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Health Insurance Navigators for the Low-Income Elderly

Health Insurance Navigators for the Low-Income Elderly
低收入老年人的健康保险导航
批准号:
7475057
负责人:
Alex D Federman
金额:
$21.49万
依托单位国家:
美国
项目类别:
财政年份:
2006
资助国家:
美国
项目状态:
已结题
起止时间:
2006-08-15 至 2011-07-31

项目摘要

项目成果

Alex D Federman的其他基金

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中文摘要
翻译
描述(由申请人提供):保罗·比森老年事业发展奖的候选人是一名接受过正式临床研究培训的普通内科医生。他提出了一项研究和职业发展计划,以现有技能为基础,发展以患者为导向的老龄化相关研究的专业知识,重点关注老年人护理的经济障碍和降低这些障碍的干预措施的发展。老年人医疗保险的复杂性对选择满足个人需求的保险构成了重大挑战,特别是对于低收入的市中心老年人,因为他们受教育程度低、识字问题和其他障碍。新的医疗保险处方药福利将大大增加这一挑战,因为人们必须权衡无数因素才能做出最佳的计划选择。以前促进老年人保险决策的努力收效甚微。一个原因可能是这些努力缺乏理论基础或强有力的经验数据来支持这种方法。本应用程序的目的是通过经验识别影响低收入,内城老年人保险决策的因素,并设计一个
英文摘要
DESCRIPTION (provided by applicant): The candidate for this Paul B. Beeson Career Development Award in Aging is a general internist with formal clinical research training. He proposes a research and career development plan to build on existing skills and develop expertise in patient-oriented aging-related research, with a focus on financial barriers to care for the elderly and the development of interventions to lower these barriers. The complexities of healthcare coverage for the elderly pose a significant challenge for choosing coverage that meets individual needs, especially for low-income, inner-city seniors because of low education, literacy problems, and other barriers. The new Medicare prescription drug benefit will add greatly to this challenge because of the myriad factors one must weigh to make an optimal plan selection. Previous efforts to facilitate insurance decision making for the elderly have had only modest effects. One reason may be that these efforts have lacked theoretical grounding or strong empirical data to support the approach. The goal of this application is to empirically identify factors that influence insurance decision making among low-income, inner city seniors, and devise a method to facilitate decision making. The 4 specific aims are 1) to determine the influence of cognitive impairment and health literacy on healthcare coverage decisions; 2) to develop measures of the impact of healthcare costs on the elderly, and of their insurance information and decision making support needs; 3) to test the validity of applying the Transtheoretical Model's stages of change construct to the process of healthcare coverage decision making; 4) to develop and evaluate an intervention that applies the findings from Aims 1-3 to provide insurance coverage decision making support that is tailored to the individual's specific needs, and takes into account their readiness to make a change in healthcare coverage. For Aims 1-3, we will conduct focus groups to develop a survey instrument, and will survey 400 elderly adults from hospital and community-based clinics and senior centers in East Harlem and the Bronx, NY. For Aim 4, we will develop a health insurance navigator intervention that uses trained layperson volunteers to assist patients, who have been screened for need, with making optimal healthcare coverage decisions. We will evaluate the intervention in a randomized controlled trial. This research would provide clinicians and investigators with new research and clinical tools, expand understanding of the insurance decision making in older adults, and create an efficient, low cost method to help seniors optimize insurance decisions. Study findings would also give policymakers insight into the real world experience of some of the most vulnerable elderly patients participating in the largest expansion of Medicare since its inception. The career development plan includes didactic courses, expert advising, and mentored research to develop skills in qualitative and survey research methods, and to expand the candidate's clinical geriatrics knowledge base.
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Research Training for the Care of Vulnerable Older Adults with Alzheimer’s Disease and Related Dementias and Other Chronic Conditions
Natural Language Processing and Automated Speech Recognition to Identify Older Adults with Cognitive Impairment
Research Training for the Care of Vulnerable Older Adults with Alzheimer’s Disease and Related Dementias and Other Chronic Conditions
Natural Language Processing and Automated Speech Recognition to Identify Older Adults with Cognitive Impairment
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