Director of Health Economics
BCBSNC predicts 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.
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.
"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."
The costs of readmission
About 13 percent of inpatients account for the majority of hospital costs and much of those costs are related to readmission. Hospitals, doctors and insurers have tried different approaches to reduce readmissions – from increasing discharge education to assigning case managers to follow up with patients.
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
BCBSNC wanted to come up with a better way to identify patients at risk for readmission, and reach them quickly – in some cases while they were still in the hospital. But much of the detail about a hospitalization doesn't make its way to the insurer until that 30-day window has passed. The goal: Predict the potential for readmission by looking at the data supplied at the time of authorization and admission so staff can engage with the patients and providers before discharge.
"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
While BCBSNC wants to manage medical expenses and keep premiums reasonable, the readmission project is mainly about enhancing the quality of life.
"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
BCBSNC uses SAS® Enterprise Miner™ software to build the prediction models. It chose SAS Enterprise Miner because of the availability of advanced statistical options like neural networks and decisions trees.
"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
Ultimately, the insurer wants its efforts to be productive for its members. "By using insights provided by predictive models, we can empower our nurse case managers to make a difference in people's lives,'' Wansink says.
Cut the rate of hospital readmissions that occur within 30 days of discharge.
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%.