Developing and refining methods to measure hospital-level diagnostic intensity, identify outlier hospitals, and characterize the relationship between diagnostic intensity and missed diagnoses

项目总结/摘要 提供低价值的医疗服务导致美国的医疗成本居高不下。研究显示 美国医疗保健提供强度的区域差异不能用以下差异来解释： 医疗复杂性和与结果差异无关表明，低价值医疗保健是 不是均匀分布在全国各地。然而，医院医疗保健强度的变化 水平还没有得到很好的研究。经过良好校准和验证的医院级诊断强度指标 缺乏。有效的方法来识别诊断强度方面的离群医院将允许更好的 了解低价值护理和诊断过度使用的驱动因素。此外，这将使更好的 诊断强度和护理质量之间关系的表征，特别是错过 诊断。它还将允许确定诊断测试水平较低但诊断率较低的医院。 因此，他们的护理过程可以被研究和复制。 候选人是约翰霍普金斯大学的住院医师和初级研究员。他最近 发表了一份手稿，描述了医院级诊断强度指数的发展， 非特异性诊断代码与特异性诊断测试配对作为诊断率的代表。他 组建了一个指导/咨询小组，该小组具有评估证据强度的专业知识， 价值关怀和识别诊断错误。这位候选人的长期目标是成为一名无党派人士 调查人员具有专业知识，了解低价值健康的驱动因素，诊断之间的关系 强度和质量，并最终制定干预措施，以帮助低绩效医院系统最大限度地减少 过度使用而不影响质量。 这项工作将描述医院级别的诊断强度，以便可以识别离群医院， 可以阐明医院一级诊断强度与漏诊之间的关系。的 该项目包括三个目标：1）对表征医院水平的文献进行系统综述 诊断强度，2）将医院级诊断强度指数应用于医疗保险索赔数据，并开发 并测试增强的诊断强度指数，3）利用该指数来表征 诊断强度和漏诊。目标2和3将使用Medicare 100%有限数据集声明。 除了执行这些目标，候选人将参加课程，从他的指导阅读中学习 导师和顾问，参加研讨会和国家会议，并定期与他的指导团队会面。 他将学习进行系统审查所需的技能，获得分析大型索赔的专业知识， 数据集，并更好地了解如何衡量质量。这指导了研究和职业生涯 发展将帮助他实现成为一名独立调查员的目标。

PROJECT SUMMARY/ABSTRACT The provision of low-value medical services contributes to high healthcare costs in the US. Studies showing regional variation in the intensity of healthcare provision within the US that are not explained by differences in medical complexity and not associated with differences in outcomes demonstrate that low-value healthcare is not homogenously distributed throughout the country. However, variation in healthcare intensity at the hospital level has not been well-studied. Well-calibrated and validated hospital-level measures of diagnostic intensity are lacking. Effective methods to identify outlier hospitals with respect to diagnostic intensity will allow a better understanding of the drivers of low-value care and diagnostic overuse. Additionally, this will allow for better characterization of the relationship between diagnostic intensity and quality of care, specifically missed diagnoses. It will also allow identification of hospitals with lower levels of diagnostic testing and yet low rates of missed diagnoses so their care processes can be studied and replicated. The candidate is a hospitalist physician and junior investigator at Johns Hopkins University. He has recently published a manuscript describing the development of a hospital-level diagnostic intensity index which utilizes non-specific diagnosis codes paired with specific diagnostic tests as a proxy for diagnostic yield. He has assembled a mentoring/advising team with expertise in evaluating the strength of evidence, measuring low- value care, and identifying diagnostic errors. The candidate’s long-term goal is to become an independent investigator with expertise in understanding the drivers of low-value health, the relationship between diagnostic intensity and quality, and ultimately developing interventions to help low-performing hospital systems minimize overuse without compromising quality. This work will characterize hospital-level diagnostic intensity such that outlier hospitals can be identified and the relationship between diagnostic intensity and missed diagnoses at the hospital level can be elucidated. The project includes three aims: 1) Perform a systematic review of the literature characterizing hospital-level diagnostic intensity, 2) Apply the hospital-level diagnostic intensity index to Medicare claims data and develop and test an augmented diagnostic intensity index, 3) Utilize this index to characterize the relationship between diagnostic intensity and missed diagnoses. Aims 2 and 3 will use Medicare 100% limited dataset claims. In addition to executing these aims, the candidate will take courses, learn from directed readings by his mentors and advisor, attend seminars and national conferences, and meet with his mentoring team regularly. He will learn the skills necessary to conduct systematic reviews, gain expertise in analyzing large claims datasets, and develop a better understanding of how to measure quality. This mentored research and career development will help him achieve his goal of becoming an independent investigator.