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Artificial intelligence to improve clinical coding practice in Scandinavia: a crossover randomized controlled trial
Taridzo Chomutare;
Therese Olsen Svenning;
Miguel Ángel Tejedor Hernández;
Phuong Dinh Ngo;
Andrius Budrionis;
Kaisa Markljung;
Lill Irene Hind;
Torbjørn Torsvik;
Karl Øyvind Mikalsen;
Aleksandar Babic;
Hercules Dalianis
ABSTRACT
Background:
Crossover randomized controlled trial.
Objective:
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.
Methods:
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.
Results:
Based on Mann-Whitney U test, the median coding time difference for complex clinical text sequences was 123 seconds (P <.001, 95% CI: 81 to 164), representing a 46% reduction in median coding time when our tool was 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.
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. Clinical Trial: clinicaltrials.gov NCT06286865; https://clinicaltrials.gov/study/NCT06286865
Citation
Please cite as:
Chomutare T, Svenning TO, Hernández MT, Ngo PD, Budrionis A, Markljung K, Hind LI, Torsvik T, Mikalsen K, Babic A, Dalianis H
Artificial Intelligence to Improve Clinical Coding Practice in Scandinavia: Crossover Randomized Controlled Trial