Attention-Deficit/Hyperactivity Disorder in 2- to 5-Year-Olds: A Primary Care Network Experience

Bannett, Yair; Feldman, Heidi M.; Gardner, Rachel; Bláha, Ondřej; Huffman, Lynne C.

doi:10.1016/j.acap.2020.04.009

Cited by 7 publications

(4 citation statements)

References 16 publications

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“…For this cohort study, we reviewed EHRs for children aged 48 to 71 months with 2 or more visits (from October 1, 2015, to December 31, 2019) at 1 of 10 primary care practices within the Packard Children’s Health Alliance, a community-based pediatric health care network in the San Francisco, California, area, as previously described . Patients with 1 or more ADHD-related visits, defined as a visit with International Classification of Disease, Tenth Revision ADHD diagnosis codes or symptom-level diagnosis codes (eg, hyperactivity), were included.…”

Section: Methodsmentioning

confidence: 99%

Rate of Pediatrician Recommendations for Behavioral Treatment for Preschoolers With Attention-Deficit/Hyperactivity Disorder Diagnosis or Related Symptoms

et al. 2022

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Section: Methodsmentioning

confidence: 99%

Rate of Pediatrician Recommendations for Behavioral Treatment for Preschoolers With Attention-Deficit/Hyperactivity Disorder Diagnosis or Related Symptoms

et al. 2022

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“…We reviewed patient records of children aged 4-6 years who had at least 2 visits during the examined period, and who had at least one visit in the study period with a disorder (ADHD) or symptom-level diagnosis code (e.g., hyperactivity, distractibility), as done in our previous study (n=423). 25 We excluded patients with a diagnosis of autism in the study period.…”

Section: Data Sourcesmentioning

confidence: 99%

Measuring Quality-of-Care in Treatment of Children with Attention-Deficit/Hyperactivity Disorder: A Novel Application of Natural Language Processing

Pillai¹,

Posada

Gardner

et al. 2023

Preprint

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Objective: To develop and validate a novel application of natural language processing (NLP) techniques to measure pediatrician adherence to evidence-based guidelines in the treatment of young children with attention-deficit/hyperactivity disorder (ADHD). Materials and Methods: We extracted structured and free text data from electronic health records of all office visits (2015-2019) of children aged 4-6 years seen in a community-based primary healthcare network in California, who had >1 visits with an ICD-10 diagnosis of ADHD. Two pediatricians manually annotated clinical notes of the first ADHD visit for 423 patients. Inter-annotator agreement was assessed for recommendation for first-line behavioral treatment; Disagreements were reconciled. The BioClinical Bidirectional Encoder Representations from Transformers (BioClinical-BERT) was used to identify mentions of behavioral treatment recommendations using a 70/30 train/test split. Following an error analysis and threshold selection, we completed external (temporal) validation by deploying the model on 1,020 unannotated notes representing other ADHD visits and well-care visits; all positively classified notes and 5% of negatively classified notes were annotated. Results: Of 423 included patients, 313 (74%) were male, 268 (63%) were privately insured; 138 (33%) were white; 61 (14%) were Hispanic. The BERT model of first ADHD visits achieved F1=0.78, precision=0.84, and recall=0.72. Following threshold selection, temporal validation on notes from other visits achieved F1=0.80, recall=0.92 and precision=0.7. Conclusion: Deploying a machine learning algorithm on a large and variable set of clinical notes accurately captured pediatrician adherence to guidelines in treatment of children with ADHD. This approach can be used to measure quality-of-care at scale and improve clinical care for various chronic medical conditions.

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“…7–9 The few studies that have assessed PCP adherence to AAP practice guidelines for ADHD medication management analyzed available EHR structured data (e.g., prescriptions, encounter dates) to assess medication prescription patterns and timely follow-up of patients. 10,11 Studies that aimed to gather more detailed information, such as evidence in the EHR narrative text that clinicians monitored medication benefits and side effects, have been limited in scope due to the need for labor-intensive chart reviews. 11,12…”

Section: Introductionmentioning

confidence: 99%

“…[7][8][9] The few studies that have assessed PCP adherence to AAP practice guidelines for ADHD medication management analyzed available EHR structured data (e.g., prescriptions, encounter dates) to assess medication prescription patterns and timely follow-up of patients. 10,11 Studies that aimed to gather more detailed information, such as evidence in the EHR narrative text that clinicians monitored medication benefits and side effects, have been limited in scope due to the need for labor-intensive chart reviews. 11,12 The current national quality metrics that aim to capture high-quality ADHD medication management -Healthcare Effectiveness Data and Information Set (HEDIS) measures -are claims-based metrics that are readily available for analysis and reporting at scale.…”

Section: Introductionmentioning

confidence: 99%

Leveraging a Large Language Model to Assess Quality-of-Care: Monitoring ADHD Medication Side Effects

Bannett,

Gunturkun,

Pillai

et al. 2024

Preprint

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Objective: To assess the accuracy of a large language model (LLM) in measuring clinician adherence to practice guidelines for monitoring side effects after prescribing medications for children with attention-deficit/hyperactivity disorder (ADHD). Methods: Retrospective population-based cohort study of electronic health records. Cohort included children aged 6-11 years with ADHD diagnosis and ≥2 ADHD medication encounters (stimulants or non-stimulants prescribed) between 2015-2022 in a community-based primary healthcare network (n=1247). To identify documentation of side effects inquiry, we trained, tested, and deployed an open-source LLM (LLaMA) on all clinical notes from ADHD-related encounters (ADHD diagnosis or ADHD medication prescription), including in-clinic/telehealth and telephone encounters (n=15,593 notes). Model performance was assessed using holdout and deployment test sets, compared to manual chart review. Results: The LLaMA model achieved excellent performance in classifying notes that contain side effects inquiry (sensitivity= 87.2%, specificity=86.3/90.3%, area under curve (AUC)=0.93/0.92 on holdout/deployment test sets). Analyses revealed no model bias in relation to patient age, sex, or insurance. Mean age (SD) at first prescription was 8.8 (1.6) years; patient characteristics were similar across patients with and without documented side effects inquiry. Rates of documented side effects inquiry were lower in telephone encounters than in-clinic/telehealth encounters (51.9% vs. 73.0%, p<0.01). Side effects inquiry was documented in 61% of encounters following stimulant prescriptions and 48% of encounters following non-stimulant prescriptions (p<0.01). Conclusions: Deploying an LLM on a variable set of clinical notes, including telephone notes, offered scalable measurement of quality-of-care and uncovered opportunities to improve psychopharmacological medication management in primary care.

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Attention-Deficit/Hyperactivity Disorder in 2- to 5-Year-Olds: A Primary Care Network Experience

Cited by 7 publications

References 16 publications

Rate of Pediatrician Recommendations for Behavioral Treatment for Preschoolers With Attention-Deficit/Hyperactivity Disorder Diagnosis or Related Symptoms

Rate of Pediatrician Recommendations for Behavioral Treatment for Preschoolers With Attention-Deficit/Hyperactivity Disorder Diagnosis or Related Symptoms

Measuring Quality-of-Care in Treatment of Children with Attention-Deficit/Hyperactivity Disorder: A Novel Application of Natural Language Processing

Leveraging a Large Language Model to Assess Quality-of-Care: Monitoring ADHD Medication Side Effects

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