Adapting Electronic Medical Record to Measure Medical Outcomes in ASD Populations

项目摘要/摘要。这项指导研究科学家发展奖(K99)将提供 具备实现当前职业目标所需的高级培训和技能的申请者； 制定一个独立的研究计划，重点是改善儿童的医疗治疗 通过比较有效性研究(CER)确定自闭症谱系障碍(ASD) 适当的医疗干预。从长远来看，这一预测产生的经验和数据是 预计将导致使用使用电子产品创建的多站点数据仓库实施CER的更大规模的研究 医疗记录。 研究背景和意义。自闭症谱系障碍(ASD)以社交障碍为特征 互动和交流，以及受限、重复和刻板的行为模式，影响 每1000名儿童中有11.3人(88人中有1人)。目前，人们对以医院为基础的 自闭症患者的门诊医疗服务利用模式。有限的研究，样本量小 检查从精神病学/心理学、神经病学等专业寻求的伴随治疗； 发育行为儿科学；临床遗传学；胃肠病学和营养学；内分泌学；以及 过敏/免疫学，但没有提供治疗效用的估计，更不用说可靠的数据了 制定标准实践指南所需的信息。这项研究的目标是创造必要的 数据仓库支持ASD患者内科治疗效果的比较研究。 研究计划。拟议研究的具体目标包括： 目标1：开发和试点使用电子病历(EMR)捕获和 测量自闭症谱系障碍(ASD)患者的医疗利用模式，包括 初级护理、专科和紧急护理/急诊科使用。开发的技术和 将利用在初步研究中获得的经验来进行数据收集。目标1将评估 通过描述性分析确定电子数据捕获的可行性和有效性，并进行验证检查 使用自闭症发现研究所(ADI)的患者图表并确定数据的完整性。 目的2：测量ASD患者的利用模式，并利用聚类分析确定健康 护理利用情况亚组。主要结果是枚举利用率并定义 寻求治疗。分层将被用来按年龄组和按合作伙伴检查医疗接触 病态。将使用聚类分析来检查分组模式的数据。身份识别 分组是设计未来比较分析的初步步骤。 目的3：在电子病历中实施和评估一个预期的患者识别标记，以增强 全面的数据提取和提高注册表条目的稳健性。遭遇的百分比 在电子病历标记之前由数据提取捕获的数据将与之后的遭遇百分比进行比较 在已知人群中实施。流程评估将评估可接受性、易用性 记录定制的实施和覆盖范围。 概念目标R00：1)评估提供者计算机化医嘱集和病历数据的使用 核对表以加强数据收集；2)对患有以下疾病的患者进行药物治疗的CER 以及3)扩大电子病历的使用，以创建一个前瞻性的、纵向的医疗数据仓库 处理利用率。 培训目标。为了实现这些研究目标，申请者需要接受以下培训：1) 掌握ASD治疗方法的技能和专业知识，以便更好地确定分析的重点 并成为该领域独立的、成熟的调查员；2)获得以下技能和专业知识 利益攸关方参与和评价卫生干预措施，包括开展形成性研究； 措施评估，以及成功实施有效的卫生干预措施 3)通过指导、培训和培训为独立的研究生涯培养技能 在赠款申请和管理、研究设计、评估、培训和指导方面的经验 研究人员、手稿出版和研究中的道德行为。培训活动将包括：a) 咨询Stahmer博士(ASD和健康差距)，Connelly博士(利益相关者参与)，Lighter (信息技术)、谢尔(儿科药品)、麦金农(循证研究)和 Symen(统计)；b)焦点小组观察；c)课程作业(生物信息学、纵向分析、临床 决策分析)；和d)参加专业组织和会议；e)手稿 发展。

Project Summary/Abstract. This Mentored Research Scientist Development Award (K99) will provide the applicant with the advanced training and skills necessary to meet immediate career goals; specifically developing an independent program of research focusing on improving the medical treatment of children with autism spectrum disorders (ASD) by using comparative effectiveness research (CER) to determine the most appropriate medical interventions. In the long term, the experience and data generated by this projected are expected to lead to larger studies implementing CER using multi-site data warehouses created using electronic medical records. Background and Significance. Autism spectrum disorder (ASD) is characterized by impairments in social interaction and communication along with restricted, repetitive, and stereotyped patterns of behavior, affecting as many as 11.3 per 1,000 (one in 88) children. Currently, little is known about the hospital-based and outpatient health care utilization patterns of patients with ASD. Limited studies, with small sample sizes have examined concomitant treatments sought from specialties such as Psychiatry/Psychology; Neurology; Developmental-Behavioral Pediatrics; Clinical Genetics; Gastroenterology and Nutrition; Endocrinology; and Allergy/Immunology, but have not provided estimates of the utility of the treatments let alone robust data needed for development of standard practice guidelines. The goal of this research is to create the necessary data warehouse to support the comparative effectiveness research of medical treatments in patients with ASD. Research Plan. The specific aims of the proposed research include: Aim 1: To develop and pilot test methods for using an Electronic Medical Record (EMR) to capture and to measure medical treatment utilization patterns among patients with autism spectrum disorder (ASD) including primary care, specialty, and urgent care/emergency department use. The techniques developed and experience gained in the preliminary studies will be employed to perform the data capture. Aim 1 will assess the feasibility and validity of electronic data capture through descriptive analysis, and perform validation checks using Autism Discovery Institute (ADI) patient charts and determining data completeness. Aim 2: To measure utilization patterns among patients with ASD and to identify, using cluster analysis, health care utilization subgroups. The primary outcome is enumerating utilization rates and defining the scope of treatment sought. Stratification will be employed to examine medical encounters by age groups and by co- morbidities. Cluster analysis will be used to examine the data for subgroup patterns. Identification of subgroups is a preliminary step to design future comparative analyses. Aim 3: To implement and evaluate a prospective patient identification marker in the EMR to enhance comprehensive data extraction and to improve the robustness of registry entries. Percent of encounters captured by the data extraction before the EMR marker will be compared against percent encounters after implementation within a known population. Process evaluation will assess acceptability, ease of implementation, and reach of the record customization. Conceptual Aims R00: 1) To evaluate the use of provider computerized order sets and medical history data checklists to enhance data capture; 2) To conduct CER of pharmaceutical treatments among patients with ASD; and 3) To expand use of the EMR to create a prospective, longitudinal data warehouse of medical treatment utilization. Training Goals. In order to accomplish these research aims, the applicant requires the following training: 1) Acquire skills and expertise in ASD treatment approaches in order to better determine the focus of analyses and to become an independent, established investigator in the field; 2) Obtain the skills and expertise in stakeholder engagement and evaluation of health interventions, including conducting formative research, measure evaluation, and the successful implementation of effective health interventions with diverse populations; and 3) Build skills for an independent research career through mentorship, training, and experience in grant applications and administration, research design, evaluation, training and mentoring research staff, manuscript publication, and ethical conduct in research. Training activities will include: a) consultation with Drs. Stahmer (ASD and health disparities), Connelly (stakeholder engagement), Lighter (information technology), Schell (pediatric pharmaceuticals), MacKinnon (evidence-based research), and Slymen (statistics); b) focus group observation; c) course work (bioinformatics, longitudinal analysis, clinical decision analysis); and d) participation in professional organizations and conferences; and e) manuscript development.