Demonstration of Near Zero Antibiotic Prescribing for Acute Bronchitis

描述（由申请人提供）：背景：研究表明，指南状态和性能测量表明，非复杂性急性支气管炎的抗菌处方是不合适的。然而，在美国每年2250万例急性支气管炎就诊中，临床医生给超过60%的患者开了抗菌剂。以往成功的干预措施虽然在流行病学上是严格的，但仅将抗菌素处方率降低到40%或50%。目前尚不清楚急性支气管炎的抗菌药物处方率是否可以在实际实践中接近零，同时保持患者的安全和满意度。研究计划：该项目的具体目标1是开发一种ehr集成算法，用于诊断和治疗成人急性支气管炎，目标是将抗菌药物处方率降低到接近零。具体目标2是在一个大型、多样化的初级保健实践中，对急性支气管炎进行控制、持续监测、实施ehr综合诊断和治疗算法。我们将采用多模式实施——包括计算机化决策支持、报告工具和临床医生反馈——以及质量改进技术，以确保遵守算法，并将抗菌药物处方率降至接近零。主要结果是急性支气管炎患者的抗菌药物处方率。次要结果包括患者症状、患者满意度、患者安全性和医疗保健成本。为了确保算法和干预措施在未来的有效传播，一个主要结果将是捕获和描述对抗菌药物处方率影响最大的成分。具体目标2a是开发一种独立于电子病历的算法和实施方法，用于没有电子病历的诊所。具体目标3是在11个诊所的初级保健实践研究网络中传播算法和实施方法，并将急性支气管炎的抗菌药物处方率降低到接近零。未来方向和影响：在未来，我们计划广泛传播并继续测试依赖ehr和不依赖ehr版本的算法和实现方法的有效性和安全性。开发、示范和广泛传播一种有效的算法，将急性支气管炎的抗菌药物处方减少到接近零，这有可能减少药物不良事件、医疗成本和抗菌耐药细菌在美国的流行。该项目还将检查各种工具的有效性，以改变门诊实践，可以推广到其他临床条件。

DESCRIPTION (provided by applicant): Background: Studies show, guidelines state, and performance measures assert that antimicrobial prescribing for uncomplicated acute bronchitis is inappropriate. However, clinicians prescribe antimicrobials in over 60% of the 22.5 million acute bronchitis visits in the United States each year. Previous successful interventions, while epidemiologically rigorous, have only reduced the antimicrobial prescribing rate to 40% or 50%. It is unknown if the antimicrobial prescribing rate for acute bronchitis can be brought to near zero percent in actual practice while maintaining patient safety and satisfaction. Research Plan: Specific Aim 1 of this project is to develop an EHR-integrated algorithm for the diagnosis and treatment of adults with acute bronchitis with a goal of reducing the antimicrobial prescribing rate to near zero percent. Specific Aim 2 is to perform a controlled, continuously-monitored, implementation of an EHR-integrated diagnosis and treatment algorithm for acute bronchitis in a large, diverse primary care practice. We would use a multi-modal implementation - including computerized decision support, reporting tools, and clinician feedback - and quality improvement techniques to ensure adherence to the algorithm and reduce the antimicrobial prescribing rate to near zero percent. The primary outcome would be the antimicrobial prescribing rate for patients with acute bronchitis. Secondary outcomes would include patient symptoms, patient satisfaction, patient safety, and healthcare costs. To ensure the effective future dissemination of the algorithm and intervention, a major outcome would be the capture and description of the components that had the greatest effect on the antimicrobial prescribing rate. Specific Aim 2a is to develop an EHR-independent version of the algorithm and implementation method for use in clinics that do not have EHRs. Specific Aim 3 is to disseminate the algorithm and implementation method throughout an 11-clinic Primary Care Practice-Based Research Network and reduce the antimicrobial prescribing rate for acute bronchitis to near zero percent. Future Directions and Implications: In the future, we plan to broadly disseminate and continue to test the effectiveness and safety of the EHR-dependent and EHR-independent versions of the algorithm and implementation method. Development, demonstration, and broad dissemination of an effective algorithm to reduce antimicrobial prescribing to near zero percent for acute bronchitis has the potential to decrease adverse drug events, healthcare costs, and the prevalence of antimicrobial resistant bacteria in the United states. This project will also examine the effectiveness of a variety of tools for changing ambulatory practice that can generalize to other clinical conditions. PUBLIC HEALTH RELEVANCE: Antibiotics do not help patients with acute bronchitis, but doctors prescribe antibiotics in over 60% of the 22.5 million visits for acute bronchitis and previous efforts have only reduced the antibiotic prescribing rate to about 40%. We propose to develop and test a diagnosis and treatment algorithm for acute bronchitis that would reduce the antibiotic prescribing rate to near zero percent, making sure this is safe for patients. By developing, testing, and disseminating such an algorithm, we hope to decrease healthcare costs, adverse drug reactions, and the prevalence of antibiotic-resistant bacteria.