ArticlePloS one2022
Pathologist workload, work distribution and significant absences or departures at a regional hospital laboratory.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
6 citing papers in PubMed.
- DeepLuAd: Semantic-guided virtual histopathology of lung adenocarcinoma via stimulated Raman scattering.Theranostics · 2026Article
- Deep learning model targeting cancer surrounding tissues for accurate cancer diagnosis based on histopathological images.Journal of translational medicine · 2025Article
- AI improves accuracy, agreement and efficiency of pathologists for Ki67 assessments in breast cancer.Scientific reports · 2024Article
- Neuroanatomical location of lung cancer brain metastases in 234 patients with a focus on cancer subtyping and biomarkers.PloS one · 2024Article
- Artificial Intelligence in Digital Pathology for Bladder Cancer: Hype or Hope? A Systematic Review.Cancers · 2023Review
- Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveAssess the work environment of salaried pathologists via (1) the national workload system (L4E), (2) work distribution among/in three hospital groups, and (3) the frequency of significant absences or departures (SADs).
methodsAutomated analysis of pathology reports from a regional laboratory (accessioned 2011-2019) using validated computer code.
resultsThe study set contained 574,099 pathology reports, reported by 63 pathologists. The average yearly L4E workload units/full-time equivalent for three hospital groups were 8,101.6, 6,906.5 and 4,215.8. The average Gini coefficient for full-time pathologists in the three hospital groups were respectively 0.05, 0.16 and 0.23. The average yearly SADs rates were respectively 13%, 16% and 9%. The group with the highest SADs rate had the intermediate Gini coefficient and intermediate workload.
conclusionsHigh individual workload and work maldistribution appear to be associated with SADs. Individual workload maximums and greater transparency may be essential for limiting staff turnover, maintaining high morale, and efficient laboratory function with a high quality of care.
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Registered trials
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