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Time Required for Gross Examination of Routine Second and Third Trimester Singleton Placentas by Pathologists'
Christopher Horn1,2,3, Nicole Engel2,3, Dominique Drouin3
1Department of Pathology and Laboratory Medicine, University of Calgary Cumming School of Medicine, Calgary, AB, Canada.
Summary
Pathologists' Assistants (PAs) spent an average of 14.5 minutes grossing placentas. More blocks prepared correlated with longer grossing times, informing pathology workload models.
Area of Science:
- Anatomic Pathology
- Laboratory Medicine
- Pathology Workforce Management
Background:
- Workload measurement in anatomic pathology relies on specimen complexity and clinical significance.
- Pathologists' Assistants (PAs) are increasingly involved in gross examination, but task-specific time data is limited.
- Accurate time data is crucial for effective staffing and workload management in pathology labs.
Purpose of the Study:
- To determine the time required for PAs to perform gross examinations of second and third trimester singleton placentas.
- To provide objective data for pathology workload models.
- To establish a baseline for PA grossing times in a tertiary hospital setting.
Main Methods:
- Seven certified PAs grossed a minimum of 10 placentas each using a standardized protocol.
- Exclusion criteria included specimens requiring photography, ancillary studies, or immediate pathologist consultation.
- Data analyzed using mixed linear regression and multivariate analysis to assess factors influencing grossing time.
Main Results:
- The overall average grossing time for placentas was 14.5 minutes (95% CI: 11.7–17.3 minutes).
- Individual PA grossing times ranged from 11.0 to 17.8 minutes.
- Increasing the number of blocks prepared was significantly associated with longer grossing times.
Conclusions:
- This study provides the first objective data on PA grossing times for routine placentas.
- The methodology is replicable for other specimen types and settings.
- Findings offer valuable insights for refining current pathology workload models.
