ArticleCytopathology : official journal of the British Society for Clinical Cytology2026
Application and Impact of Quality Assurance Dashboards in Cytology Laboratories-The CytoLog Application.
Article in Cytopathology : official journal of the British Society for Clinical Cytology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
introductionPerformance feedback aims to improve quality, yet challenges in data selection, presentation and management of human interactions persist. Interactive dashboards summarising key performance indicators (KPIs) and fostering active engagement may enhance feedback. This study presents our experience with the CytoLog application-an updated version of the pilot dashboard at Eurofins-Medserv Laboratory (EML)-and pilot results at the Cytology Laboratory, Kenézy Gyula Campus, University of Debrecen (CLUD). MATERIAL AND
methodsData extraction, processing (including KPI calculation), and dashboard development were performed in Python and deployed as a web-based application. API endpoints were utilised to ensure secure, anonymized patient data access and user-specific dashboards. CytoLog introduces data tables, trend charts, and a quality gauge as additions to the pilot dashboard and supports quarterly updates.
resultsAt EML, 1,045,034 Pap tests and 14,227 thyroid cytology cases (2018-2024) were examined, involving 11 cytopathologists (CPs) and 23 cytotechnologists (CTs). A gratifying improvement in the ASC/LSIL ratio (0.8 to 1.7) was observed, and the ASC-US/ASC-H ratio corrected between 2023 and 2024 (75.7%/24.3% to 80.3%/19.7%). The abnormal rate decreased (6.6% to 3.5%). At CLUD, 198,663 Pap tests (2020-2024) were examined, involving 3 CPs and 10 CTs. A decrease in both the abnormal rate (8.3% to 5.3%) and the ASC/LSIL ratio (6.6 to 3.2) was observed.
conclusionThe CytoLog application enables continuous performance monitoring while ensuring secure data management and adaptability across different laboratory environments. The improvement of the ASC/LSIL and ASC-US/ASC-H ratios at EML testify to the impact of self-directed quality improvement even without structured interventions. Awareness of metric limitations and potential bias remains essential when interpreting data-driven trends.
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