Routine electronic health records (HER)/administrative data could enable time- and cost-efficient, real-time surveillance and health-economic evaluation of hepatobiliary postoperative complications, yet no validated algorithm currently exists. We aimed to develop and externally validate an interpretable procedure-code-based, internationally portable algorithm classifying 30-day complications by Clavien-Dindo (CDC) grades and to compare its performance to machine learning (ML) approaches.