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

• Published in: Information systems research Vol. 22; no. 3; pp. 429 - 446
• Main Authors: Aron, Ravi, Dutta, Shantanu, Janakiraman, Ramkumar, Pathak, Praveen A.
• Format: Journal Article
• Language: English
• Published: Linthicum INFORMS 01.09.2011; The Institute for Operations Research and the Management Sciences (INFORMS); Institute for Operations Research and the Management Sciences
• Subjects: Automatic control; Automation; Compliance; Datasets; Error rates; Field research; Field study; Health informatics; Hospital administration; hospital information systems; hospital performance; Hospital units; Hospitals; Hypotheses; Impact analysis; Information storage and retrieval systems; Information systems; Information technology; Management; Mechanization; Medical errors; Medical research; Methods; Patient safety; Physicians; Prevention; procedural errors; Quality management; Studies; Technology application; United States; United States; Asia
• ISSN: 1047-7047; 1526-5536
• DOI: 10.1287/isre.1110.0350

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.