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Jornal de Biomarcadores Moleculares e Diagnóstico

Study of Quality Assurance Programs in Anatomic Pathology that Drive Improved Proficiency, Reduce Cost and Enhance Positive Patient Outcomes

Abstract

Mark Priebe MT

Objective: To review the frequency and related impact of interpretive errors in anatomic pathology and how quality assurance (QA) programs measure in their ability to help reduce diagnostic interpretive error in surgical pathology.

Design: From an extensive number of published studies, the rate of major discrepancies identified for cancer patients referred to another institution occur from 4.6% to 14.7%, depending on type of tissue. However published data indicates the current intra-lab QA programs ability to detect these discrepancies is only 0.8% to 1.7%. To help understand the cause of this gap, four formal quality assurance case review programs both inter and intra-lab were reviewed for their ability to satisfy a set of selected design attributes known to help identify interpretive error. Peer reviewed literature was researched to support claims for each program percent compliance to the attributes, strengths, drawbacks, and best demonstrated practices were identified.

Results: No program met the selected attribute listing 100%, compliance ranged from 29% (met 2 of 7) to 86% (met 6 of 7) for each program.

Conclusion: Laboratories should be aware of the limitations of each QA program and take into consideration their case and pathologist mix and current on-site concerns, select a program with attributes that best match their QA needs. In general, programs that are standardized, include external review by subspecialist and are performed close to the final sign-out date may offer the greatest amount of error discovery and potential to positively influence patient outcomes and continuous improvement. Although this study focused on discordance in cancer related surgical pathology, case review can also be an effective tool in discovery of all histology/cytology diagnostic and clerical discrepancies.

Isenção de responsabilidade: Este resumo foi traduzido usando ferramentas de inteligência artificial e ainda não foi revisado ou verificado

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