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Related Experiment Video

Updated: Apr 23, 2026

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Development and validation of algorithms to identify acute diverticulitis.

Aniket Kawatkar1, Li-Hao Chu, Rajan Iyer

  • 1Department of Research and Evaluation, Kaiser Permanente Southern California, Pasadena, CA, USA.

Pharmacoepidemiology and Drug Safety
|September 27, 2014
PubMed
Summary
This summary is machine-generated.

Developing accurate algorithms using electronic medical records (EMRs) can improve diverticulitis identification. Supplementing diagnosis codes with antibiotic prescriptions enhances case finding, though results vary by care setting.

Keywords:
algorithmsdiagnosis validationdiverticulitispharmacoepidemiologypositive predictive valuesensitivity

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Area of Science:

  • Medical Informatics
  • Gastroenterology
  • Health Services Research

Background:

  • Accurate identification of diverticulitis cases is crucial for effective patient management and resource allocation.
  • Electronic Medical Records (EMRs) offer a vast data source for developing clinical case-finding algorithms.
  • Existing methods for identifying diverticulitis in EMRs may have limitations in accuracy and completeness.

Purpose of the Study:

  • To develop and validate algorithms for precise diverticulitis patient identification using EMR data.
  • To assess the performance of algorithms across different care settings (inpatient, emergency department, outpatient).

Main Methods:

  • Utilized EMR data from Kaiser Permanente Southern California (2008-2009) for adults diagnosed with diverticulitis.
  • Developed and validated algorithms incorporating diagnosis codes, antibiotic prescriptions, CT scans, and patient history.
  • Manual chart review was performed to confirm diverticulitis diagnoses for algorithm validation.
  • Evaluated algorithm performance using sensitivity and positive predictive value (PPV).

Main Results:

  • An algorithm combining diverticulitis diagnosis codes with antibiotic prescriptions within 7 days showed strong performance.
  • In the validation sample, the algorithm achieved high sensitivity and PPV across care settings.
  • Specific performance metrics included: Outpatient (84.6% sensitivity, 98.2% PPV), Emergency Department (95.8% sensitivity, 98.1% PPV), and Inpatient (91.8% sensitivity, 82.6% PPV).

Conclusions:

  • Supplementing diagnostic codes with antibiotic prescription data significantly improves the accuracy of diverticulitis case identification in EMRs.
  • The effectiveness of these algorithms varies depending on the care setting.
  • These validated algorithms can enhance the precision of epidemiological studies and clinical management of diverticulitis.