Coding of Childhood Psychiatric and Neurodevelopmental Disorders in Electronic Health Records of a Large Integrated

Jiaxiao M Shi1, Vicki Y Chiu1, Chantal C Avila1

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

JMIR Mental Health
|May 21, 2024
PubMed

Insights

Diagnostic codes accurately identify pediatric mental health conditions, showing consistent reliability across different coding systems and the COVID-19 pandemic. This supports their use in research and clinical practice for conditions like autism spectrum disorder and ADHD.

Area of Science:

  • Pediatric Health
  • Health Informatics
  • Epidemiology

Background:

  • Rising prevalence of chronic pediatric mental, emotional, and behavioral disorders impacts children's long-term health and quality of life.
  • Uncertainty exists regarding prevalence estimates due to a lack of validated databases for pharmacoepidemiological research.
  • Accurate data is crucial for understanding and addressing the growing burden of these conditions in children.

Purpose of the Study:

  • To assess the accuracy of diagnostic coding for pediatric mental, emotional, and behavioral disorders within electronic health records (EHRs).
  • To compare coding quality before and after the transition to International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM).
  • To evaluate coding accuracy in the context of the COVID-19 pandemic.

Main Methods:

  • Chart review of 1200 children (aged 2-17) with selected mental health conditions (ASD, ADHD, MDD, AD, DBD).
  • Data collected from EHRs across pre-pandemic (ICD-9-CM and ICD-10-CM periods) and pandemic (post-COVID-19) eras.
  • Validity assessed by comparing diagnosis codes against gold-standard chart abstraction using sensitivity, specificity, PPV, NPV, F-score, and Youden J statistic.

Main Results:

  • Overall agreement between diagnosis codes and chart abstraction was strong and consistent across ICD-9-CM, ICD-10-CM, and pandemic/pre-pandemic periods.
  • High accuracy metrics (sensitivity, specificity, PPV, NPV) were observed for autism spectrum disorder (ASD), attention-deficit hyperactivity disorder (ADHD), major depression disorder (MDD), and disruptive behavior disorders (DBD).
  • Anxiety disorder (AD) coding showed strong performance but was relatively lower than other conditions; interrater reliability was almost perfect (κ=95%).

Conclusions:

  • Diagnostic codes demonstrate high reliability for identifying key pediatric mental, emotional, and behavioral disorders in EHRs.
  • Coding accuracy remained stable despite the implementation of ICD-10-CM and the challenges posed by the COVID-19 pandemic.
  • Findings support the utility of EHR diagnostic codes for pharmacoepidemiological research and clinical surveillance of childhood mental health conditions.
Abstract