The Automatable Activity–Based Approach to Complexity Unit Scoring as a task-specific model approach to monetizing outcomes of pathology artificial intelligence solutions
Background Cost-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 implementati...
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Korean Society of Pathologists & the Korean Society for Cytopathology
2025-07-01
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| Series: | Journal of Pathology and Translational Medicine |
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| Online Access: | http://www.jpatholtm.org/upload/pdf/jptm-2025-04-15.pdf |
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| author | Stavros Pantelakos Martha Nifora Georgios Agrogiannis |
| author_facet | Stavros Pantelakos Martha Nifora Georgios Agrogiannis |
| author_sort | Stavros Pantelakos |
| collection | DOAJ |
| description | Background Cost-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. Methods Herein, 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. Results For 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. Conclusions The 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. |
| format | Article |
| id | doaj-art-126a74c33bc54d44abdd441132015bba |
| institution | Kabale University |
| issn | 2383-7837 2383-7845 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Korean Society of Pathologists & the Korean Society for Cytopathology |
| record_format | Article |
| series | Journal of Pathology and Translational Medicine |
| spelling | doaj-art-126a74c33bc54d44abdd441132015bba2025-08-20T03:50:53ZengKorean Society of Pathologists & the Korean Society for CytopathologyJournal of Pathology and Translational Medicine2383-78372383-78452025-07-0159422523410.4132/jptm.2025.04.1517158The Automatable Activity–Based Approach to Complexity Unit Scoring as a task-specific model approach to monetizing outcomes of pathology artificial intelligence solutionsStavros Pantelakos0Martha Nifora1Georgios Agrogiannis2 1st Department of Pathology, School of Medicine, National and Kapodistrian University of Athens, Athens, Greece Istomedica Pathology Lab, Athens, Greece 1st Department of Pathology, School of Medicine, National and Kapodistrian University of Athens, Athens, GreeceBackground Cost-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. Methods Herein, 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. Results For 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. Conclusions The 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.http://www.jpatholtm.org/upload/pdf/jptm-2025-04-15.pdfartificial intelligencecapital financingdecision makingpathologyworkload |
| spellingShingle | Stavros Pantelakos Martha Nifora Georgios Agrogiannis The Automatable Activity–Based Approach to Complexity Unit Scoring as a task-specific model approach to monetizing outcomes of pathology artificial intelligence solutions Journal of Pathology and Translational Medicine artificial intelligence capital financing decision making pathology workload |
| title | The Automatable Activity–Based Approach to Complexity Unit Scoring as a task-specific model approach to monetizing outcomes of pathology artificial intelligence solutions |
| title_full | The Automatable Activity–Based Approach to Complexity Unit Scoring as a task-specific model approach to monetizing outcomes of pathology artificial intelligence solutions |
| title_fullStr | The Automatable Activity–Based Approach to Complexity Unit Scoring as a task-specific model approach to monetizing outcomes of pathology artificial intelligence solutions |
| title_full_unstemmed | The Automatable Activity–Based Approach to Complexity Unit Scoring as a task-specific model approach to monetizing outcomes of pathology artificial intelligence solutions |
| title_short | The Automatable Activity–Based Approach to Complexity Unit Scoring as a task-specific model approach to monetizing outcomes of pathology artificial intelligence solutions |
| title_sort | automatable activity based approach to complexity unit scoring as a task specific model approach to monetizing outcomes of pathology artificial intelligence solutions |
| topic | artificial intelligence capital financing decision making pathology workload |
| url | http://www.jpatholtm.org/upload/pdf/jptm-2025-04-15.pdf |
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