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Root cause analysis of pre-microscopic errors in anatomical pathology using Eindhoven classification

Author(s)
Fakeha Rehman Department of Pathology, King Edward Medical University, Lahore, Pakistan.
Muhammad Saqlain Haider Department of Pathology, Tehsil Healthcare Unit, Nankana Sahib, Punjab, Pakistan
Rakhshindah Bajwa Department of Pathology, King Edward Medical University, Lahore, Pakistan.
Muhammad Raheel Anwar Department of Pathology, King Edward Medical University, Lahore, Pakistan.
Muhammad Zeeshan Sarwar East Surgical Ward, 7Student, Final Year MBBS, King Edward Medical University, Lahore, Pakistan.
Fatima Naumeri Pediatric Surgery Ward, King Edward Medical University, Mayo Hospital, Lahore, Pakistan.
Zayed Rashid King Edward Medical University, Lahore, Pakistan
Abstract
Objective: To evaluate pre-microscopic errors in anatomical pathology. Methods: The cross-sectional descriptive study was conducted at the Department of Pathology of a tertiary care hospital in Lahore, Pakistan, from September, 2016, to January, 2017, and comprised surgical pathology specimens. Errors were noted across the pre-microscopic process. Defects per million opportunities were calculated to determine sigma metric value in every step, from requisition to slide preparation. Root cause analysis was applied to the process of histology preparation to identify the root cause of each previously identified problem using Eindhoven classification. All errors were recorded on a pre-designed proforma. Results: There were 2420 specimens. While errors were encountered in all phases of the pre-microscopic process, but the (G6: n=1085, 44.83%), followed by requisition (R3: n=893, 36.9%) and cover slipping (C1: n=776, 32.06%). Conclusion: Development of standard procedures and protocols with staff training is likely to help in controlling the errors.
Publication Details
Page(s) 687-693
DOI DOI not available
Published Journal: Journal of Pakistan Medical Association, Volume: 70, Issue: 4, Year: 2020

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Keywords
Errors, Anatomical pathology, Eindhoven, six sigma metrics., Root cause analysis
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