How Artificial Intelligence Affects Human Performance in Medical Chart Coding
Director, Inovalon Artificial Intelligence Lab for Advanced Insights
Co-Director, Center for Health Information and Decision Systems
Robert H. Smith School of Business
University of Maryland
Friday, Oct 30
9:00 – 10:00 am | Zoom
While the impact of artificial intelligence (AI) on jobs has generated considerable discussion and debate, little is known about how AI affects knowledge worker productivity. We developed an AI solution for medical chart coding in a publicly traded company and then evaluated its impact on productivity regarding coders’ job experience. We find evidence that AI improves worker productivity overall. However, different from existing studies on skill biased technological change, we find that seniority goes the opposite way: the productivity of senior workers has a much less productivity boost from the use of AI than that of junior workers. To uncover the mechanism behind this surprising finding, we look at the task specific experience. Our results confirm the existence of complementarity between human experience and AI. Further analysis reveals that the performance discrepancy of job experience is attributable to senior user resistance. This paper provides new empirical insights into how AI affects knowledge worker productivity, with important implications for wider adoption and use of AI among knowledge workers.