JMIR medical informatics · 2026
Mining and Mapping 25 Years of Medication Use in Child and Adolescent Mental Health Services: Contact-Level Descriptive Analysis of Electronic Health Records.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Pant Dipendra, Clausen Carolyn, Leventhal Bennett L, Røst Thomas Brox, Koposov Roman, Westbye Odd Sverre, Arntzen Jostein, Skokauskas Norbert, Nytrø Øystein
Аннотация
BACKGROUND: Norwegian Child and Adolescent Mental Health Services (CAMHS) use the World Health Organization's (WHO) multiaxial diagnostic system based on the International Classification of Diseases, Tenth Revision (ICD-10); however, analysis of prescribing patterns among axes I-III is underexplored in electronic health records (EHRs) with intertwined patient, episode of care, and contact information.
OBJECTIVE: This study aimed to develop and demonstrate an analytic pipeline for mining and mapping information from EHRs to facilitate understanding of clinical processes and support informed decision-making. This study used the Norwegian CAMHS EHR data to identify common diagnoses, comorbidities, and medication use across axes I-III per individual contact.
METHODS: We extracted records of patients ≤19 years old with a primary mental health diagnosis on axes I-III and one or more medications per individual contact. Diagnoses were categorized according to ICD-10 and medications according to the Anatomical Therapeutic Chemical (ATC) classification system. Descriptive analyses quantified contact counts, diagnosis frequency, comorbidity rates, and medication frequency within each diagnostic category. Next, we mapped the medications used across all the contacts and noncomorbid contacts separately along each axis.
RESULTS: Of 7214 prescribing contacts (axis I: n=7179, 99.51%; axis II: n=821, 11.38%; axis III: n=65, 0.90%), comorbidity was present in 12.06% (n=866) contacts in axis I, 96.10% (n=789) contacts in axis II, and 96.92% (n=63) contacts in axis III. Leading diagnoses were behavioral-emotional disorders (ICD-10 codes F90-F98) in axis I, school skills and learning difficulties (ICD-10 code F81) in axis II, and mild mental retardation (ICD-10 code F70) in axis III. Most observed comorbidities were F90-F98 with speech and language development disorder (ICD-10 code F80), ICD-10 code F81, and mixed specific skills development disorder (ICD-10 code F83). Psychostimulants predominated across all diagnosis axes, with methylphenidate being the most common. For other ATC categories, the most commonly prescribed medications were antidepressants (sertraline and fluoxetine), antipsychotics (risperidone and aripiprazole), hypnotics and sedatives (melatonin), antiepileptics (lamotrigine), anxiolytics (diazepam), and nonpsychotropics (laxatives, vitamins, and supplements). Medication profiles varied minimally by axis or comorbidity status.
CONCLUSIONS: We demonstrated a mining and mapping analytic pipeline for EHRs to analyze diagnoses, comorbidities, and prescribing practices at the individual contact level. In the Norwegian CAMHS, axis I diagnoses are common, often behavioral-emotional disorders. Among the medications, psychostimulants and antidepressants are common. Beyond characterizing diagnoses and medication prescribing patterns, the study presents an approach for mining and mapping EHR data to analyze and provide service-level metrics, as well as clinical practice insights.
OBJECTIVE: This study aimed to develop and demonstrate an analytic pipeline for mining and mapping information from EHRs to facilitate understanding of clinical processes and support informed decision-making. This study used the Norwegian CAMHS EHR data to identify common diagnoses, comorbidities, and medication use across axes I-III per individual contact.
METHODS: We extracted records of patients ≤19 years old with a primary mental health diagnosis on axes I-III and one or more medications per individual contact. Diagnoses were categorized according to ICD-10 and medications according to the Anatomical Therapeutic Chemical (ATC) classification system. Descriptive analyses quantified contact counts, diagnosis frequency, comorbidity rates, and medication frequency within each diagnostic category. Next, we mapped the medications used across all the contacts and noncomorbid contacts separately along each axis.
RESULTS: Of 7214 prescribing contacts (axis I: n=7179, 99.51%; axis II: n=821, 11.38%; axis III: n=65, 0.90%), comorbidity was present in 12.06% (n=866) contacts in axis I, 96.10% (n=789) contacts in axis II, and 96.92% (n=63) contacts in axis III. Leading diagnoses were behavioral-emotional disorders (ICD-10 codes F90-F98) in axis I, school skills and learning difficulties (ICD-10 code F81) in axis II, and mild mental retardation (ICD-10 code F70) in axis III. Most observed comorbidities were F90-F98 with speech and language development disorder (ICD-10 code F80), ICD-10 code F81, and mixed specific skills development disorder (ICD-10 code F83). Psychostimulants predominated across all diagnosis axes, with methylphenidate being the most common. For other ATC categories, the most commonly prescribed medications were antidepressants (sertraline and fluoxetine), antipsychotics (risperidone and aripiprazole), hypnotics and sedatives (melatonin), antiepileptics (lamotrigine), anxiolytics (diazepam), and nonpsychotropics (laxatives, vitamins, and supplements). Medication profiles varied minimally by axis or comorbidity status.
CONCLUSIONS: We demonstrated a mining and mapping analytic pipeline for EHRs to analyze diagnoses, comorbidities, and prescribing practices at the individual contact level. In the Norwegian CAMHS, axis I diagnoses are common, often behavioral-emotional disorders. Among the medications, psychostimulants and antidepressants are common. Beyond characterizing diagnoses and medication prescribing patterns, the study presents an approach for mining and mapping EHR data to analyze and provide service-level metrics, as well as clinical practice insights.