137 From Excel to Diagnostic Excellence: A Python-Driven Approach to Optimizing Pathologist Workflows

Abstract

Abstract Introduction/Objective Modern pathology requires precise data interpretation to enhance patient care and operational efficiency. We applied a Python-based informatics approach to analyze turnaround times, second opinion consultations, and diagnostic concordance. This investigation aimed to determine actionable insights and assess the feasibility of a subspecialty frozen section service. Methods/Case Report Monthly frozen section data from 2023 was gathered. A Python script was developed to transform the data into over 150 dynamic graphs. These visualizations detail turnaround times, second opinions, concordance rates, and overall performance metrics. Breakdowns by case complexity and specialty pairing (surgeon and pathologist subspeciality) further informed our analysis. Results The average turnaround time of 43.4 minutes for same-specialty pairs versus 46.9 minutes for different specialties. Cases requiring a second opinion extended turnaround time notably, and delays were most pronounced in breast, gastrointestinal, and renal cases. Diagnostic concordance remained high at 97.5% regardless of surgeon-pathologist speciality pairing. Conclusion Our findings demonstrate that aligning surgeon and pathologist specialties modestly reduces turnaround times, yet complexity and second opinion requirements significantly extend them. Tailored second opinion protocols and subspecialty-specific strategies can optimize efficiency and maintain high diagnostic accuracy. Also, targeted interventions, such as refining second opinion protocols, can enhance both operational efficiency and diagnostic accuracy in modern pathology.

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