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Decision Making Challenges and Needs for Health Insurance Exchange Enrollees

Decision Making Challenges and Needs for Health Insurance Exchange Enrollees
健康保险交易所参与者的决策挑战和需求
批准号:
9153170
负责人:
Alison A Galbraith
金额:
$39.48万
依托单位国家:
美国
项目类别:
财政年份:
2016
资助国家:
美国
项目状态:
已结题
起止时间:
2016-08-01 至 2020-05-31

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英文摘要
PROJECT SUMMARY/ABSTRACT Navigating health insurance options is daunting, particularly for consumers who must purchase coverage on their own in the non-group market. Much is at stake in these decisions, which determine not only the adequacy of financial risk protection but may also have important consequences for health care access and health, especially for vulnerable populations. Health insurance exchanges can facilitate plan selection by creating a marketplace to choose among qualified health plans and by providing decision support tools to help consumers optimize their choices. The goal of the proposed project is to understand the downstream experiences of enrollees in the non-group market after choosing a plan, and to identify those at highest risk for adverse outcomes so that strategies can be developed to address their needs. The project’s specific aims are to: 1) examine how exchange enrollees differ from those who enroll in non-group plans outside the exchange in terms of socioeconomic and clinical characteristics, decision support tools used, and type of plan chosen; 2) explore whether use of particular tools during enrollment in non-group exchange and non-exchange plans is associated with type of plan chosen and better downstream health care outcomes (e.g. avoiding delayed/forgone care and financial burden due to out-of-pocket costs, and being able to continue to see one’s prior provider); 3) identify enrollees who prove to be at highest risk for adverse downstream outcomes in non- group plans using characteristics that can be measured at enrollment; and 4) understand challenges enrollees face choosing and using insurance benefits and the types of information and tools needed during and after enrollment. We will use a longitudinal design to follow a cohort of individuals and families enrolling in non- group plans from a large insurance carrier in and outside health insurance exchanges during 2017 open enrollment in four New England states. We will gather baseline survey data about enrollee characteristics and decision making tools used to choose the plan. After 12 months of enrollment, we will collect follow-up survey data about downstream outcomes, and obtain claims data on utilization and costs in the plan. The research team’s expertise in health policy, health services research, economics, and clinical medicine will foster a policy-relevant, scientifically rigorous R01 in an environment with a track record of academic research using health plan data and populations. This project provides a unique and timely opportunity to rigorously study the impact of unprecedented changes to the non-group insurance market on health plan decision making, access, and affordability. This project will provide crucial data for policy makers and health plans to inform the design of exchanges and decision support tools to enable consumers to choose high quality health insurance coverage that minimizes costs and promotes use of high-value health care.
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Decision Making Challenges and Needs for Health Insurance Exchange Enrollees
  • 批准号:
    9322331
  • 项目类别:
  • 资助金额:
    $39.93万
  • 财政年份:
    2016
  • 负责人:
    Alison A Galbraith
  • 依托单位:
Child Health and Family Experience under Health Insurance Cost-Sharing
  • 批准号:
    7634453
  • 项目类别:
  • 资助金额:
    $13.42万
  • 财政年份:
    2007
  • 负责人:
    Alison A Galbraith
  • 依托单位:
Child Health and Family Experience under Health Insurance Cost-Sharing
  • 批准号:
    7428905
  • 项目类别:
  • 资助金额:
    $13.27万
  • 财政年份:
    2007
  • 负责人:
    Alison A Galbraith
  • 依托单位:
Child Health and Family Experience under Health Insurance Cost-Sharing
  • 批准号:
    8105467
  • 项目类别:
  • 资助金额:
    $13.75万
  • 财政年份:
    2007
  • 负责人:
    Alison A Galbraith
  • 依托单位:
国内基金
海外基金
Scalable Learning and Optimization: High-dimensional Models and Online Decision-Making Strategies for Big Data Analysis