Healthcare and Life Sciences


Angela Fontes

Senior Research Economist

Transparency Tool:  Many Users, Little Savings

In 2012 employees at a Midwestern IT firm began using a price transparency tool that uses behavioral economics principles. Study aims were: (1) to determine characteristics of households who used the tool and (2) to determine if using households purchased medical and prescription drug services at lower prices than non-using households. Primary study data are medical claims and enrollment data for 18 months before launching the transparency tool and 18 months afterwards. The treatment group is tool users and the comparison group is non-users. Sixty-two percent of households used the tool, a figure considerably higher than previous studies reported. Probit analysis suggested households with higher risk scores in the pre-treatment period, headed by an older worker, and having coverage for a spouse and children were more likely users. Multivariate analysis indicated the tool had a weak or no effect on overall


Angela Fontes, PhD, PMP, is a Senior Research Economist for NORC at the University of Chicago.  Dr. Fontes has been providing analytic solutions to complex business and social issues through comprehensive research consulting for over a decade. She specializes in working collaboratively with clients to identify and scope research needs, source solutions, and design cutting edge research methodologies that result in specific, actionable recommendations.  Current and former clients include Capital One, Wachovia, and other Fortune 500 financial institutions, as well as numerous not-for-profit clients such as Planned Parenthood, the Northwest Side Housing Center, FINRA, and United Way.   Dr. Fontes, holds a Ph.D. in Consumer Behavior and Family Economics from the University of Wisconsin-Madison, and is a certified Project Management Professional (PMP).  Her research is focused on understanding consumer behavior in economic markets, and is a recognized expert with published research on consumer behavior related to retirement planning, financial asset, home and insurance ownership, and financial risk tolerance in journals including Hispanic Journal of Behavioral Sciences, Health Affairs, the Journal of Family and Economic Issues, the Journal of Women, Politics and Policy, and Financial Counseling and Planning.  Prior to her work at NORC, Fontes worked in market research analytic consulting for Chamberlain Research Consultants and Leo Burnett.  Fontes is adjunct faculty at Northwestern University, where she teaches graduate courses in behavioral economics, predictive analytics and survey design and policy analysis.

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Devjeet Haldar

Divisional VP for Business Analytics & Information Management
Abbott Laboratories

Art and Science are Strategic Allies in the War on Waste at Abbott Laboratories

In high volume manufacturing, keeping processes lean and efficient is an imperative to drive operational efficiency, sustainable cost savings, and reduce variability. Monitoring the manufacturing lines with data analytics solution reduced the cycle time to determine true root case and take corrective actions, mitigated risk to meet the margin improvement targets, and automated highly manual processes of measuring waste. As a foundational step, we invoked math, business and technology skills at scale to wage an effective war on waste. Key highlights are: a) Augmented first principles thinking to synthesize loss points and KPIs, b) Established Big Data architecture – a scalable platform for repeatable insights, c) Created a coherent data model by integrating data from disparate sources, and d) Applied behavioral science and design thinking to build intuitive cockpit for monitoring waste at most direct actionable areas of the organization with drill down decision rights capabilities.
As DVP of Business Analytics and Information Management, Devjeet is providing vision and transformation leadership to initiate and execute on advanced analytics and information management strategy for the company, across all four major businesses of Nutrition, Diagnostic, Medical Devices, and Established Pharmaceutical. In his current role, Devjeet has established Abbott’s Center of Excellence for Data Science and focuses on top line growth and bottom line efficiencies by partnering with key functions such as Marketing, Commercial, Finance, Operations, R&D and by evangelizing analytics as “the way to do business”. Devjeet has 20 years of experience in business and technology across diverse industries. Prior to joining Abbott in November 2005, Devjeet held various positions in Management Consulting roles and was responsible for building consulting practice, managing large transformation projects, and consulting executive leadership on innovative solutions.

