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.

项目摘要/摘要 2017年，有170万美国人患有阿片类药物使用障碍（OUD），导致47,000人死亡 来自阿片类药物过量。社会确定健康（SDOH）会影响患者的OUD风险水平和 医师的阿片类药物处方。医师缺乏快速准确评估SDOH相关的工具 使用OUD，并且缺乏相关资源进行干预的知识。临床决策支持（CD）可以 快速评估患者与OUD风险相关的SDOH因素，并提供可行的建议， 这将减少风险评估时间并解决知识差距。 2018年，UCSF研究人员 为社会风险因素创建了医学术语的汇编，以将SDOH风险映射到 医学词汇。但是，大多数SDOH记录在临床注释中。我的长期职业目标是 具有专业知识的研究独立性：1）OUD风险评估，2）SDOH研究和3）干预 开发，实施和评估。与这些目标有关，本研究将使用自然语言 处理（NLP）以识别临床笔记中的SDOH，检查SDOH和OUD之间的关联， 并开发一个CD工具来评估OUD风险。然后，我们将评估可用性，可接受性和可行性 在临床环境中使用CDS工具的。这项研究将帮助医生快速准确评估 OUD风险，较早进行干预并改善护理。我们的研究目的包括：目标1。使用NLP识别SDOH 临床笔记并检查SDOH和OUD之间的关联。我们将使用Compendium和NLP 在临床笔记中提取新的SDOH。两个评估者将手动验证新的SDOH，并使用描述性 统计数据以表征SDOH和OUD之间的关联。 （训练目标1和2）。目标2：开发一个 评估OUD风险的CD工具。我们将使用AIM 1的SDOH和OUD协会来开发监督 我们的CD工具的机器学习算法。我们将通过测量其正确的能力来验证CDS工具 评估患者EHR数据的OUD风险（培训目标1和2）。目标3：测试可用性，可接受性和 医生使用CD工具的可行性。将要求40位医生评估样本患者病例，然后 在相同情况下给定CD结果。医师将指出他们是否会遵循CD 建议。此外，将要求参与者完成面试和问卷调查 评估可用性和可接受性。我们将通过检查招聘，实施和 元数据。 （训练目标3）。这些目标是可以实现的，因为我有NLP和机器的经验 学习和我的导师是OUD研究，SDOH研究和干预设计的专家；并有一个 职业发展的出色记录。这个K01将通过 提供a）开发OUD风险评估干预措施的技能； b）新型SDOH领域的专业知识； c）一种创新的可进行的可扩展干预措施； D）R01的初步数据。