Adapting Electronic Medical Record to Measure Medical Outcomes in ASD Populations

项目总结/抽象。指导研究科学家发展奖（K99）将提供

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.