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

注意力缺陷/多动障碍初级保健管理的新质量措施

基本信息

批准号：
10686112
负责人：
Yair Bannett
金额：
$ 19.41万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-08-18 至 2027-07-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10686112
关键词：
Academy Acceleration Adoption Affect Algorithms American Area Attention deficit hyperactivity disorder Behavior Disorders Behavior Therapy Caring Characteristics Child Child Care Child Health Child Mental Health Child health care Childhood Classification Clinical Clinical Practice Guideline Code Collaborations Communities Consolidated Framework for Implementation Research Consumption Data Development Diagnosis Diagnostic Disparity Electronic Health Record Environment Equity Ethnic Origin Evidence based practice Family Feedback Funding Future Goals Guidelines Health Health Personnel Health Services Health Technology Healthcare Healthcare Systems Household Hybrids Individual Insurance Intervention Interview Language Machine Learning Managed Care Manuals Measurement Measures Medical Mental Health Mental disorders Mentors Methods Modeling Monitor Morbidity - disease rate National Institute of Mental Health Natural Language Processing Natural Language Processing pipeline Outcome Patient-Focused Outcomes Patients Pediatrics Performance Pharmaceutical Preparations Physicians Population Positioning Attribute Primary Care Primary Health Care Process Publishing Qualitative Methods Quality Indicator Quality of Care Race Recommendation Reduce health disparities Research Sampling Scientist Standardization Strategic Planning Structure Subgroup System Techniques Text Time Training Variant Work acceptability and feasibility aged behavioral health care delivery care providers career clinical care cost design developmental disease disparity reduction electronic structure evidence base evidence based guidelines experience health care disparity health care service organization implementation science improved learning engagement machine learning method machine learning model maltreatment multidisciplinary neurobehavioral disorder new technology novel overtreatment patient population primary care provider primary care setting provider adherence side effect skills sociodemographic disparity statistics structured data success support tools unnecessary treatment

项目摘要

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.

项目摘要/摘要