytics tools keep patients safely at home, researchers evaluated a care coordination program administered by Independence Blue Cross of Philadelphia to its Medicare Advantage (MA) members with congestive heart failure. As part of their evaluation, researchers used a predictive algorithm to assign the insurer’s MA members a risk score, with the highest-scoring members selected for telephone follow-up initiated via registered nurse health coaches.
In general, the predictive algorithm pulled from medical and pharmacy claims, lab results, demographic data and other information.
Telephone follow-up through the care coordination program revolved around needs assessment, care planning, appointment scheduling, medication assistance and transportation coordination, along with social worker and behavioral health specialist support when necessary.
Overall, researchers analyzed claims data for 29,222 fully-insured Blue Cross of Philadelphia MA members diagnosed with congestive heart failure. Their final sample included 1,974 patients, roughly half of whom were targeted for the predictive analytics-driven care coordination program.
Among high-risk Medicare Advantage members with congestive heart failure, a proactive outreach program paired with a claims-based predictive algorithm reduced the likelihood of an ED or specialist visit over the course of a year by 20% and 21%, respectively.
“These findings suggest that continuity of care, in the form of a communication hub as part of a targeted intervention, may serve as an effective tool in reducing unnecessary utilization among high-risk populations,” researchers concluded. “Additionally, effective telehealth case management, rather than face to face case management, can lead to reductions in ED care.”
Predictive analytics in home health care
The Journal of Health Economics study looked at care management and risk-stratification from an insurer’s perspective, but home health providers have also increasingly turned to predictive analytics solutions to prevent negative health outcomes.
Baton Rouge, Louisiana-based Amedisys (Nasdaq: AMED), for example, has been fine-tuning its use of a risk-stratification tool designed to slash re-hospitalization rates and keep patients away from the ED.
The tool is engineered by Nashville, Tennessee-based predictive analytics firm Medalogix, which Amedisys invested in late last year.
“In order for us to move into where the world’s going to from a risk-based environment, we need to have the data, business intelligence [capabilities] at an individual patient level to drive better outcomes,” Amedisys CEO Paul Kusserow previously told Home Health Care News. “You have to be able to predict on a very regular basis who’s your highest risk and how you are going to alter your care plan so you can stop unnecessary hospitalizations.”
Birmingham, Alabama-based Encompass Health Corporation (NYSE: EHC) has likewise experimented predictive analytics technology.
In addition to reducing the likelihood of an ED or specialist visit, the predictive analytics-fueled care coordination program from Blue Cross of Philadelphia helped cut hospital admissions by 38% at 30 days, 46% at 90 days, 26% at 180 days and 20% after 360 days relative to a control group average, according to the study.
Those findings did not carry full statistical significance, however, meaning they could have been a result of chance.
‘The results imply savings 30 days post-outreach of $500 to $1,000 for ED visits per member per year,” the researchers wrote. “Similarly, while mostly statistically insignificant, the reduction in hospitalization implies annual savings of about $13,000 per member over the 30 days after outreach.”
In general, those savings are triggered by more efficiently targeting the sickest members within a high-spending population, who — based on predictive models — have the most to gain from an intervention.
Guy David, associate professor at the University of Pennsylvania’s Wharton School, was the lead author of the study.
HHCN previously connected with David as part of a story highlighting how longer home health visits are tied to lower hospital readmission rates.