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Development and Validation of an Automated Method to Identify Patients Undergoing Radical Cystectomy for Bladder

Hung-Jui Tan1,2, Robin Clarke3, Karim Chamie4

  • 1VA/UCLA Robert Wood Johnson Clinical Scholars Program, Los Angeles, California.

Urology Practice
|August 18, 2023
PubMed
Summary
This summary is machine-generated.

Natural language processing (NLP) accurately identifies radical cystectomy cases for bladder cancer quality improvement. NLP also better characterizes surgical details like robotic use and urinary diversion compared to claims data.

Keywords:
cystectomynatural language processingrobotic surgical proceduresurinary bladder neoplasmsurinary diversion

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Area of Science:

  • Urology
  • Health Informatics
  • Data Science

Background:

  • Accurate case identification is crucial for quality improvement in healthcare.
  • Electronic health records (EHRs) offer vast data, but require advanced methods for structured information extraction.
  • Natural Language Processing (NLP) can transform unstructured text in EHRs into structured data for enhanced quality measurement.

Purpose of the Study:

  • To evaluate the application of NLP for identifying and characterizing radical cystectomy procedures in bladder cancer patients.
  • To compare the performance of an NLP-enhanced algorithm against an administrative claims-based algorithm and manual chart review.

Main Methods:

  • A sample of 497 radical cystectomy procedures were analyzed.
  • Three methods were used for case identification: NLP-enhanced algorithm, administrative claims algorithm, and manual chart review.
  • Agreement was assessed using kappa statistic, sensitivity, specificity, and predictive values, with manual review as the reference standard.

Main Results:

  • Both NLP and claims-based algorithms accurately identified radical cystectomy cases (99.8% and 98.6% agreement, respectively).
  • NLP demonstrated superior performance in characterizing robotic surgery (98.8% vs. 90.5%) and continent urinary diversion (96.4% vs. 83.3%).
  • NLP achieved higher agreement (κ=0.976 and κ=0.924) for these specific surgical characteristics compared to claims data.

Conclusions:

  • NLP and claims-based algorithms are effective for identifying radical cystectomy cases.
  • NLP offers a distinct advantage in accurately classifying specific surgical details of cystectomy procedures.
  • NLP represents a promising methodology for advancing quality measurement in urological surgery.