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Updated: Jun 23, 2026
Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
Published on: July 11, 2025
Activity-based differentiation of pathologists' workload in surgical pathology
G A Meijer1, J J Oudejans, J J M Koevoets
1Department of Pathology, VU University Medical Center, Amsterdam, The Netherlands. ga.meijer@vumc.nl
This study introduces a new way to measure how much work pathologists do in a lab. The model looks at specific tasks like cutting tissue samples and examining slides under a microscope. It calculates how long each task takes based on the number of samples and slides involved. The researchers found that tasks like cutting and dictation take more time when there are more samples. They tested their model by comparing predicted workload to real measurements and found a strong match. This approach allows for better budget planning and resource allocation in pathology labs.
Area of Science:
- Pathology workload analysis in clinical diagnostics
- Healthcare resource allocation in laboratory medicine
Background:
Accurate budget control in pathology depends on reliable workload metrics. Traditional methods often overlook variations in specimen types and procedural changes. Prior research has shown that workload is influenced by specimen volume and procedural complexity. However, no prior work had resolved how to quantify workload shifts due to protocol changes. Existing models lack flexibility to adapt to evolving diagnostic practices. This gap motivated the need for a dynamic workload measurement system. No prior work had resolved how to integrate specimen-specific variables into workload calculations. This study addresses these limitations by introducing a new modeling approach.
Purpose Of The Study:
This study aimed to develop a workload model that adapts to specimen type and procedural changes. The goal was to create a flexible tool for resource allocation in pathology. The specific problem is the lack of dynamic workload metrics in pathology. The motivation comes from the need for accurate budget planning. Current methods fail to account for protocol variations. This approach allows workload to be calculated per specimen type. The model uses activity-based metrics to improve accuracy. It provides a framework for adjusting resource allocation as practices evolve.
Main Methods:
The diagnostic process was divided into distinct activities. Time spent on each activity was measured experimentally. Linear regression was used to model time per activity. Variables included number of slides and blocks per specimen. Standard protocols were used to calculate expected workload. Actual workload was measured for validation. Correlation between calculated and actual workload was tested. The model was refined to reflect real-world diagnostic workflows.
Main Results:
Cutting up and microscopic procedures correlated strongly with number of blocks. Dictation time also correlated with slide count per specimen. Calculated workload matched actual workload within statistical limits. The model showed high accuracy for different specimen types. Time estimates were derived from linear regression equations. Each activity’s time was a function of specimen complexity. Validation confirmed the model’s predictive power. The approach allows workload to be predicted based on specimen characteristics.
Conclusions:
The model provides a flexible framework for workload prediction in pathology. It accounts for changes in specimen types and protocols. Calculated workload correlates with actual measurements. This approach supports accurate resource allocation. The model can be adapted to different diagnostic settings. It allows for budget adjustments based on workload changes. The study demonstrates the feasibility of activity-based costing. The findings support the use of this model for pathology practice management.
Frequently Asked Questions
The model uses linear regression to estimate time per activity based on number of slides and blocks per specimen.
Cutting up, microscopic procedures, and dictation correlated highly with number of blocks and slides.
These variables directly influence time spent on cutting and microscopic examination per specimen.
Calculated workload was compared to actual measured workload across a range of specimen types.
Linear regression allows workload to be predicted as a function of specimen complexity metrics.
The model supports flexible resource allocation by predicting workload based on specimen characteristics.
