Improving medication safety for nursing home residents prescribed psychotropic dr

描述(申请人提供)：理查德·D·博伊斯，博士，匹兹堡大学生物医学信息学助理教授。他接受过信息学、药物流行病学和比较有效性研究方面的培训，并坚定地致力于提高老年人的用药安全性。他之前的工作重点是预测药物-药物相互作用的人工智能方法，以及 治疗减压疗养院居民的抗抑郁药。目前的提议是由国家老龄研究所颁发为期三年的指导式研究科学家发展奖(K01)，用于培训和支持，以解决他在老龄研究和养老院环境方面的知识空白。在与他的指导团队(Charles Reynolds博士、Jordan Karp博士和Steven Handler博士)的合作下，他制定了一项培训和研究计划，既可以填补这些知识空白，又可以为他的独立研究生涯做好准备。HIS K01的首要目标是成为一名专家，研究如何有效地将药物流行病学和比较有效性研究的研究成果转化为信息学干预措施，以提高老年人，特别是养老院居民的用药安全性。为了实现这一目标，他提出了八项职业发展活动和两个研究目标，将帮助他1)获得在疗养院环境下创建有效的临床决策支持干预措施的科学和实践方面的严格培训；2)彻底了解如何在疗养院环境中检测、监测、报告和评估跌倒和跌倒风险因素；以及3)了解多个疗养院临床医生利益相关者关于需要哪些研究来帮助减少与药物相关的不良事件的看法。这两个研究目的将使他能够整合和应用他将通过拟议的培训活动获得的知识，探索积极监测暴露于精神倾向PDDIs的患者的可行性和潜在的临床用途。该方法将使用大多数美国(US)NHS可用的电子数据，在接触到涉及精神药物的PDDI的居民过渡到不可接受的跌倒高风险状态时，向医生和/或护士提供高度具体和可操作的警报。博伊斯博士的假设是，这种方法将扩大NH临床医生对PDDIs进行推荐的“勤奋监测”的能力，同时避免警觉疲劳。他预计，该方法将推广到其他流行的NH ADE(例如，精神错乱)和其他药物类别(例如，情绪稳定剂)。这项建议与公共卫生有关，因为它将探索一种管理PDDIS的创新方法，最终可能减少最普遍的多因素老年综合征(FLOW)的发生。它还将为R系列拨款提案提供信息，以研究博伊斯博士计划在项目第二年提交的成熟干预措施。

DESCRIPTION (provided by applicant): Richard D. Boyce, PhD is an Assistant Professor of Biomedical Informatics at the University of Pittsburgh. He has training in informatics, pharmacoepidemiology, and comparative effectiveness research as well as a strong commitment to improving medication safety for older adults. His prior work has focused on artificial intelligence methods for predicting drug-drug interactions and the comparative safety of antidepressants for treating de- pressed nursing home residents. The current proposal is for a three year Mentored Research Scientist Development Award (K01) from the National Institute on Aging for training and support that will address gaps in his knowledge of aging research and the nursing home setting. In collaboration with his mentoring team (Drs. Charles Reynolds, Jordan Karp, and Steven Handler) he has developed a training and research plan that will both fill in these knowledge gaps and prepare him for an independent research career. The overarching goal of his K01 is to become an expert on how to effectively translate research results from pharmacoepidemiology and comparative effectiveness research to informatics interventions that improve medication safety for older adults, especially nursing home residents. To accomplish this goal, he proposes eight career development activities and two research aims that will help him 1) obtain rigorous training in the science and practice of creating clinical decision support interventions that are effective in the nursing home setting; 2) obtain a thorough understanding of how falls and fall risk factors are detected, monitored, reported, and assessed in the nursing home setting; and 3) learn the perceptions of multiple nursing home clinician stakeholders on what research is needed to help reduce medication-related adverse events. The two research aims will enable him to integrate and apply the knowledge that he will gain through the proposed training activities by exploring the feasibility and potential clinical usefulness of actively monitoring patients exposed to psycho- tropic PDDIs. The approach would use electronic data available in most United States (US) NHs to provide highly specific and actionable alerts to physicians and/or nurses when a resident who is exposed to a PDDI involving a psychotropic drug transitions to a state of unacceptably high risk for experiencing a fall. Dr. Boyce's hypothesis is that this approach will extend the ability of NH clinicians to perform the recommended "diligent monitoring" of PDDIs, while avoiding alert fatigue. He anticipates that the approach would be generalizable to other prevalent NH ADEs (e.g., delirium) and other drug classes (e.g., mood stabilizers). This proposal is relevant to public health because it will explore an innovative approach to managing PDDIs that may ultimately re- duce the occurrence of one of the most prevalent multifactorial geriatric syndromes (falls). It wil also inform an R-series grant proposal to study a mature intervention that Dr. Boyce plans to submit in year 2 of the project.