Artificial intelligence to improve clinical coding practice in Scandinavia: a crossover randomized controlled trial

TL;DR

This study evaluates an AI tool called Easy-ICD that significantly reduces coding time for complex clinical texts in Scandinavia, demonstrating potential to enhance clinical workflow efficiency, though accuracy improvements were not statistically significant.

Contribution

The paper introduces and tests a novel AI tool, Easy-ICD, for clinical coding, showing its effectiveness in reducing coding time for complex texts in a Scandinavian setting.

Findings

46% reduction in coding time for complex texts with AI tool

No significant accuracy improvement observed

Potential to improve clinical workflow efficiency

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.