Artificial intelligence to improve clinical coding practice in Scandinavia: a crossover randomized controlled trial
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866916462423179264 |
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| author | 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 Dalianis, Hercules |
| author_facet | 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 Dalianis, Hercules |
| contents | \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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2410_23725 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Artificial intelligence to improve clinical coding practice in Scandinavia: a crossover randomized controlled trial 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 Dalianis, Hercules Computers and Society Artificial Intelligence Computation and Language Human-Computer Interaction Machine Learning \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. |
| title | Artificial intelligence to improve clinical coding practice in Scandinavia: a crossover randomized controlled trial |
| topic | Computers and Society Artificial Intelligence Computation and Language Human-Computer Interaction Machine Learning |
| url | https://arxiv.org/abs/2410.23725 |