Integrative Informatics Approach to Predict Readmissions and Improve Outcomes in COPD

综合信息学方法预测 COPD 的再入院率并改善预后

• 批准号: 10597170
• 负责人: David Jacobs
• 金额: $ 19.24万
• 依托单位: STATE UNIVERSITY OF NEW YORK AT BUFFALO
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-01 至 2026-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10597170
• 关键词: Active Learning; Address; Admission activity; Affect; Automobile Driving; Award; Caregivers; Caring; Chronic Obstructive Pulmonary Disease; Clinical; Clinical Data; Clinical Pharmacists; Clinical Research; Clinical Trials Design; Complex; Data; Development; Development Plans; Diagnosis; Discipline of Nursing; Education; Electronic Health Record; Epidemiology; Event; Expenditure; Face; Health Services; Health Services Research; Healthcare Systems; Home; Hospitalization; Hospitals; Individual; Informatics; Intervention; Interview; K-Series Research Career Programs; Knowledge; Learning; Length of Stay; Link; Machine Learning; Managed Care; Medical; Mentors; Mentorship; Methodology; Modeling; Monitor; Natural Language Processing; Nature; Outcome; Patient Care; Patient Discharge; Patient Readmission; Patients; Performance; Pharmaceutical Preparations; Pharmacy facility; Population; Positioning Attribute; Pragmatic clinical trial; Primary Care; Program Sustainability; Provider; Public Health; Quality of Care; Recovery; Research; Research Personnel; Resources; Risk; Risk Factors; Sampling; Social Characteristics; Social support; Structure; Techniques; Testing; Time; Training; United States; Universities; Work; administrative database; biomedical informatics; care coordination; career development; clinical care; clinical practice; clinical predictive model; cost; cost effectiveness; experience; feasibility testing; health care service utilization; high dimensionality; high risk; hospital readmission; implementation science; improved; improved outcome; individualized medicine; informatics tool; innovation; multidisciplinary; patient safety; personalized care; personalized medicine; practice setting; prediction algorithm; predictive modeling; predictive tools; readmission rates; readmission risk; risk prediction; risk prediction model; risk stratification; skills; social; social disparities; social factors; success; therapy design; tool

Project Summary This proposal for a mentored career development award consists of a training and research plan devised to facilitate Dr. David Jacobs' transition to an independent investigator focusing on the implementation of medication use interventions during the transition from hospital to home for high-needs patients. Dr. Jacobs is a clinical pharmacist with an advanced degree in epidemiology and health services and has significant experience conducting clinical research. The candidate's current research is focused on transition of care interventions. Given the resource intensive nature of these interventions, the sustainability of these programs will heavily depend on identifying and targeting patients at high-risk for hospital readmission. To anticipate such admissions, predictive models have been developed; however, our ability to predict hospital readmissions remains poor. This is likely because detailed social information, which is disproportionately represented in high-needs populations, is typically absent in risk stratification tools. Therefore, the long-term research objective is to build predictive models that combine social information with rich clinical data to individualize care management interventions and reduce readmissions. The specific aims are: (i) to determine social risk factors driving hospital readmissions by conducting semi-structured interviews with patients, their caregivers, and clinicians; (ii) to develop a risk- prediction model using advanced informatics techniques; and (iii) to develop and test the feasibility of a pilot intervention aimed at improving transition strategies. This work will focus on a singular complex condition, chronic obstructive pulmonary disease (COPD), in developing a risk stratification tool in order to improve its predictive performance for identifying high-risk patients. COPD will serve as the model condition since it is one of the major readmission diagnoses, and there exists a high level of complexity following hospital discharge at patients' care transition. This award will provide the applicant with mentor-guided didactic and experiential learning to address the following career development objectives: 1) gain experiential learning in qualitative methodology, 2) advance his knowledge in biomedical informatics and develop predictive models integrating high-dimensional electronic health record data, and 3) increase his understanding of implementation science and gain practical experience in conducting a pragmatic clinical trial. The primary mentor, Dr. Sanjay Sethi, and the mentorship team will work closely to monitor his progress toward independence and will provide him with the guidance and the resources to guarantee his success. The proposed study leverages the extensive resources available at the University at Buffalo to address an important public health issue. Achieving the proposed aims and acquiring these advanced skills will position the candidate to submit successful R01s testing the proposed clinical prediction model and transition strategy in real-world settings. In summary, a comprehensive career development plan in the context of a well-defined training, research, and mentorship structure will allow Dr. Jacobs to emerge as a highly successful, independent clinician-investigator in health services research.

项目摘要 这项关于职业发展指导奖的提议包括一个培训和研究计划，旨在 促进大卫·雅各布斯博士向独立调查员过渡，专注于执行 在高需求患者从医院过渡到家庭的过程中，使用药物干预。雅各布斯博士是一位 临床药剂师，具有流行病学和卫生服务方面的高级学位，并具有丰富的经验 进行临床研究。这位候选人目前的研究重点是护理干预的过渡。 鉴于这些干预措施的资源密集型性质，这些方案的可持续性将大大 依赖于识别和瞄准再次住院的高危患者。为了预测这样的招生， 预测模型已经开发出来；然而，我们预测再次住院的能力仍然很差。这 可能是因为详细的社会信息在高需求人群中不成比例地存在， 在风险分层工具中通常不存在。因此，长期的研究目标是建立预测性 将社会信息与丰富的临床数据相结合的模式，以实现个性化护理管理干预 并减少再入院人数。具体目标是：(1)确定推动再次住院的社会风险因素 通过与患者、他们的照顾者和临床医生进行半结构化访谈；(Ii)发展风险- 使用先进的信息学技术建立预测模型；以及(Iii)开发和测试试点的可行性 旨在改进过渡战略的干预措施。这项工作将集中在一种特殊的复杂情况下，慢性 阻塞性肺疾病(COPD)，正在开发风险分层工具，以提高其预测性 用于识别高危患者的性能。慢性阻塞性肺疾病将作为模型条件，因为它是主要的 再入院诊断，出院后患者护理存在高度复杂性 过渡。该奖项将为申请者提供导师指导的说教和体验学习，以解决 以下职业发展目标：1)在定性方法方面获得体验性学习，2)进步 他在生物医学信息学和开发集成高维电子学的预测模型方面的知识 健康记录数据，以及3)增加对实施科学的理解，获得实践经验 进行一项务实的临床试验。主要导师Sanjay Sethi博士和导师团队将发挥作用 密切监测他走向独立的进展，并将为他提供指导和资源 以保证他的成功。建议的研究利用香港大学现有的广泛资源，网址为 布法罗解决了一个重要的公共卫生问题。实现提议的目标并获得这些先进的 技能将定位候选人提交成功的R01测试建议的临床预测模型，并 真实世界环境中的过渡策略。综上所述，全面的职业发展计划在 明确的培训、研究和指导结构将使雅各布斯博士成为一名高度 在卫生服务研究中成功、独立的临床医生和调查员。