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：
10525048
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/10525048
关键词：
Academy 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 Clinical Practice Guideline Clinical Research Code Collaborations Communities Consolidated Framework for Implementation Research Consumption Data Development Diagnosis Diagnostic Disease Management Electronic Health Record Environment Ethnic Origin Evidence based practice Family Feedback Funding Future Goals Guidelines Health Health Personnel Health Services Health Technology Healthcare Healthcare Systems Household Hybrids Individual Institutes Insurance Intervention Interview Language Lead Life Machine Learning 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 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 aged base behavioral health care delivery care providers career clinical care cost design developmental disease electronic structure evidence base evidence based guidelines experience health care disparity health care service organization implementation science improved machine learning method machine learning model maltreatment multidisciplinary neurobehavioral disorder new technology novel overtreatment patient population 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 /摘要注意力缺陷/多动症（ADHD）影响了8-10％的美国儿童。初级保健提供者（PCP）照顾大多数多动症儿童，但在多动症治疗中质量差异，社会人口统计学差异为潜在的驱动因素可能导致终身发病率和/或不必要的治疗方法。迫切需要制定多动症治疗的质量措施，是缓解分布和改善健康的先决条件结果。该建议的目的是利用机器学习（ML）方法的最新进展 - 启用整个患者人群的电子健康记录（EHR）数据的分析 - 发展强大多动症治疗的质量措施，并为质量改进干预做准备。这个K23提案将加速班内特博士向独立的物理科学家的过渡，以实现他的长期目标改善患有发育和行为障碍儿童的社区初级保健。他的多学科导师团队包括Heidi Feldman（ADHD研究导师），C。JasonWang（医疗保健）技术与卫生服务联合会）和格蕾丝·李（Grace Lee）（质量改进与实施科学合作（导师）。这个全国认可的身体科学家团队将向Bannett博士确保实现目标（1）应用机器学习技术来评估护理质量，同时减轻偏见，（2）提前研究高级统计和定性方法的技能，（3）在质量改进方面建立专业知识实施科学方法以及（4）增强了专业技能和向独立的过渡。博士 Bannett的临床和研究经验，他的心理团队以及斯坦福的环境，将他定位为了实现提案的目标。基于他在分析EHR数据的经验和驾驶成功的经验 Bannett博士是自然语言处理管道，具有以下具体目的：（1）制定准则 - 将自由文本分析与结构化EHR数据相结合的基于基于的质量度量来评估PCP 使用多动症4-11岁的儿童的治疗，（2）评估PCP遵守循证指南用于多动症治疗并检测护理中的差异并最大程度地减少ML模型中的相关偏差，（3）优先考虑质量改进干预措施旨在改善多动症护理和减轻家庭和分布临床利益相关者认为可行，可接受且重要。与NIMH的战略计划保持一致提案将（1）加强利益相关者之间的合作，以不断改善证据在初级保健环境中的实践，（2）确定并确定计划的PCP和系统级质量的目标旨在标准化多动症护理和减轻差异的改进干预措施，（3）应用新颖提供实时反馈和对高质量多动症护理的连续监控的技术。与未来 R01资金，Bannett博士将在儿科网络中跨越效应制定了质量措施医疗保健系统，并同时实施与数据驱动的质量改进干预措施。