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PATIENT PREFERENCES FOR DISCLOSURE: A NATIONAL SURVEY

PATIENT PREFERENCES FOR DISCLOSURE: A NATIONAL SURVEY
患者的披露偏好:全国调查
批准号:
6391075
负责人:
WENDY LEVINSON
金额:
$43.99万
依托单位国家:
美国
项目类别:
财政年份:
1999
资助国家:
美国
项目状态:
已结题
起止时间:
1999-09-15 至 2003-08-31

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中文摘要
翻译
在不断变化的医疗保健环境中,以前由患者及其医生做出的重要决定现在受到健康计划和保险公司等第三方的强烈影响,这些第三方的干预导致许多患者质疑他们的医生是否真的在为他们的最佳利益行事。医生们报告说,他们面临的挑战是,如何有效地与患者就第三方限制进行沟通,同时建立信任并保持患者的满意度。这项研究建议通过征求患者的意见来回答这个问题,他们应该如何以及何时了解第三方政策,特别是转介限制和利益冲突。通过一项基于具有代表性的美国人口样本的全国性调查,受访者将接触到医生使用的不同沟通策略,以披露转诊限制和财务利益冲突。这些声明将反映职业行为的最高标准,并将基于医生在道德规范内行事的假设。这项研究建议的具体目标如下:(1)制定医生披露第三方限制的沟通策略,并评估其真实性和可接受性;(2)在来自高渗透率和低渗透率的托管护理市场的患者中试点测试调查工具(包含披露第三方限制的录音陈述);(3)通过由国家舆论研究中心(NORC)管理的具有全国代表性的综合社会调查(GSS),实验性地评估公众对如何实施关于第三方限制的信息的偏好;(4)评估第三方限制的披露如何影响患者信任、满意度和退出健康计划的意向的结果;以及(5)评估披露结果如何基于患者对第三方限制的经验、健康状况和其他人口统计特征而变化。研究结果将为负责向患者和广大公众披露信息的立法机构和卫生保健组织提供决策依据。这项研究将形成广泛传播的针对医生的教育干预的基础,帮助他们以加强治疗性医患关系的方式披露有关第三方限制的信息。
英文摘要
In the changing health care environment, important decisions previously made by patients and their physicians are now strongly influenced by third parties such as health plans and insurers whose intrusion has led many patients to question whether their physicians are truly acting in their best interest. Physicians report that they are challenged in knowing how to communicate effectively with patients about third-party restrictions while simultaneously building trust and maintaining patient satisfaction. This study proposes to answer this question by eliciting patient opinions on how and when they should be informed about third-party policies, particularly referral-restrictions and conflicts of interest. Through a national survey based on a representative sample of the US population, respondents will be exposed to different communication strategies used by physicians to disclose referral restrictions and financial conflicts of interest. These statements will reflect the highest standards of professional conduct and will be based on the assumption that physicians act within ethical parameters. The specific objectives of this research proposal are as follows: (1) develop communication strategies for disclosure of third-party restrictions by physicians and evaluate their authenticity and acceptability; (2) pilot test a survey instrument (containing audiotaped statements of disclosure of third-party restrictions) among patients from high and low penetration managed care markets; (3) experimentally evaluate public preferences on how information regarding third-party restrictions should be implemented through the nationally representative General Social Survey (GSS) administered by the National Opinion Research Center (NORC); (4) Evaluate how disclosure of third-party restrictions affects outcomes of patient trust, satisfaction, and intent to disenroll from health plan; and (5) evaluate how disclosure outcomes vary based on patients' experience with third-party restrictions, health status, and other demographic characteristics. The results will provide the basis for policy decisions for both legislative bodies and health care organizations responsible for disclosure to patients and the public at large. The study will form the basis of broadly disseminated educational interventions for physicians helping them to disclose information about third-party restrictions in manner that reinforces the therapeutic patient-physician relationship.
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INFORMED DECISION MAKING IN OLDER PATIENTS AND SURGEONS
  • 批准号:
    6649194
  • 项目类别:
  • 资助金额:
    $36.15万
  • 财政年份:
    2001
  • 负责人:
    WENDY LEVINSON
  • 依托单位:
INFORMED DECISION MAKING IN OLDER PATIENTS AND SURGEONS
  • 批准号:
    6544646
  • 项目类别:
  • 资助金额:
    $52.42万
  • 财政年份:
    2001
  • 负责人:
    WENDY LEVINSON
  • 依托单位:
INFORMED DECISION MAKING IN OLDER PATIENTS AND SURGEONS
  • 批准号:
    6533902
  • 项目类别:
  • 资助金额:
    $66.82万
  • 财政年份:
    2001
  • 负责人:
    WENDY LEVINSON
  • 依托单位:
INFORMED DECISION MAKING IN OLDER PATIENTS AND SURGEONS
  • 批准号:
    6797233
  • 项目类别:
  • 资助金额:
    $36.12万
  • 财政年份:
    2001
  • 负责人:
    WENDY LEVINSON
  • 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