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Reducing Costs and Improving Patient Outcomes with In-Database Analytics

Jennifer Vogel (Capital BlueCross), John Rollins (IBM), Greg Rodd (IBM Corporation)
Value-Based Care Salon H-K
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Hospital readmission rates have become an increasingly urgent concern in the face of competition in the health insurance industry and new federally mandated requirements. As part of the Affordable Care Act and Hospital Readmission Reduction Program, the Centers for Medicare and Medicaid Services are making reductions in Diagnostic Related Group (DRG) payments for readmissions. One of the more costly and frequently occurring readmissions is due to congestive heart failure (CHF).

In a pilot study supporting a proposed program to reduce CHF readmission risk and thereby provide better patient outcomes, Capital Blue Cross (CBC) and IBM teamed to develop a predictive model to identify CHF patients at high risk of 30-day readmission. These high-risk patients would then be targeted for additional post-discharge intervention. Using a cohort of approximately 2,800 patients hospitalized with CHF, a decision tree classification model was developed to predict patients’ readmission outcomes. All phases of the predictive modeling, scoring, and data preparation were performed in-database using the native capabilities of the IBM PureData System for Analytics platform installed at Capital Blue Cross.

This study highlights the substantial added value that can be realized through in-database analytics. Key advantages include centralized data governance and integration, automated data preparation, accelerated development of predictive models, model governance and storage, and enterprise-wide, on-demand deployment of those models.

With in-database analytics as the foundation, physicians can now incorporate the most timely, data-driven information into their patient care decisions. CHF patients at high risk of readmission can be targeted at the point of care and at the time of transfer of care for special intervention to reduce the likelihood of readmission, leading to better patient outcomes and reduced costs.

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Jennifer Vogel

Capital BlueCross

Jennifer Vogel is the Senior Director of Analytics & Reporting Strategy at Capital BlueCross. She leads the implementation of the Enterprise Information Management data warehouse with responsibility for data quality identification and improvement, data governance, and analytics, having previously served as Director of Network Management Analysis and Reporting in the Provider Relations Department. Prior to joining Capital BlueCross, she served as Executive Project Manager for the CEO of NYLCare Health Plans and as an actuarial analyst at Sanus Corp. Health Systems in New York and at PMA in Philadelphia. Ms. Vogel holds a degree in Mathematics from Dickinson College.

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John Rollins


John B. Rollins, Ph.D., P.E., is the Chief Data Miner and Technical Manager of the Advanced Analytics POC Team, IBM Netezza Analytics Solutions, IBM Software Group. His background is in the fields of engineering, econometrics, and advanced analytics in many industries. He holds seven patents, and he has authored a best-selling engineering textbook, many peer-reviewed technical papers, and two IBM Redbooks. He holds two doctoral degrees in economics and petroleum engineering from Texas A&M University and is a registered professional engineer in Texas.

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Greg Rodd

IBM Corporation

Gregory D. Rodd is a Data Scientist with the Advanced Analytics POC Team, IBM Netezza Analytics Solutions, IBM Software Group. He has extensive industry experience as a consultant in energy and utilities, retail, financial credit engineering, in data warehouse and statistical services consulting in academia, and in donation targeting for a worldwide relief organization.

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