Objectives: Randomized controlled trials (RCTs) have shown that attention-deficit/hyperactivity disorder (ADHD) medications significantly reduce symptomatology at a group level, but individual response to ADHD medication is variable. Thus, developing prediction models to stratify treatment according to individual baseline clinicodemographic characteristics is crucial to support clinical practice. A potential valuable source of data to develop accurate prediction models is real-world clinical data extracted from electronic healthcare records (EHRs). Yet, systematic information regarding EHR data on ADHD is lacking. Methods: We conducted a comprehensive review of studies that included EHR reporting data regarding individuals with ADHD, with a specific focus on treatment-related data. Relevant studies were identified from PubMed, Ovid, and Web of Science databases up to February 24, 2024. Results: We identified 103 studies reporting EHR data for individuals with ADHD. Among these, 83 studies provided information on the type of prescribed medication. However, dosage, duration of treatment, and ADHD symptom ratings before and after treatment initiation were only reported by a minority of studies. Conclusion: This review supports the potential use of EHRs to develop treatment response prediction models but emphasizes the need for more comprehensive reporting of treatment-related data, such as changes in ADHD symptom ratings and other possible baseline clinical predictors of treatment response.
Keywords: attention-deficit/hyperactivity disorder (ADHD); electronic healthcare records (EHRs); personalizing treatment; pharmacological treatment; prediction model; treatment response.