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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.
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.
Background:
Mental, emotional, and behavioral disorders are chronic pediatric conditions, and their prevalence has been on the rise over recent decades. Affected children have long-term health sequelae and a decline in health-related quality of life. Due to the lack of a validated database for pharmacoepidemiological research on selected mental, emotional, and behavioral disorders, there is uncertainty in their reported prevalence in the literature.
Objectives:
We aimed to evaluate the accuracy of coding related to pediatric mental, emotional, and behavioral disorders in a large integrated health care system's electronic health records (EHRs) and compare the coding quality before and after the implementation of the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) coding as well as before and after the COVID-19 pandemic.
Methods:
Medical records of 1200 member children aged 2-17 years with at least 1 clinical visit before the COVID-19 pandemic (January 1, 2012, to December 31, 2014, the ICD-9-CM coding period; and January 1, 2017, to December 31, 2019, the ICD-10-CM coding period) and after the COVID-19 pandemic (January 1, 2021, to December 31, 2022) were selected with stratified random sampling from EHRs for chart review. Two trained research associates reviewed the EHRs for all potential cases of autism spectrum disorder (ASD), attention-deficit hyperactivity disorder (ADHD), major depression disorder (MDD), anxiety disorder (AD), and disruptive behavior disorders (DBD) in children during the study period. Children were considered cases only if there was a mention of any one of the conditions (yes for diagnosis) in the electronic chart during the corresponding time period. The validity of diagnosis codes was evaluated by directly comparing them with the gold standard of chart abstraction using sensitivity, specificity, positive predictive value, negative predictive value, the summary statistics of the F-score, and Youden J statistic. κ statistic for interrater reliability among the 2 abstractors was calculated.
Results:
The overall agreement between the identification of mental, behavioral, and emotional conditions using diagnosis codes compared to medical record abstraction was strong and similar across the ICD-9-CM and ICD-10-CM coding periods as well as during the prepandemic and pandemic time periods. The performance of AD coding, while strong, was relatively lower compared to the other conditions. The weighted sensitivity, specificity, positive predictive value, and negative predictive value for each of the 5 conditions were as follows: 100%, 100%, 99.2%, and 100%, respectively, for ASD; 100%, 99.9%, 99.2%, and 100%, respectively, for ADHD; 100%, 100%, 100%, and 100%, respectively for DBD; 87.7%, 100%, 100%, and 99.2%, respectively, for AD; and 100%, 100%, 99.2%, and 100%, respectively, for MDD. The F-score and Youden J statistic ranged between 87.7% and 100%. The overall agreement between abstractors was almost perfect (κ=95%).
Conclusions:
Diagnostic codes are quite reliable for identifying selected childhood mental, behavioral, and emotional conditions. The findings remained similar during the pandemic and after the implementation of the ICD-10-CM coding in the EHR system.
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