The Impact of Automation of Systems on Medical Errors: Evidence from Field Research

Ravi Aron, Carey Business School of Johns Hopkins University; Shantanu Dutta, USC Marshall School of Business; Ramkumar Janakiraman, Texas A&M Mays Business School; and Praveen A. Pathak, University of Florida Warrington College of Business Administration

Information Systems Research, 22(3):429-446

Abstract: We use panel data from multiple wards from two hospitals spanning a three-year period to investigate the impact of automation of the core error prevention functions in hospitals on medical error rates. Although there are studies based on anecdotal evidence and self-reported data on how automation impacts medical errors, no systematic studies exist that are based on actual error rates from hospitals. Further, there is no systematic evidence on how incremental automation over time and across multiple wards impacts the rate of medical errors. The primary objective of our study is to fill this gap in the literature by empirically examining how the automation of core error prevention functions affects two types of medical errors. We draw on the medical informatics literature and principal-agency theory and use a unique panel data set of actual documented medical errors from two major hospitals to analyze the interplay between automation and medical errors. We hypothesize that the automation of the sensing function (recording and observing agent actions) will have the greatest impact on reducing error rates. We show that there are significant complementarities between quality management training imparted to hospital staff and the automation of control systems in reducing interpretative medical errors. We also offer insights to practitioners and theoreticians alike on how the automation of error prevention functions can be combined with training in quality management to yield better outcomes. Our results suggest an optimal implementation path for the automation of error prevention functions in hospitals.

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