Context Changes and the Performance of a Learning Human-in-the-loop System: A Case Study of Automatic Speech Recognition Use in Medical Transcription

Abstract

The paper presents how organizational practices enable the improvement and maintenance of task performance in a learning human-in-the-loop system exposed to a wide range of context changes. We investigate how the case company tripled the efficiency of medical transcribers by leveraging its machine learning-based automatic speech recognition technology. We find that the focal system operated across stable, drifting, and jumping contexts. Despite changes, it continued to improve or maintained performance thanks to two sets of organizational practices aligning it with the context: extending and refining. This paper makes two key contributions: It shows the importance of considering context changes in the design and operation of learning human-in-the-loop systems. Our empirical findings help with resolving some contradictory outcomes of the recent conceptual work. Secondly, we show that context alignment practices are situated at the sociotechnical system level and, thus, are not just technical solution nor can be detached from social elements.