Predictive Analytics solutions deal with uncovering insights from trends and patterns to determine the impact of operational adjustments and market forces on your organization. Statistical analysis and predictive modeling expand on the findings gained through business intelligence solutions to answer “What will happen?” given certain business situations.
Perficient’s IBM Predictive Analytics practice has seen tremendous success over the past year in delivering custom solutions across a variety of industries, including healthcare. One key healthcare solution created and implemented by the practice is the Patient Readmission Predictive Analytics model. To find out more about this healthcare solution, we interviewed subject matter expert Dale Less, a Senior Solutions Architect at Perficient.
Tell us about your experience with IBM Predictive Analytics solutions?
I have been using IBM Predictive Analytics solutions for approximately 4 years and other analytics products for over 10 years. I have built solutions using IBM Predictive Analytics to identify different types of insurance fraud and abuse, violent crimes for law enforcement, marketing research and hospital readmission prediction.
Where has the IBM BA team recently implemented our Patient Readmission Predictive Analytics solution?
We developed a custom readmission solution for a large Healthcare System in Ohio. This solution is unique in that rather than focusing on a single disease or condition, this solution predicts readmissions across all diseases and conditions. Another unique aspect of this solution is that it focuses on the psycho-social needs on the patient and the family rather than a traditional clinical approach, which is the focus of most of the industry.
What key outcomes was this customer looking to achieve by implementing this solution?
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The Healthcare Organization is attempting to reduce hospital readmissions across all of their patients. They are taking the approach of educating the patients and their families to better manage their condition while coordinating various services within the community. They are looking to identify patients who are in the most need of interventions, identify which interventions are most appropriate and begin the intervention process as quickly as possible; whereas traditionally, interventions occurred at the end of a patient’s stay. Early intervention allows for more time for clinicians to provide instruction, increase patient and family member understanding and allow time for community services to be arranged so they are ready when the patient is discharged. This solution also helped to optimize hospital resources to provide interventions to those who stood to benefit the most.
What outcomes did the client achieve?
The predictive modeling process derives a prediction of whether or not a person is at risk for being readmitted within 30 days for each day they are in the hospital. These predictions are assigned a probability which is used to prioritize daily patient interventions. Nursing assessment data is also segmented into different risk and intervention profiles to identify health and lifestyle behaviors that clinicians can use to develop appropriate interventions and plans of care.
Did we deliver additional value to the customer they did not intend to receive?
We derived unique measures of patient frailty and how well they manage their care outside of their hospital stay. We developed a unique method for determining if a patient has fallen and the injuries that were possibly sustained prior to being admitted. This fall indictor, along with nutrition and certain types of medications, increased the readmission risk for certain patients by over a factor of 13, compared to similar patients. The client was aware that falls were an issue with their elderly population but they were not aware of how it was related to hospital readmissions.
What were the biggest surprises the customer has uncovered through these efforts?
We looked beyond just the inpatient hospital stay, to activities at the physician office level and during Emergency Room visits. Our analysis showed that patients who were frequent users of the ER were significantly more likely to be readmitted. The client was unaware of this finding. Many of these patients did not have a primary care physician and used the ER as their primary care physician. As a result of this discovery, the hospital is exploring staffing the ER with social workers that can quickly intervene and decrease hospital admission. This could lead to millions of dollars of annual savings.
What IBM software components were implemented as part of this solution?
IBM SPSS Modeler Gold and SPSS Statistics.
Outside of the Patient Readmission solution, what are some other use cases for Predictive Analytics?
Pretty much any industry that has large volumes of structured or unstructured data. IBM SPSS connects to most databases using a standard ODBC connection. Consistently the feedback we get from new users is that the software is very intuitive and “gets out of their way,” allowing them more time to do analytics and predictive modeling and less time writing code.