Evidence map›Paper›PMID 42303249›Full record

ArticleJMIR medical informatics2026

Mining and Mapping 25 Years of Medication Use in Child and Adolescent Mental Health Services: Contact-Level Descriptive Analysis of Electronic Health Records.

Dipendra Pant, Carolyn Clausen, Bennett L Leventhal, Thomas Brox Røst, Roman Koposov, Odd Sverre Westbye, Jostein Arntzen, Norbert Skokauskas, Øystein Nytrø

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Article in JMIR medical informatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

9 authors.

Dipendra PantDepartment of Computer Science, Norwegian University of Science and Technology, Trondheim, Trøndelag, Norway.ORCID 0000-0001-5643-7541
Carolyn ClausenDepartment of Mental Health, Regional Centre for Child and Youth Mental Health and Child Welfare (RKBU Central Norway), Norwegian University of Science and Technology, Trondheim, Trøndelag, Norway.ORCID 0000-0003-2121-7252
Bennett L LeventhalPsychiatry and Behavioral Neuroscience, The University of Chicago, Chicago, IL, United States.ORCID 0000-0001-6985-3691
Thomas Brox RøstDepartment of Computer Science, Norwegian University of Science and Technology, Trondheim, Trøndelag, Norway.ORCID 0000-0001-8395-7868
Roman KoposovRegional Centre for Child and Youth Mental Health and Child Welfare (RKBU North), UiT The Arctic University of Norway, Tromsø, Troms, Norway.ORCID 0000-0002-7393-2920
Odd Sverre WestbyeDepartment of Mental Health, Regional Centre for Child and Youth Mental Health and Child Welfare (RKBU Central Norway), Norwegian University of Science and Technology, Trondheim, Trøndelag, Norway.ORCID 0009-0008-9780-6022
Jostein ArntzenDepartment of Child and Adolescent Psychiatry, Clinic of Mental Health Care, St Olav's University Hospital, Trondheim, Trøndelag, Norway.ORCID 0009-0007-2440-3524
Norbert SkokauskasDepartment of Mental Health, Regional Centre for Child and Youth Mental Health and Child Welfare (RKBU Central Norway), Norwegian University of Science and Technology, Trondheim, Trøndelag, Norway.ORCID 0000-0002-9352-3489
Øystein NytrøDepartment of Computer Science, Norwegian University of Science and Technology, Trondheim, Trøndelag, Norway.ORCID 0000-0002-8163-2362

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundNorwegian 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.

objectiveThis 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.

methodsWe 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.

resultsOf 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.

conclusionsWe 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.

Indexed as

Data MiningElectronic Health RecordsMental DisordersMental Health ServicesAdolescentChildChild, PreschoolFemaleHumansInternational Classification of DiseasesMaleNorwayadolescentschildrenclinical decision supportdata miningdiagnosiselectronic health recordsknowledge representationmedicationmental health

Identifiers

PMID42303249
PMCPMC13320007

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