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The Automatable Activity-Based Approach to Complexity Unit Scoring as a task-specific model approach to monetizing
Stavros Pantelakos1,2, Martha Nifora3,4, Georgios Agrogiannis1
11st Department of Pathology, School of Medicine, National and Kapodistrian University of Athens, Athens, Greece.
Journal of Pathology and Translational Medicine
|July 3, 2025
Summary
Artificial intelligence in pathology can be monetized by measuring workload. This study shows potential cost savings of over €2,400 annually per lab using AI for prostate cancer screening.
Area of Science:
- Digital pathology
- Artificial intelligence in healthcare
- Health economics
Background:
- Healthcare cost-containment policies necessitate monetizing digital health interventions.
- Previous studies focused on operational savings or error reduction for digital pathology.
- A comprehensive economic evaluation linking AI diagnostic tasks to financial figures is lacking.
Purpose of the Study:
- To monetize outcomes of implementing a pathology artificial intelligence (AI) solution.
- To quantify cost savings and efficiency gains from AI in prostate cancer screening.
Main Methods:
- Utilized the Automatable Activity-Based Approach to Complexity Unit Scoring to measure workload.
- Encoded 132 prostate core biopsy samples.
- Calculated avoided workload, full-time equivalent (FTE) gains, and cost savings for AI deployment.
Main Results:
- Estimated 4,291 complexity units of avoided workload annually.
- Projected FTE gains of 0.12.
- Calculated potential cost savings of €2,402.34 per year (€8.93 per case).
Conclusions:
- The Automatable Activity-Based Approach to Complexity Unit Scoring is a viable tool for economic evaluation of AI in histopathology.
- This method supports assessing the implementation of task-specific AI solutions in laboratories.
