Friend or Foe? The Interaction Between Human and Artificial Intelligence on Performance in Medical Chart Coding

52 Pages Posted: 24 Jun 2019 Last revised: 24 Feb 2021

See all articles by Weiguang Wang

Weiguang Wang

University of Rochester - Simon Business School

Guodong (Gordon) Gao

University of Maryland - R.H. Smith School of Business

Ritu Agarwal

University of Maryland - Robert H. Smith School of Business

Date Written: June 18, 2019

Abstract

The impact of artificial intelligence (AI) on jobs has generated considerable discussion and debate. Does AI substitute for worker experience or complement the performance of more experienced employees? This debate has significant implications for economies and societies. We developed an AI solution for medical chart coding in a publicly traded company and evaluated its impact on productivity, as conditioned by coders’ experience, in a field setting. We find evidence that AI improves worker productivity overall, but its impact depends on the type of worker experience. Workers with greater task experience, measured by the number of charts they have coded over the years, gain more from AI. However, workers with greater seniority experience, measured by the number of years they have been working for the company, gain less from AI than their junior colleagues. To uncover the mechanism behind this surprising finding, we conducted a survey of senior workers and found that user resistance caused by a lack of trust is the reason for lower productivity gains from AI. This insight is further confirmed in a lab experiment. This study provides new empirical insights into how AI affects knowledge worker productivity, with important implications for wider adoption and use of AI in knowledge-intensive work.

Keywords: artificial intelligence, productivity, worker skills, healthcare, medical coding

Suggested Citation

Wang, Weiguang and Gao, Guodong (Gordon) and Agarwal, Ritu, Friend or Foe? The Interaction Between Human and Artificial Intelligence on Performance in Medical Chart Coding (June 18, 2019). Available at SSRN: https://ssrn.com/abstract=3405759 or http://dx.doi.org/10.2139/ssrn.3405759

Weiguang Wang (Contact Author)

University of Rochester - Simon Business School ( email )

Rochester, NY 14627
United States

Guodong (Gordon) Gao

University of Maryland - R.H. Smith School of Business ( email )

4325 Van Munching Hall
College Park, MD 20742
United States

HOME PAGE: http://www.rhsmith.umd.edu/faculty/ggao/

Ritu Agarwal

University of Maryland - Robert H. Smith School of Business ( email )

College Park, MD 20742-1815
United States

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