Improving the Framework for Healthcare Public Reporting

完善医疗保健公共报告框架

• 批准号: 8449404
• 负责人: JEFFREY H SILBER
• 金额: $ 26.52万
• 依托单位: CHILDREN'S HOSP OF PHILADELPHIA
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-30 至 2015-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8449404
• 关键词: Improving; Framework; Healthcare; Public; Reporting

DESCRIPTION (provided by applicant): Any effort to improve the science of public reporting must include an approach that ensures that guidance provided to the public is accurate, informative, relevant and understandable. Our approach will be to improve upon these elements in the context of outcomes measurement across hospitals using a fully Bayesian framework. In previous work we have demonstrated that the Hospital Compare random effects mortality model provides predictions that may be misleading when evaluating small hospitals. In this application we will develop a more realistic approach to modeling hospital outcomes that is both more accurate (less misleading) than Hospital Compare, and more informative from the perspective of the individual patient seeking guidance on which hospital to choose for care. Furthermore, in order for the public to benefit, not only do the models need improvement, but the public will need to increase their use of these models. To accomplish this latter goal, our approach will address barriers to the general use of these reports. We will develop models that are personalized to specific patient characteristics (making them more relevant for the individual patient), and, by making use of the Bayesian framework, we will introduce new methods for presenting results that adapt to common mistakes surrounding the interpretation of probabilities. Thus, patient error in the interpretation of results will be both less likely to occur and less likly to lead to mistaken hospital selection. Finally, as models will inevitably change and improve, we will develop a framework for future model comparisons, in order to assess whether new models should be adopted. In the end, we hope to develop a better approach with respect to (1) presenting results so that ease of use and understanding is improved, and use is increased; (2) the model predictions are improved; and (3) the process for adoption of future models are made more transparent and rational. Specifically, we will develop a fully Bayesian approach to model development for the conditions of AMI, Pneumonia, and Congestive Heart Failure. AIM 1 will construct a new approach to the presentation of public reporting results, using Bayesian derived probabilities, constructed so as to reduce errors in selection. AIM 2 will develop fully probabilistic predictive models of hospital outcomes. AIM 3 will develop a framework for evaluating any new model for public reporting. We will base our evaluation on two principles: (1) That a population following the recommendations of the improved model should have a higher predicted survival than a population using another model; and (2) We will use data to compare models using, for example, Bayes factors. At the conclusion of this project, we will have developed a better method to present information to the public, a better model for predicting and comparing outcomes across hospitals, and better methods to select and improve future models that may be used to aid the public in hospital selection. PUBLIC HEALTH RELEVANCE: Any effort to improve the science of public reporting must include an approach that ensures that guidance provided to the public is accurate, informative, relevant and understandable. Through the use of Bayesian models (a form of statistical modeling that is ideally suited to compare risks), this project will develop a better method to present information to the public, a better model for predicting and comparing outcomes across hospitals, and better methods to select and improve future models that may be used to aid the public in hospital selection.

描述（由申请人提供）：任何提高公共报告科学性的努力都必须包括确保向公众提供的指导准确、信息丰富、相关且易于理解的方法。我们的方法将是使用完全贝叶斯框架在跨医院的结果测量的背景下改进这些要素。在之前的工作中，我们已经证明，医院比较随机效应死亡率模型提供的预测在评估小型医院时可能会产生误导。在此应用程序中，我们将开发一种更现实的方法来对医院结果进行建模，该方法比 Hospital Compare 更准确（误导性更小），并且从寻求选择哪家医院的指导的患者个体的角度提供更多信息。此外，为了让公众受益，不仅模型需要改进，而且公众需要增加对这些模型的使用。为了实现后一个目标，我们的方法将解决这些报告的普遍使用的障碍。我们将开发针对特定患者特征的个性化模型（使它们与个体患者更相关），并且通过利用贝叶斯框架，我们将引入新的方法来呈现结果，以适应围绕概率解释的常见错误。因此，患者在解释结果时出现错误的可能性较小，也不太可能导致错误的医院选择。最后，由于模型不可避免地会发生变化和改进，我们将开发一个未来模型比较的框架，以评估是否应该采用新模型。最后，我们希望在以下方面开发出一种更好的方法：（1）呈现结果，从而提高易用性和理解性，并增加使用； (2) 模型预测得到改进； (3)未来模型的采用过程更加透明和合理。具体来说，我们将开发一种完全贝叶斯方法来开发 AMI、肺炎和充血性心力衰竭病症的模型。 AIM 1 将使用贝叶斯派生概率构建一种新的公开报告结果呈现方法，以减少选择错误。 AIM 2 将开发医院结果的完全概率预测模型。 AIM 3 将开发一个框架来评估任何新的公共报告模型。我们的评估将基于两个原则：（1）遵循改进模型建议的人群应该比使用其他模型的人群具有更高的预测生存率； (2) 我们将使用数据来比较模型，例如使用贝叶斯因子。在该项目结束时，我们将开发出一种更好的方法来向公众提供信息，一种更好的模型来预测和比较各医院的结果，以及更好的方法来选择和改进未来的模型，这些模型可用于帮助公众医院选择。 公共卫生相关性：任何提高公共报告科学性的努力都必须包括确保向公众提供的指导准确、信息丰富、相关且易于理解的方法。通过使用贝叶斯模型（一种非常适合比较风险的统计模型），该项目将开发一种更好的方法来向公众提供信息，一种更好的模型来预测和比较各医院的结果，以及更好的方法来向公众提供信息。选择并改进可用于帮助公众选择医院的未来模型。