Quality Management Using Data Analytics: An Application to Pharmaceutical Regulation

54 Pages Posted: 3 Jan 2016

See all articles by Vishal Ahuja

Vishal Ahuja

Southern Methodist University (SMU) - Information Technology and Operations Management Department (ITOM)

John R. Birge

University of Chicago - Booth School of Business

Chad Syverson

University of Chicago Booth School of Business; National Bureau of Economic Research (NBER)

Elbert S. Huang

University of Chicago - Pritzker School of Medicine

Min-Woong Sohn

University of Virginia - School of Medicine

Date Written: December 22, 2015

Abstract

The U.S. government regulates consumer products through its various federal agencies. One such agency is the Food and Drug Administration (FDA) that governs the approval and safe public use of pharmaceutical products. If a drug is found unsafe, the FDA can issue a recall or a black box warning (BBW). This regulatory decision directly affects an operational decision: providers' production technology, affecting their treatment choices. Existing methods for monitoring drug safety are geared towards identifying unknown adverse drug reactions (ADRs) and suffer from several shortcomings such as reliance on limited data. There is a lack of data-driven approaches to evaluate a drug's association with a specific ADR. We propose a data-driven approach that fills this gap. We demonstrate the workings of our approach using a controversial BBW on a diabetes drug that warned prescribers of an increased risk of heart attack and cardiovascular mortality with the drug. Our findings, based on a large and comprehensive dataset, suggest that the drug was not harmful. On the contrary, we find that individuals who used the drug were less likely to die from cardiovascular complications or experience a heart attack. Our approach is robust to multiple specifications, avoids selection bias, and is complementary to existing drug surveillance systems. Further, our approach offers policymakers a decision support system to carefully assess drug safety in a real-world setting. Our approach can be extended to other consumer products that are subject to recalls and/or warnings such as toys, food, and automobiles.

Keywords: quality management; data analytics; FDA decision making

JEL Classification: C33, I18, L15

Suggested Citation

Ahuja, Vishal and Birge, John R. and Syverson, Chad and Huang, Elbert S. and Sohn, Min-Woong, Quality Management Using Data Analytics: An Application to Pharmaceutical Regulation (December 22, 2015). Available at SSRN: https://ssrn.com/abstract=2709881 or http://dx.doi.org/10.2139/ssrn.2709881

Vishal Ahuja (Contact Author)

Southern Methodist University (SMU) - Information Technology and Operations Management Department (ITOM) ( email )

Dallas, TX 75275
United States

John R. Birge

University of Chicago - Booth School of Business ( email )

5807 S. Woodlawn Avenue
Chicago, IL 60637
United States

Chad Syverson

University of Chicago Booth School of Business ( email )

1126 East 59th Street
Chicago, IL 60637
United States

National Bureau of Economic Research (NBER)

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

Elbert S. Huang

University of Chicago - Pritzker School of Medicine ( email )

Chicago, IL 60637
United States

Min-Woong Sohn

University of Virginia - School of Medicine ( email )

United States

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