Improving age-related risk assessment and documentation for diverse older adults with cancer

项目摘要 Melody K Schiaffino博士，公共卫生硕士，是圣地亚哥州立大学（SDSU）终身助理教授， 中心的核心教员健康公平，教育和放射医学研究， 转化科学在加州大学圣地亚哥分校，和UCSD摩尔斯癌症中心的准会员。博士 Schiaffino于2014年获得卫生服务研究博士学位，2008年获得流行病学硕士学位。作为 作为一名健康差异系统科学家，Schiaffino博士的目标是识别组织和供应商层面的风险 影响如何向接受癌症治疗的不同老年人提供护理的因素， 最佳的护理和治疗效果。老年人，特别是少数民族，成年人面临更大的风险， 癌症治疗的症状负担和治疗相关毒性风险较高。题为"改善 年龄相关的风险评估和记录在不同的老年人诊断为癌症，旨在 检查导致对年龄相关风险进行次优评估的提供者和文件障碍。年龄- 相关的风险评估是一系列基于证据的临床测试和筛查，可以改善癌症 对于老年人的治疗计划，这对于有语言障碍的老年人来说尤其突出。 目前对造成次优评估的机制研究不足， 放射肿瘤学中年龄相关风险的记录。虽然有证据表明， 当评估与年龄相关的风险时，最近的临床试验结果显示， 4家供应商正在日常实践中实施此评估。有效的评估更少 常见于有英语语言障碍的老年人。了解组织和提供商级别 对使用电子健康记录（EHR）的评估和记录实践的看法，以及 定性采访和观察肿瘤学家，将有助于为未来的诊所工作流程重新设计提供信息。的 拟议的职业发展和培训计划支持Schiaffino博士成为一名 独立的，老化系统科学家通过以下三个目标：1）获得先进的自然 语言处理（NLP）算法开发培训，从EHR中提取非结构化文本，2） 参与观察和培训老年肿瘤护理服务，了解临床工作流程 以及围绕年龄相关风险评估的临床实践，以及3）获得临床工作流程经验 实施科学的提案制定。该项目将在SDSU，UCSD和City of 希望（杜阿尔特，加利福尼亚州）与导师谁是老年医学/老年学专家（主要：艾莉森摩尔，医学博士，MS）; 放射肿瘤学/HSR（共同主要：James Murphy，MD，MS，UCSD）;老年肿瘤学/决策 （导师：William Dale，MD，PhD，City of Hope）; NLP/生物信息学（导师：Mike Hogarth，MD，UCSD）; 预测模型/生物统计学（合作者：芭芭拉贝利，博士，SDSU）和临床实施科学 和有限的英语能力（LEP）的差距（合作者：艾丽西娅费尔南德斯，医学博士，加州大学旧金山分校）。

PROJECT SUMMARY Melody K Schiaffino, PhD, MPH is a tenure-track Assistant Professor at San Diego State University (SDSU), a core faculty member in the Center for Health Equity, Education, and Research in Radiation Medicine and Translational Science at UC San Diego, and Associate Member of the UCSD Moores Cancer Center. Dr. Schiaffino received her PhD in Health Services Research in 2014 and MPH in Epidemiology in 2008. As a health disparities systems scientist, Dr. Schiaffino’s goals are to identify organizational and provider-level risk factors that affect how care is delivered to diverse older adults undergoing treatment for cancer to improve sub- optimal care delivery and treatment outcomes. Older, specifically minority, adults are at greater risk due to the higher symptom burden and treatment-related toxicity risk of cancer therapy. The proposal entitled, Improving age-related risk assessment and documentation in diverse older adults diagnosed with cancer, seeks to examine provider and documentation barriers that result in sub-optimal assessment of age-related risk. Age- related risk assessments are a series of evidence based clinical tests and screening that can improve cancer treatment planning for older adults, it is especially salient for older adults experiencing language barriers. There is currently insufficient research on the mechanisms contributing to sub-optimal assessment and documentation of age-related risk in radiation oncology. While evidence supports improved communication and cancer outcomes for patients when age-related risk is assessed, recent clinical trial findings show that only 1 in 4 providers are implementing this assessment in routine practice. Effective assessments are even less common in older adults with English language barriers. Understanding organizational and provider-level perspectives on assessment and documentation practices using electronic health records (EHRs), and qualitatively interviewing and observing oncologists, will help inform future clinic workflow redesign. The proposed career development and training plan supports Dr. Schiaffino’s trajectory toward becoming an independent, aging-systems scientist through the following three goals: 1) Obtain advanced natural language processing (NLP) algorithm development training to extract unstructured text from EHRs, 2) Engage in observation and training in geriatric oncology care delivery, to understand clinical workflow and clinical practices around assessment of age-related risk, and 3) Gain experience in clinical workflow implementation science proposal development. This project will take place at SDSU, UCSD, and City of Hope (Duarte, CA) with mentors who are experts in Geriatrics/Gerontology (Primary: Alison Moore, MD, MS); Radiation Oncology/HSR (Co- Primary: James Murphy, MD, MS, UCSD); Geriatric Oncology/ Decision-Making (Mentor: William Dale, MD, PhD, City of Hope); NLP/Bioinformatics (Mentor: Mike Hogarth, MD, UCSD); Predictive Models/Biostatistics (Collaborator: Barbara Bailey, PhD, SDSU) and clinical implementation science and Limited English Proficiency (LEP) disparities (Collaborator: Alicia Fernandez, MD, UCSF).