Improving the Framework for Healthcare Public Reporting

描述（由申请人提供）：任何改善公共报告科学的努力都必须包括一种方法，确保向公众提供的指导是准确的、翔实的、相关的和可理解的。我们的方法将是在使用完全贝叶斯框架的医院结果测量背景下改进这些要素。在之前的工作中，我们已经证明了医院比较随机效应死亡率模型在评估小医院时提供的预测可能具有误导性。在本应用程序中，我们将开发一种更现实的方法来对医院结果进行建模，这种方法既比hospital Compare更准确（更少误导），又从寻求选择哪家医院治疗指导的个体患者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（使其与个体患者更相关），并且，通过使用贝叶斯框架，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，患者在解释结果时的错误将不太可能发生，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改变和改进，我们将为未来的模型比较制定一个框架，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一种更好的方法：(1)呈现结果，以便易于使用和理解，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理性。具体来说，我们将开发一个完整的贝叶斯方法来建立AMI、肺炎和充血性心力衰竭的模型。AIM 1将构建一种新的方法来呈现公开报告结果，使用贝叶斯衍生概率，以减少选择中的错误。AIM 2将开发医院预后的完全概率预测模型。第三阶段目标将制定一个框架，用于评估任何新的公开报告模式。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另一种模型的人群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。在这个项目结束时，我们将开发出一种更好的方法向公众提供信息，一种更好的模型来预测和比较各医院的结果，以及一种更好的方法来选择和改进可能用于帮助公众选择医院的未来模型。

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.