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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.

项目概要/摘要 8-10% 的美国儿童患有注意力缺陷/多动症 (ADHD)。初级保健提供者 (PCP) 照顾大多数患有多动症的儿童，但多动症治疗的质量存在差距，社会人口统计学差异是潜在的驱动因素，可能导致终生发病和/或不必要的治疗。迫切需要制定多动症治疗的质量措施，作为缩小差距和改善健康的先决条件结果。该提案的目标是利用机器学习 (ML) 方法的最新进展 – 能够分析整个患者群体的电子健康记录 (EHR) 数据 – 开发强大的 ADHD 治疗的质量措施，并为质量改进干预措施做好准备。这个K23提案将加速 Bannett 博士向独立医师科学家的转变，实现他的长期目标改善针对患有发育和行为障碍的儿童的社区初级卫生保健。他的多学科导师团队包括 Heidi Feldman（多动症研究导师）、C. Jason Wang（医疗保健）技术与健康服务联合导师）和 Grace Lee（质量改进与实施科学联合导师）导师）。这个全国公认的医师科学家团队将确保班尼特博士实现他的目标， (1) 应用机器学习技术评估护理质量，同时减少偏见，(2) 推进研究高级统计和定性方法方面的技能，(3) 建立质量改进方面的专业知识和实施科学方法，以及（4）提高专业技能并过渡到独立。博士。班尼特的临床和研究经验、他的指导团队以及斯坦福大学的环境使他处于有利地位以实现提案的目标。基于他分析 EHR 数据的经验和试点的成功 Bannett 博士的自然语言处理流程有以下具体目标：（1）制定指南- 基于质量措施，将自由文本的 ML 分析与结构化 EHR 数据相结合来评估 PCP 治疗 4-11 岁 ADHD 儿童，(2) 评估 PCP 遵守循证指南的情况用于 ADHD 治疗并检测护理差异并最大程度地减少 ML 模型中的相关偏差，(3) 确定优先级质量改进干预措施旨在改善多动症护理并减少家庭和家庭之间的差异临床医生利益相关者认为可行、可接受且重要。与 NIMH 的战略计划相一致，提案将 (1) 加强利益相关者之间的合作，不断改进基于证据的初级保健机构的实践，(2) 确定计划的 PCP 和系统级质量目标并确定优先顺序旨在标准化多动症护理和减少差异的改进干预措施，以及（3）应用新的提供实时反馈和持续监测高质量多动症护理的技术。与未来班尼特博士将在 R01 资助下，在全国儿科网络中交叉验证制定的质量措施医疗保健系统，同时实施数据驱动的质量改进干预措施。