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Developing model-based algorithms to identify screening colonoscopies using administrative health databases.

Maida J Sewitch1, Mengzhu Jiang, Lawrence Joseph

  • 1Department of Medicine, McGill University, Montreal, Quebec, Canada. maida.sewitch@mcgill.ca

BMC Medical Informatics and Decision Making
|April 12, 2013
PubMed
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Model-based algorithms using administrative data were not accurate enough for identifying screening colonoscopies. This study highlights a method for validating administrative data when a gold standard is unavailable.

Area of Science:

  • Health Services Research
  • Colorectal Cancer Screening
  • Health Informatics

Background:

  • Accurate identification of screening colonoscopies in administrative databases is crucial for monitoring colorectal cancer (CRC) screening and quality assurance.
  • Previous algorithms based on expert opinion lacked sufficient accuracy for this purpose.

Purpose of the Study:

  • To develop and evaluate the accuracy of model-based algorithms for identifying screening colonoscopies within health administrative databases.

Main Methods:

  • Recruited patients aged 50-75 from endoscopy units in Montreal and Calgary.
  • Utilized Bayesian latent class analysis on physician billing and hospitalization data to determine colonoscopy indication.
  • Applied multivariate logistic regression and recursive partitioning models to assess algorithm accuracy.

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Main Results:

  • The latent class model identified 554 screening exams from 1229 participants.
  • Multivariate logistic regression achieved an area under the curve of 0.786.
  • Recursive partitioning demonstrated 84.5% sensitivity and 63.3% specificity for identifying screening colonoscopies.

Conclusions:

  • Model-based algorithms using administrative data did not achieve sufficient accuracy for identifying screening colonoscopies.
  • The developed methodology for creating a latent reference standard can be valuable for validating administrative data in other contexts lacking a gold standard.