Developing a Clinical Decision Support Tool that Assesses Risk of Opioid Use Disorder Using Natural Language Processing, Machine Learning, and Social Determinants of Health from Clinical Notes

开发一种临床决策支持工具，利用自然语言处理、机器学习和临床记录中的健康社会决定因素来评估阿片类药物使用障碍的风险

• 批准号: 10675434
• 负责人: William Brown
• 金额: $ 18.64万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-15 至 2026-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10675434
• 关键词: Address; Affect; American; Assessment tool; Caring; Clinical; Code; Computerized Medical Record; County; Data; Data Scientist; Diabetes Mellitus; Diagnosis; Documentation; Drug Prescriptions; Education; Evaluation; Foundations; Goals; HIV; Health; Health Insurance Portability and Accountability Act; Hospitals; ICD-10-CM; Information Systems; Institution; Institutional Review Boards; International; Intervention; Interview; Knowledge; Logical Observation Identifiers Names and Codes; Machine Learning; Manuals; Maps; Measurement; Measures; Medical; Mentors; Mentorship; Metadata; Modeling; Natural Language Processing; Outcome; Output; Participant; Patient-Focused Outcomes; Patients; Performance; Physicians; Play; Procedures; Questionnaires; Recommendation; Research; Research Design; Research Methodology; Research Personnel; Resources; Risk; Risk Assessment; Risk Factors; Role; Running; SNOMED Clinical Terms; Sampling; Specialist; Surveys; System; Technology; Terminology; Testing; Text; Time; Training; Vocabulary; Work; addiction; biomedical informatics; care outcomes; career; career development; clinical decision support; disorder risk; experience; food insecurity; heuristics; housing instability; improved; innovation; knowledgebase; machine learning algorithm; medication-assisted treatment; novel; opioid epidemic; opioid mortality; opioid overdose; opioid use; opioid use disorder; patient oriented; prescription opioid; recruit; safety net; skills; social; social health determinants; statistics; success; supervised learning; support tools; therapy design; therapy development; tool; trial readiness; usability

PROJECT SUMMARY/ABSTRACT In 2017, 1.7 million Americans suffered from opioid use disorders (OUD), which led to 47,000 American deaths from opioid overdose. Social determinates of health (SDoH) affect patients' OUD risk level and physicians' opioid prescribing. Physicians lack the tools to quickly and accurately assess SDoH associated with OUD, and lack knowledge of relevant resource for intervention. Clinical decision support (CDS) could quickly assess a patients' SDoH factors associated with OUD risk and provide actionable recommendations, which would reduce OUD risk assessment time and address knowledge gaps. In 2018, UCSF researchers created the Compendium of Medical Terminology Codes for Social Risk Factors that maps SDoH risks to medical vocabularies. However, most SDoH are documented in clinical notes. My long-term career goal is research independence with expertise in: 1) OUD risk assessment, 2) SDoH research, and 3) intervention development, implementation, and evaluation. Related to these goals, this study will use natural language processing (NLP) to identify SDoH in clinical notes, examine associations between SDoH and OUD, and develop a CDS tool to assess OUD risk. We will then assess usability, acceptability, and feasibility of using the CDS tool in clinical settings. This research will help physicians quickly and accurately assess OUD risk, intervene earlier, and improve care. Our research aims include: Aim 1. Use NLP to identify SDoH in clinical notes and examine associations between SDoH and OUD. We will use the Compendium and NLP to extract new SDoH in clinical notes. Two raters will manually validate the new SDoH, and use descriptive statistics to characterize associations between SDoH and OUD. (training goals 1 and 2). Aim 2: Develop a CDS tool to assess OUD risk. We will use SDoH and OUD associations from aim 1 to develop a supervised machine learning algorithm for our CDS tool. We will validate the CDS tool by measuring its ability to correctly assess OUD risk in patients' EHR data (training goals 1 and 2). Aim 3: Test the usability, acceptability, and feasibility of physicians' use of the CDS tool. 40 physicians will be asked to assess sample patient cases, then given CDS results on those same cases. Physicians will indicate whether they would follow the CDS's recommendations. Additionally, participants will be asked to complete an interview and questionnaire to evaluate usability and acceptability. We will assess feasibility by examining recruitment, implementation, and metadata. (training goal 3). These aims are achievable because I have experience in NLP and machine learning and my mentors are experts in OUD research, SDoH research, and intervention design; and have an outstanding record in career development. This K01 will help me achieve researcher independence by providing a) skills to develop an OUD risk assessment intervention; b) expertise in a novel growing SDoH field; c) an innovative trial-ready scalable intervention; and d) preliminary data for an R01.

项目总结/文摘