ArticleJournal of pathology and translational medicine2025
The Automatable Activity-Based Approach to Complexity Unit Scoring as a task-specific model approach to monetizing outcomes of pathology artificial intelligence solutions.
Article in Journal of pathology and translational medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Benign Biliary Tumors and Precursor Neoplasms: An Updated Clinicopathological and Molecular Review Based on the 2026 WHO Classification.Biomedicines · 2026Review
- Artificial intelligence in non-small cell lung cancer: transforming diagnosis, treatment, and prognostic evaluation.Frontiers in medicine · 2026Review
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3 authors.
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Abstract
backgroundCost-containment policies are increasingly affecting decision-making in healthcare. In this context, the need for monetization of digital health interventions has been recently emphasized. Previous studies have attempted to extrapolate cost containment in conjunction with the implementation of digital pathology solutions mostly on the basis of operational cost savings or diagnostic error reduction. However, no study has attempted to link a wider spectrum of potential diagnostic tasks performed by artificial intelligence algorithms to financial figures.
methodsHerein, we employ a workload measurement tool for the purpose of monetizing particular outcomes associated with the implementation of a pathology artificial intelligence solution. A hundred and thirty-two prostate core biopsy samples were encoded for workload using the Automatable Activity-Based Approach to Complexity Unit Scoring. Subsequently, avoided workload, full-time equivalent gains, and corresponding cost savings were calculated assuming full clinical deployment of a well-developed prostate cancer screening tool.
resultsFor a fixed percentage of negative cores and a steady yearly workload of prostate core biopsies, the estimated total avoided workload amounted to 4,291 complexity units per year, with an average avoidance of 16.25 complexity units per ascension number. The calculated full-time equivalent gains were 0.12, whereas projected cost savings were as high as €2,402.34 per year or €0.55 per complexity unit, which in turn would yield an average of €8.93 per ascension number.
conclusionsThe Automatable Activity-Based Approach to Complexity Unit Scoring appears to be a suitable economic evaluation tool for assessing the possible implementation of task-specific artificial intelligence solutions in a given histopathology laboratory or group of laboratories, considering it is a task-specific workload measurement tool per design.
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