Literature DB >> 16683877

Error reduction in surgical pathology.

Raouf E Nakhleh1.   

Abstract

CONTEXT: Because of its complex nature, surgical pathology practice is inherently error prone. Currently, there is pressure to reduce errors in medicine, including pathology.
OBJECTIVE: To review factors that contribute to errors and to discuss error-reduction strategies.
DESIGN: Literature review.
RESULTS: Multiple factors contribute to errors in medicine, including variable input, complexity, inconsistency, tight coupling, human intervention, time constraints, and a hierarchical culture. Strategies that may reduce errors include reducing reliance on memory, improving information access, error-proofing processes, decreasing reliance on vigilance, standardizing tasks and language, reducing the number of handoffs, simplifying processes, adjusting work schedules and environment, providing adequate training, and placing the correct people in the correct jobs.
CONCLUSIONS: Surgical pathology is a complex system with ample opportunity for error. Significant error reduction is unlikely to occur without a sustained comprehensive program of quality control and quality assurance. Incremental adoption of information technology and automation along with improved training in patient safety and quality management can help reduce errors.

Entities:  

Mesh:

Year:  2006        PMID: 16683877     DOI: 10.5858/2006-130-630-ERISP

Source DB:  PubMed          Journal:  Arch Pathol Lab Med        ISSN: 0003-9985            Impact factor:   5.534


  7 in total

1.  Metabolic imaging in multiple time scales.

Authors:  V Krishnan Ramanujan
Journal:  Methods       Date:  2013-09-04       Impact factor: 3.608

2.  Toponostics of invasive ductal breast carcinoma: combination of spatial protein expression imaging and quantitative proteome signature analysis.

Authors:  Claudia Röwer; Björn Ziems; Anngret Radtke; Oliver Schmitt; Toralf Reimer; Cornelia Koy; Hans-Jürgen Thiesen; Bernd Gerber; Michael O Glocker
Journal:  Int J Clin Exp Pathol       Date:  2011-02-28

3.  Multi-Class Classification of Breast Cancer Using 6B-Net with Deep Feature Fusion and Selection Method.

Authors:  Muhammad Junaid Umer; Muhammad Sharif; Seifedine Kadry; Abdullah Alharbi
Journal:  J Pers Med       Date:  2022-04-26

4.  Computational Algorithms that Effectively Reduce Report Defects in Surgical Pathology.

Authors:  Jay J Ye; Michael R Tan
Journal:  J Pathol Inform       Date:  2019-07-01

Review 5.  Big data in basic and translational cancer research.

Authors:  Peng Jiang; Sanju Sinha; Kenneth Aldape; Sridhar Hannenhalli; Cenk Sahinalp; Eytan Ruppin
Journal:  Nat Rev Cancer       Date:  2022-09-05       Impact factor: 69.800

6.  Deep Learning for Classification of Colorectal Polyps on Whole-slide Images.

Authors:  Bruno Korbar; Andrea M Olofson; Allen P Miraflor; Catherine M Nicka; Matthew A Suriawinata; Lorenzo Torresani; Arief A Suriawinata; Saeed Hassanpour
Journal:  J Pathol Inform       Date:  2017-07-25

7.  Deep Learning-Based Multi-Class Classification of Breast Digital Pathology Images.

Authors:  Weiming Mi; Junjie Li; Yucheng Guo; Xinyu Ren; Zhiyong Liang; Tao Zhang; Hao Zou
Journal:  Cancer Manag Res       Date:  2021-06-10       Impact factor: 3.989

  7 in total

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