Elsevier

Gastrointestinal Endoscopy

Volume 75, Issue 6, June 2012, Pages 1233-1239.e14
Gastrointestinal Endoscopy

Original article
Clinical endoscopy
Applying a natural language processing tool to electronic health records to assess performance on colonoscopy quality measures

https://doi.org/10.1016/j.gie.2012.01.045Get rights and content

Background

Gastroenterology specialty societies have advocated that providers routinely assess their performance on colonoscopy quality measures. Such routine measurement has been hampered by the costs and time required to manually review colonoscopy and pathology reports. Natural language processing (NLP) is a field of computer science in which programs are trained to extract relevant information from text reports in an automated fashion.

Objective

To demonstrate the efficiency and potential of NLP-based colonoscopy quality measurement.

Design

In a cross-sectional study design, we used a previously validated NLP program to analyze colonoscopy reports and associated pathology notes. The resulting data were used to generate provider performance on colonoscopy quality measures.

Setting

Nine hospitals in the University of Pittsburgh Medical Center health care system.

Patients

Study sample consisted of the 24,157 colonoscopy reports and associated pathology reports from 2008 to 2009.

Main Outcome Measurements

Provider performance on 7 quality measures.

Results

Performance on the colonoscopy quality measures was generally poor, and there was a wide range of performance. For example, across hospitals, the adequacy of preparation was noted overall in only 45.7% of procedures (range 14.6%-86.1% across 9 hospitals), cecal landmarks were documented in 62.7% of procedures (range 11.6%-90.0%), and the adenoma detection rate was 25.2% (range 14.9%-33.9%).

Limitations

Our quality assessment was limited to a single health care system in western Pennsylvania.

Conclusions

Our study illustrates how NLP can mine free-text data in electronic records to measure and report on the quality of care. Even within a single academic hospital system, there is considerable variation in the performance on colonoscopy quality measures, demonstrating the need for better methods to regularly and efficiently assess quality.

Section snippets

Background

NLP is a field of computer science in which the computer is trained to “read” text to identify relevant data.20 C-QUAL automatically analyzes both colonoscopy and pathology reports in the electronic health record (EHR) and abstracts the necessary information (eg, indication, polyp detection, cecal intubation). It can thereby assess all the important aspects of colonoscopy quality. We tested C-QUAL by comparing it with the criterion standard of manual abstraction by a physician and found that

Methods

We conducted a cross-sectional analysis of reports from relevant colonoscopy procedures over a 2-year period in a single hospital system.

Results

A total of 24,157 reports were analyzed by using the NLP tool. Of all colonoscopies, 54.1% were performed on women and the majority of patients (59.0%) were between 50 and 69 years of age (Table 2). All 9 hospitals were in urban areas, and 4 were members of the Council of Teaching Hospitals. The number of admissions per year at the hospitals varied from 5000 to less than 10,000 (n = 2, 22.2%), 10,000 to less than 20,000 (n = 5, 55.6%), and 20,000 or more (n = 2, 22.2%) (Online Appendix Table 5

Discussion

Our results highlight the potential of NLP to measure performance on colonoscopy quality measures. Our NLP tool efficiently analyzed a large sample of colonoscopy reports. Our findings demonstrate that there is clear variation in performance, even within a highly regarded academic health care system. Across the 9 hospitals, there was almost a threefold variation in the adenoma detection rate. The variation in performance on the quality measures across physicians was even greater.

Previous work

References (32)

  • A. Sonnenberg et al.

    Cost-effectiveness of colonoscopy in screening for colorectal cancer

    Ann Intern Med

    (2000)
  • J.C. van Rijn et al.

    Polyp miss rate determined by tandem colonoscopy: a systematic review

    Am J Gastroenterol

    (2006)
  • H. Singh et al.

    Risk of developing colorectal cancer following a negative colonoscopy examination: evidence for a 10-year interval between colonoscopies

    JAMA

    (2006)
  • R.L. Barclay et al.

    Colonoscopic withdrawal times and adenoma detection during screening colonoscopy

    N Engl J Med

    (2006)
  • M.F. Kaminski et al.

    Quality indicators for colonoscopy and the risk of interval cancer

    N Engl J Med

    (2010)
  • J.S. Goodwin et al.

    Overuse of screening colonoscopy in the Medicare population

    Arch Intern Med

    (2011)
  • Cited by (0)

    DISCLOSURE: All authors disclosed no financial relationships relevant to this publication.

    If you would like to chat with an author of this article, you may contact Dr Mehrotra at [email protected].

    View full text