A Novel Natural Language Processing Tool Improves Colonoscopy Auditing of Adenoma and Serrated Polyp Detection Rates.

Abstract

Background and study aims: Determining adenoma detection rate (ADR) and serrated polyp detection rate (SDR) can be challenging as they usually involve manual matching of colonoscopy and histology reports. This study aimed to validate a Natural Language Processing (NLP) code that enables rapid and efficient data extraction to calculate ADR and SDR.

Design: A NLP code was developed to automatically extract colonoscopy quality indicators from colonoscopy and histology reports at a tertiary health service. These reports were manually reviewed to verify the concordance of ADR and SDR between the two methods. This process was applied in the initial training phase, repeated following modification of the code, and again with a validation cohort.

Results: Included in the training and test phases were 5911 colonoscopies, with 2022 in the validation phase. The NLP code extracted patient names with 99.9% concordance and had a 98.9% accuracy in ADR and SDR in the training phase. Search terms were subsequently modified to take into consideration spelling variations and overlapping terminologies. Using data from the same cohort, accuracy of the NLP improved to 100%, excluding four colonoscopies that had missing histology reports in the test phase. Using a validated cohort, NLP had a 99.9% accuracy in ADR and SDR. The total time taken for auditing using NLP in the validation phase was less than 1 h.

Conclusions: An automatic NLP code had an accuracy of almost 100% in determining ADR and SDR in a tertiary colonoscopy service. Wider adoption of NLP enables significant improvements in colonoscopy audits that is accurate and time efficient.