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A framework for evaluating diagnostic discordance in pathology discovered during research studies
Sherry Feng1, Donald L Weaver, Patricia A Carney
1From the School of Medicine (Ms Feng), the Division of General Internal Medicine (Dr Reisch and Dr Elmore), and the Department of Anatomic Pathology (Dr Rendi), University of Washington, Seattle; the Departments of Pathology, College of Medicine, and the Vermont Cancer Center (Dr Weaver), Family Medicine and Radiology (Dr Geller), and Pathology (Dr Goodwin), University of Vermont, Burlington; the Departments of Family Medicine and Public Health & Preventive Medicine (Dr Carney) and Medical Informatics & Clinical Epidemiology and Medicine (Dr Nelson), Oregon Health and Science University, Portland; the Section of Biostatistics and Epidemiology (Dr Onega), and the Department of Community & Family Medicine (Dr Tosteson), Dartmouth College, Lebanon, New Hampshire; the Department of Pathology, Stanford University, Stanford, California (Dr Allison); Biostatistics Modeling and Methods (Mr Longton) and Biostatistics and Biomathematics (Dr Pepe), Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle.
Diagnostic discordance in research is common, with a new framework reducing initial findings from 32.2% to under 10%. This improves scientific data quality and patient care.
Area of Science:
- Pathology
- Medical Research
- Data Quality Assurance
Background:
- Diagnostic discordance in research studies is not well understood.
- Accurate diagnoses are crucial for reliable research outcomes.
Purpose of the Study:
- To characterize diagnostic discordance in breast biopsy research.
- To implement and evaluate a novel research framework for assessing diagnostic discrepancies.
Main Methods:
- A 5-step framework was developed to analyze diagnostic discordance.
- 407 breast biopsy cases were independently reviewed by a pathology expert.
- The framework involved comparing diagnoses, assessing clinical significance, correcting errors, and handling borderline cases.
Main Results:
- Initial discordance was 32.2%, reduced to 9.6% after applying the framework.
- Data errors accounted for 2.9% of initial discrepancies.
- Clinically meaningful discordance was found in 9.6% of cases, with 53% considered borderline.
Conclusions:
- A structured research framework effectively identifies and refines diagnostic discordance.
- Addressing diagnostic errors enhances the integrity of research data.
- Improved data accuracy from diagnostic concordance can advance patient care.
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