Utilizing the open-source programming language Python to create interactive Quality Assurance dashboards for diagnostic and screening performance in Cytology.

Effective feedback on cytology performance relies on navigating complex laboratory information system data, which is prone to errors and lacks flexibility. As a comprehensive solution, we used the Python programming language to create a dashboard application for screening and diagnostic quality metrics. Data from the 5-year period (2018-2022) were accessed. Versatile open-source Python libraries (user developed program code packages) were used from the first step of LIS data cleaning through the creation of the application. To evaluate performance, we selected 3 gynecologic metrics: the ASC/LSIL ratio, the ASC-US/ASC-H ratio, and the proportion of cytologic abnormalities in comparison to the total number of cases (abnormal rate). We also evaluated the referral rate of cytologists/cytotechnologists (CTs) and the ratio of thyroid AUS interpretations by cytopathologists (CPs). These were formed into colored graphs that showcase individual results in established, color-coded laboratory "goal," "borderline," and "attention" zones based on published reference benchmarks. A representation of the results distribution for the entire laboratory was also developed. We successfully created a web-based test application that presents interactive dashboards with different interfaces for the CT, CP, and laboratory management (https://drkvcsstvn-dashboards.hf.space/app). The user can choose to view the desired quality metric, year, and the anonymized CT or CP, with an additional automatically generated written report of results. Python programming proved to be an effective toolkit to ensure high-level data processing in a modular and reproducible way to create a personalized, laboratory specific cytology dashboard. • This study introduces an innovative approach to cytology quality assessment, using Python programming to create an interactive dashboard navigating complex laboratory information system (LIS) data. • The researchers conducted a comprehensive analysis of gynecologic and thyroid cytology metrics over a 5-year period, including ASC/LSIL and ASC-US/ASC-H ratio, abnormal rate, referral rate and thyroid AUS ratio. • The results are visually represented through colored graphs, categorizing individual outcomes, and offering a clear distribution across the laboratory. • The web-based application (https://drkvcsstvn-dashboards.hf.space/app) allows users to interact with personalized dashboards for cytologists/cytotechnologists, cytopathologists, and laboratory management, demonstrating Python's effectiveness as a toolkit for modular and reproducible data processing in creating tailored cytology quality assurance solutions. [ABSTRACT FROM AUTHOR]