1. Artificial intelligence to improve clinical coding practice in Scandinavia: a crossover randomized controlled trial
- Author
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Chomutare, Taridzo, Svenning, Therese Olsen, Hernández, Miguel Ángel Tejedor, Ngo, Phuong Dinh, Budrionis, Andrius, Markljung, Kaisa, Hind, Lill Irene, Torsvik, Torbjørn, Mikalsen, Karl Øyvind, Babic, Aleksandar, and Dalianis, Hercules
- Subjects
Computer Science - Computers and Society ,Computer Science - Artificial Intelligence ,Computer Science - Computation and Language ,Computer Science - Human-Computer Interaction ,Computer Science - Machine Learning - Abstract
\textbf{Trial design} Crossover randomized controlled trial. \textbf{Methods} An AI tool, Easy-ICD, was developed to assist clinical coders and was tested for improving both accuracy and time in a user study in Norway and Sweden. Participants were randomly assigned to two groups, and crossed over between coding complex (longer) texts versus simple (shorter) texts, while using our tool versus not using our tool. \textbf{Results} Based on Mann-Whitney U test, the median coding time difference for complex clinical text sequences was 123 seconds (\emph{P}\textless.001, 95\% CI: 81 to 164), representing a 46\% reduction in median coding time when our tool is used. There was no significant time difference for simpler text sequences. For coding accuracy, the improvement we noted for both complex and simple texts was not significant. \textbf{Conclusions} This study demonstrates the potential of AI to transform common tasks in clinical workflows, with ostensible positive impacts on work efficiencies for complex clinical coding tasks. Further studies within hospital workflows are required before these presumed impacts can be more clearly understood., Comment: 13 pages, 4 figures, 4 tables
- Published
- 2024
![]("http://tim.nordenfur.se/kth/euroecho-abstract-figure.jpg")
, QC 20150122