Case logs are foundational data in surgical education, yet cases are consistently under-reported. Logging behavior is driven by multiple human and systems factors, including time constraints, ease of case data retrieval, access to data-entry tools, and procedural code decision tools. We examined case logging trends at three mid-sized, general surgery training programs from September 2016-October 2020, January 2019-October 2020 and May 2019-October 2020, respectively. Across the programs we compared the number of cases logged per week when residents logged directly to ACGME versus via a resident education platform with machine learning-based case logging assistance tools. We examined case logging patterns across 4 consecutive phases: baseline default ACGME logging prior to platform access (P0 “Manual”), full platform logging assistance (P1 “Assisted”), partial platform assistance requiring manual data entry without data integrations (P2 “Notebook”), and resumed fully integrated platform with logging assistance (P3 “Resumed”).