Blue Cross and Blue Shield of North Carolina uses SAS® to predict patients at high risk of readmissions
Researchers know that hospital readmissions within 30 days of discharge are a big reason health care costs remain stubbornly high. What insurers haven't been able to figure out was how to predict who was likely to be readmitted and intervene before the person landed back in the hospital.
Customer Success Video
Check out this video to hear more about Blue Cross and Blue Shield of North Carolina and how it uses SAS.
(Runtime: 3 mins, 39 secs)
You have questions; our customers have answers. Check out this video Q&A.
Director, Health Economics
(Requires Windows Media Player 6.4.7 or higher)
Using SAS Analytics, Blue Cross and Blue Shield of North Carolina (BCBSNC) has built a model to more accurately predict hospital readmissions and deploy nurse case managers to help patients deemed most at risk. By looking at all causes for readmissions and looking at the data from the authorization stage, the model correctly beat chance by 400 percent in identifying at-risk patients.
The costs of readmission
Like many insurers, BCBSNC used length-of-stay data or diagnosis to flag patients for post-hospital outreach from nurse case managers. But the readmission rate wasn't budging.
"Our nurse case managers told us we were flagging patients that didn't require our help, while they were hearing anecdotally of other patients who needed intervention,'' explains Daryl Wansink, BCBSNC Director of Health Economics.
Digging into the data
"The perfect analysis delivered a day late is worthless,'' Wansink says.
To accurately predict and intervene, BCBSNC needed to analyze all its data on hospital readmissions to find patterns that could provide a much more nuanced flagging system. The insurer discovered 50 candidate predictors – details like whether a patient has diabetes or lives alone – that are factors in calculating readmission potential.
The model doesn't simply flag anyone with one of these predictors – that was a part of the crude method the insurer previously used. Instead, it looks at what a patient is being admitted for and compares that against the candidate predictors. For instance, a diabetic widower might be at very high risk of readmission following an in-patient stay for heart attack – but at hardly any risk for readmission for treatment of a stomach bug. In addition, the model is constantly rerun and enhanced as new information becomes available.
Saving money and helping patients
"There is tremendous potential for intervention into something that is not only a high-cost event, but one that has a severe impact on these individuals' lives and is something the clinical literature suggested was completely avoidable," Wansink says.
As soon as BCBSNC receives admission notice on one of its clients, the prediction software calculates a readmission risk. From there, nurse managers contact not only the patient but the hospital and physician practice to begin planning for a successful discharge.
"We want to do everything we can to keep the discharged patient out of the hospital, keep him healthy and keep his quality of life high,'' Wansink says.
The insurer takes a very collaborative approach toward using the information and shares it with its network providers on a daily basis. This transparent approach, and quality emphasis, has encouraged many providers to sign contracts with BCBSNC that tie reimbursement to their own efforts to reduce readmissions.
Ease of use makes project a success
"You can find relationships that aren't readily apparent when you run standard regression models,'' Wansink says. "[SAS] Enterprise Miner made it so darn easy. It cut down on the amount of work spent churning through data to look for relationships.''
In addition, Wansink says that his staff can build five models in less than the time it takes to build one using the previous method. "And the models are robust and continue to perform well over time," he adds.
Changing lives, changing outcomes
Copyright © SAS Institute Inc. All Rights Reserved.
Director of Health Economics
Blue Cross and Blue Shield of North Carolina
Cut the rate of hospital readmissions that occur within 30 days of discharge
SAS Enterprise Miner
At the time of authorization, BCBSNC predicts the potential for readmission. High-risk patients are assigned to nurse case managers who work with the patient and care team to develop discharge plans. The model correctly beat chance in identifying at-risk patients by 400 percent.
“"We want to do everything we can to keep the discharged patient out of the hospital, keep him healthy and keep his quality of life high."”
Director of Health Economics