Understanding and Improving Decision-making in Pneumonia with Informatics

简介：该提案旨在了解和改善对肺炎退伍军人的护理 通过信息学。 背景：肺炎是美国最常见的感染性死亡原因， 估计每年有50，000人死亡，80 - 100亿美元的费用。 在急诊科，20，000人住院。13风险调整后的30天死亡率范围 从11%到18%。用于肺炎的资源也有很大差异， 医院治疗估计是门诊治疗的25倍。20为了提高肺炎治疗的标准， 密集的研究和质量改进工作一直集中在建立基于证据的 肺炎的实践指南，代表了我们的研究和意见的共识， 最好的学术医院和肺炎专家然而，我们继续看到， 和其他系统一样，VA的结果表明，指南可能难以应用于真实的 实践我们的电子健康记录的最新进展使我们能够衡量实际的做法， 人口和个人水平。通过研究在分流和抗生素的决定，为病人的变化， 肺炎，我们可以从我们自己的人口中学习，以产生包括以前的证据， 代表性不足的患者，设置和场景，使其更接近真实的实践。 职业目标：我的职业目标是：1）发展高级人口分析技能，2）获得基础 在行为科学，和3）发展在信息学更深入的理解。 研究目标：我的研究目标是建立在我的职业目标之上：（1）检查分类中的变异来源 和抗生素选择决策的肺炎跨VA急诊科。(2)表征 影响急诊科医务人员肺炎决策的认知过程 展示了决策过程中的差异。(3)设计和测试一个支持决策的信息学工具- 制作，与工作流程相结合，并允许我们从临床经验中学习。 方法：目的1A.将开发和测试用于分诊和抗生素选择的实践变化模型，以及B. 将在VA系统中确定具有高和低决策阈值的急诊部门。目的 2将使用混合的方法来表征临床推理和语境影响的认知过程 通过与供应商的访谈影响决策。目标3将利用实时信息学工具来测试 当前最佳实践建议反对医生的决定，为医生提供机会， 不同意任何建议，并收集关于不同意原因的信息。 影响：这项工作将直接为VA肺炎信息学工具的开发提供信息。 最终，虽然我的临床兴趣是在肺炎，我开发的技能将应用于许多临床 医学中的问题，其中决策发生在不确定性的设置。

Introduction: This proposal aims to understand and improve the care of Veterans treated for pneumonia through informatics. Background: Pneumonia is the most common infectious cause of death in the United States, with an estimated 50,000 deaths and $8-10 billion in costs annually.12 Twenty-five thousand Veterans are seen per year in the Emergency Department, and 20,000 are hospitalized.13 Risk adjusted 30-day mortality rates range among VA facilities from 11% to 18%. The resources utilized for pneumonia also vary widely, with the cost of hospital care estimated at up to 25 times that of outpatient care.20 To improve the standard of pneumonia care, intense research and quality improvement efforts have been focused upon establishing evidence-based practice guidelines for pneumonia that represent a consensus of research and opinions generated from our best academic hospitals and pneumonia experts. However, we continue to see variation in both management and outcomes across the VA as in other systems, suggesting that guidelines may be difficult to apply to real practice. The recent advancement of our electronic health record allows us to measure actual practice at both the population and the individual level. By studying variation in triage and antibiotic decisions for patients with pneumonia, we can learn from our own population to generate evidence that includes the previously underrepresented patients, settings and scenarios, bringing it closer to real practice. Career Aims: My career goals are to: 1) Develop skills in advanced population analytics, 2) Gain a foundation in behavioral sciences, and 3) Develop a deeper understanding in informatics. Research Aims: My research aims build toward my career aims: (1) Examine sources of variation in triage and antibiotic selection decisions for pneumonia across VA emergency departments. (2) Characterize the cognitive processes influencing decision-making in pneumonia among providers at emergency departments demonstrating variation in decision-making. (3) Design and test an informatics tool that supports decision- making, integrates with workflow, and allows us to learn from clinical experience. Methods: Aim 1 A. will develop and test models of practice variation for triage and antibiotic selection and b. will identify emergency departments with high and low decision-making thresholds across the VA system. Aim 2 will use mixed methods to characterize the cognitive process of clinical reasoning and contextual influences impact decision-making through interviews with providers. Aim 3 will utilize a real-time informatics tool to test current best practice recommendations against physician decisions by providing physicians the opportunity to disagree with any recommendations, and collect information on reasons for disagreement. Impact: This work will directly inform the development of informatics tools for pneumonia for the VA. Ultimately, while my clinical interest is in pneumonia, the skills I develop will be applied to many clinical problems in medicine where decision-making occurs in the setting of uncertainty.