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Gregory Pine

MPA Healthcare Solutions

Cross-industry Pollination Of Analytic Methods – Lessons For Health Care Improvement

Awash in “big data” (claims, EHR, labs, and more) health care payers and providers often struggle with finding the “right data”—reliable, context-rich, and actionable data to support financial and clinical decision-making. MPA is known for its health care data expertise, including pioneering work in assessing outcomes throughout the continuum of care. Gregory Pine will discuss lessons learned from analytic methods in other industries and their applications within healthcare. Highlights will include innovative applications of next-generation statistical process control methods to monitor the clinical and financial outcomes of care redesign. Disruptive change in health care is survivable, and leveraging analytic methods from other industries can bring profound insight and opportunity for all participants.


Gregory Pine is CEO of MPA Healthcare Solutions, an innovative health care analytics firm that provides data integrity assessment, risk adjustment and real-time analytic services to support decision-making in the evolving health care landscape. Pine’s focus is on delivering trustworthy, actionable data that can bridge sometimes competing financial and clinical goals for hospital and insurer business operations. He has led initiatives to evaluate commercially-available data and risk-adjustment models, and to develop efficient methods for using unstructured clinical data in the targeted validation of models built on structured administrative data. He has drawn upon the academic roots of the firm in continuing a robust research and development program, with numerous peer-reviewed publications. Prior to stepping into his current role at MPA, Pine served for eight years as President of the analytics firm Healthcare Quality Insights.


Bill Roberts

MPA Healthcare Solutions

On The Application of Sparse Matrix Algorithms to Medical Claims Data

Medical claims generally consists of a small number of codes from the many tens of thousands of medical diagnosis or procedure codes potentially available. A claim can be thus mathematically represented as a very sparse, high-dimensional vector and collections of claims to be represented as sparse, high-dimensional matrices. Sparse high-dimensional matrices arise in a diverse range of applications from quantum mechanics to movie recommendation. A large amount of scientific research has been devoted to efficient processing of such matrices and new algorithms have been developed to predict matrix values and detect anomalies. This talk introduces sparse matrix algorithms and describes their application and performance in a range of problems involving medical claim data including billing anomaly detection and risk adjustment.



At Deloitte, Bill leads a team of data scientists specializing in making data actionable through analytics. He has 20 years’ experience in the application of machine learning and stochastic modeling to real-world problems. He has developed and deployed analytics in industry, government and defense agencies enabling on open and closed-source data. Bill is a part-time Professorial Lecturer of Engineering Management and Systems Engineering at The George Washington University. He has authored over 20 publications on machine learning, stochastic simulation and modeling. He has received awards for innovation, a best paper award from an international academic journal, and has notable performances in analytics competitions such as the Netflix Competition. Bill earned a Ph.D. in Statistical Signal Processing from George Mason University, and a BE with honors in Electrical Engineering and BS in Physics and Mathematics from University of Adelaide.

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Nilay D. Shah

Associate Professor of Health Services Research
Mayo Clinic

Promise Of Big Data In Healthcare

There has been significant interest in the use of ‘big data’ in healthcare over the last decade. While there is increasing availability of data over the last few years, there still exist a number of challenges around healthcare data. In this presentation, I will cover the progress that has been made in harnessing big data while identifying some of the key challenges.


Nilay Shah is health services researcher in the division of Health Care Policy and Research at Mayo Clinic and an Associate Professor of Health Services Research in the Mayo Clinic College of Medicine. He is currently the co-director of the Translating Comparative Effectiveness Research (TRACER) Unit, a part of the Mayo Center for Translational Science Activities (CTSA). He is also the Scientific Director for the OptumLabs Initiative in the Centers for Science of Health Care Delivery (CSHCD) at Mayo Clinic. Nilay’s research focus is on improving chronic care delivery, especially for patients with multiple chronic conditions. His work incorporates a range of methodological tools including mathematical models, observational designs, and prospective trials. In addition, he is involved in numerous studies to test the role of decision support tools for patient-centered knowledge translation and for translating comparative effectiveness research into routine clinical practice. Nilay is also involved in a number of studies testing different models of care delivery in both the primary care and specialty care settings.