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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.

项目总结/摘要注意力缺陷多动障碍（ADHD）影响8-10%的美国儿童。初级保健提供者（PCP）照顾大多数患有ADHD的儿童，但ADHD治疗的质量差距，社会人口差异作为潜在驱动因素，可能导致终身发病和/或不必要的治疗。迫切需要制定ADHD治疗的质量措施，作为减少差异和改善健康的先决条件结果。该提案的目的是利用机器学习（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， ADHD治疗的质量措施，并为质量改进干预措施做好准备。K23提案这将加速Bannett博士向独立医生科学家的转变，实现他的长期目标，改善以社区为基础的对发育和行为失常儿童的初级保健。他多学科的导师团队包括海蒂费尔德曼（多动症研究导师），C。Jason Wang（医疗保健技术和健康服务共同导师）和Grace Lee（质量改进和实施科学共同导师），导师）。这个全国公认的医生科学家团队将确保Bannett博士实现他的目标， (1)应用机器学习技术评估护理质量，同时减轻偏见，（2）推进研究在先进的统计和定性方法的技能，（3）建立在质量改进的专业知识，实施科学方法;（4）提高专业技能，向独立性过渡。博士 Bannett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。根据他在分析电子健康记录数据和试点成功方面的经验，作为一个自然语言处理管道，Bannett博士有以下具体目标：（1）制定指导方针- 结合联合收割机对自由文本的ML分析和结构化EHR数据来评估PCP的质量度量治疗4-11岁ADHD儿童，（2）评估PCP对循证指南的依从性用于ADHD治疗，检测护理差异并最大限度地减少ML模型中的相关偏见，（3）优先考虑质量改善干预措施，旨在改善ADHD护理和减轻家庭和临床医生利益相关者认为可行、可接受和重要。根据NIMH的战略计划，建议将（1）加强利益攸关方之间的合作，以不断改进基于证据的初级保健环境中的实践，（2）确定计划的PCP和系统级质量目标并确定优先顺序改善干预措施，旨在规范ADHD护理和减轻差异，（3）应用新的提供高质量ADHD护理的实时反馈和持续监测的技术。与未来 R 01资金，Bannett博士将在全国儿科网络中交叉验证开发的质量措施。医疗保健系统，并同时实施数据驱动的质量改进干预措施。