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: 10397143
• 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/10397143
• 关键词: Active Learning; Address; Admission activity; Affect; Automobile Driving; Award; Buffaloes; 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; Length of Stay; Link; Machine Learning; Medical; Mentors; Mentorship; Methodology; Modeling; Monitor; Natural Language Processing; Nature; Outcome; Patient Care; Patients; Performance; Pharmaceutical Preparations; Pharmacy facility; Population; Positioning Attribute; Pragmatic clinical trial; Primary Health Care; Program Sustainability; Provider; Public Health; Quality of Care; Recovery; Research; Research Personnel; Research Training; 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; 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; readmission rates; readmission risk; risk prediction; risk prediction model; risk stratification; skills; social; social disadvantage; 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 这一关于辅导式职业发展奖的建议包括一项培训和研究计划， 促进大卫雅各布博士过渡到一个独立的研究者，重点是实施 高需求患者从医院到家庭过渡期间的药物使用干预。雅各布斯医生是 临床药剂师，拥有流行病学和卫生服务高级学位，并具有丰富的经验 进行临床研究。候选人目前的研究重点是护理干预措施的过渡。 考虑到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提高。 取决于识别和针对高风险患者重新入院。为了预防这种情况， 预测模型已经被开发出来;然而，我们预测再入院的能力仍然很差。这 很可能是因为详细的社会信息，这是不成比例地代表在高需求人群， 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的 将联合收割机社会信息与丰富的临床数据相结合的模型，以个性化护理管理干预 减少再入院率具体目标是：（一）确定导致再次入院的社会风险因素 通过与患者、其护理人员和临床医生进行半结构化访谈;（ii）制定风险- 利用先进的信息技术建立预测模型;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个单一的复杂条件，慢性 阻塞性肺疾病（COPD），在开发风险分层工具，以提高其预测 识别高风险患者的性能。COPD将作为模型条件，因为它是主要的 再入院诊断，并且在患者护理出院后存在高度复杂性 过渡该奖项将为申请人提供导师指导的教学和体验式学习， 以下职业发展目标：1）获得定性方法的经验学习，2）提高 他在生物医学信息学方面的知识，并开发了集成高维电子的预测模型， 健康记录数据，3）增加他对实施科学的理解并获得实践经验 进行一项实用的临床试验主要导师Sanjay Sethi博士和导师团队将 密切关注他走向独立的进程，并将为他提供指导和资源。 以保证他的成功。这项拟议的研究利用了该大学的广泛资源， 布法罗解决一个重要的公共卫生问题。实现提出的目标，并获得这些先进的 技能将定位候选人提交成功的R 01测试拟议的临床预测模型， 现实世界中的转型战略。总而言之，一个全面的职业发展计划， 一个明确的培训，研究和指导结构将使雅各布斯博士成为一个高度 成功的，独立的临床研究员在卫生服务研究。