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An Expedited Chart Review Process for Large Database Studies Using Natural Language Processing and Multiwave Adaptive

Shirley V Wang1, Georg Hahn1, Sushama Kattinakere Sreedhara1

  • 1From the Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA.

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Summary
This summary is machine-generated.

This study introduces an efficient method for validating health outcome algorithms in large databases using natural language processing (NLP) and adaptive sampling. This approach significantly reduces chart review time and resources, improving the reliability of health research findings.

Keywords:
BayesianChart reviewMultiwave samplingNLPValidation study

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Area of Science:

  • Health Informatics
  • Biostatistics
  • Data Science in Healthcare

Background:

  • Validating code-based algorithms in large claims databases is crucial for enhancing analysis.
  • Manual chart review of electronic health records is time-consuming and resource-intensive.
  • Outcome misclassification can bias results in inferential studies.

Purpose of the Study:

  • To describe an expedited process for validating code-based algorithms.
  • To introduce efficiency through natural language processing (NLP) and adaptive sampling.
  • To illustrate the process with a case study validating an algorithm for intentional self-harm in patients with obesity.

Main Methods:

  • Utilized natural language processing (NLP) to reduce human reviewer time per chart.
  • Implemented a multi-wave adaptive sampling approach with pre-defined stopping criteria.
  • Validated a claims-based outcome algorithm for intentional self-harm in an obesity cohort.

Main Results:

  • The NLP-assisted annotation reduced review time per chart by 40%.
  • Adaptive sampling with a stopping rule would have avoided reviewing 77% of patient charts.
  • Sufficient precision of performance characteristics was maintained with limited compromise.

Conclusions:

  • The described approach facilitates routine validation of code-based algorithms.
  • This enhances the understanding of reliability in findings from database studies.
  • Improves the robustness of health research using large datasets.