Novel Quality Measures for Primary Care Management of Attention-Deficit/Hyperactivity Disorder

PROJECT SUMMARY / ABSTRACT Attention-Deficit/Hyperactivity Disorder (ADHD) affects 8-10% of US children. Primary care providers (PCPs) care for most children with ADHD but quality gaps in ADHD treatment, with sociodemographic disparities as a potential driver, may lead to life-long morbidity and/or unnecessary treatments. There is an urgent need to

develop quality measures for ADHD treatment, as a prerequisite for mitigating disparities and improving health outcomes. The objective of this proposal is to leverage recent advances in machine learning (ML) methods – enabling the analysis of electronic health record (EHR) data of an entire patient population – to develop robust

quality measures for ADHD treatment, and to prepare for quality improvement interventions. This K23 proposal will accelerate Dr. Bannett’s transition into an independent physician scientist, towards his long-term goal to improve community-based primary health care for children with developmental and behavioral disorders. His

multidisciplinary team of mentors include Heidi Feldman (ADHD research mentor), C. Jason Wang (health care technology & health services co-mentor), and Grace Lee (quality improvement & implementation science co- mentor). This nationally recognized team of physician scientists will assure Dr. Bannett achieves his goals, to

(1) apply machine learning techniques to assess quality of care while mitigating bias, (2) advance research skills in advanced statistics and in qualitative methods, (3) build expertise in quality improvement and implementation science methods, and (4) enhance professional skills and transition to independence. Dr.

Bannett’s clinical and research experiences, his mentoring team, and the environment at Stanford, position him to achieve the proposal’s aims. Building upon his experiences in analyzing EHR data and successes in piloting a natural language processing pipeline, Dr. Bannett has the following specific aims: (1) to develop guideline-

based quality measures that combine ML analysis of free text with structured EHR data to assess PCP treatment of children aged 4-11-years with ADHD, (2) to assess PCP adherence to evidence-based guidelines for ADHD treatment and to detect disparities in care and minimize related bias in ML models, (3) to prioritize

quality improvement interventions aimed at improving ADHD care and mitigating disparities that family and clinician stakeholders consider feasible, acceptable, and important. Aligned with the NIMH’s strategic plan, this proposal will (1) strengthen collaboration between stakeholders to continuously improve evidence-based

practices in primary care settings, (2) identify and prioritize targets for planned PCP- and systems-level quality improvement interventions aimed at standardizing ADHD care and mitigating disparities, and (3) apply novel technologies that provide real-time feedback and continuous monitoring of high-quality ADHD care. With future

R01 funding, Dr. Bannett will cross-validate developed quality measures in a national network of pediatric healthcare systems, and, in parallel, implement data-driven quality improvement interventions.