Next generation quality: Assessing the physician in clinical history completeness and diagnostic interpretations using funnel plots and normalized deviations plots in 3,854 prostate biopsies

• Main Authors: Michael Bonert, Ihab El-Shinnawy, Michael Carvalho, Phillip Williams, Samih Salama, Damu Tang, Anil Kapoor
• Format: Article
• Language: English
• Published: Wolters Kluwer Medknow Publications 2017-01-01
• Series: Journal of Pathology Informatics
• Subjects: Continuous quality improvement; data mining; funnel plots; Gleason score; grade groups; inter-rater variation; next generation quality; normalized deviations plots; prostate cancer; statistical process control
• Online Access: http://www.jpathinformatics.org/article.asp?issn=2153-3539;year=2017;volume=8;issue=1;spage=43;epage=43;aulast=Bonert

Background: Observational data and funnel plots are routinely used outside of pathology to understand trends and improve performance. Objective: Extract diagnostic rate (DR) information from free text surgical pathology reports with synoptic elements and assess whether inter-rater variation and clinical history completeness information useful for continuous quality improvement (CQI) can be obtained. Methods: All in-house prostate biopsies in a 6-year period at two large teaching hospitals were extracted and then diagnostically categorized using string matching, fuzzy string matching, and hierarchical pruning. DRs were then stratified by the submitting physicians and pathologists. Funnel plots were created to assess for diagnostic bias. Results: 3,854 prostate biopsies were found and all could be diagnostically classified. Two audits involving the review of 700 reports and a comparison of the synoptic elements with the free text interpretations suggest a categorization error rate of <1%. Twenty-seven pathologists each read >40 cases and together assessed 3,690 biopsies. There was considerable inter-rater variability and a trend toward more World Health Organization/International Society of Urologic Pathology Grade 1 cancers in older pathologists. Normalized deviations plots, constructed using the median DR, and standard error can elucidate associated over- and under-calls for an individual pathologist in relation to their practice group. Clinical history completeness by submitting medical doctor varied significantly (100% to 22%). Conclusion: Free text data analyses have some limitations; however, they could be used for data-driven CQI in anatomical pathology, and could lead to the next generation in quality of care.