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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.
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.
