Use of electronic data to improve risk adjustment for antibiotic utilization metrics

在美国，每年至少有200万人感染耐抗生素细菌和 至少有2.3万人死于这一直接后果。过度使用抗生素是导致肺炎出现的一个关键因素。 抗药性细菌。这导致联邦机构建议急性护理机构 使用国家质量论坛执行抗菌药物管理计划并记录抗菌药物使用数据 (NQF)认可的疾病控制和预防中心(CDC)国家医疗安全网络 (NHSN)抗菌药物使用措施。许多人认为，抗菌药物利用数据将不可避免地 用于评价医院的绩效，并作为绩效工资的结果。知识上的差距是 疾控中心目前用于抗菌药物使用数据的风险调整方法不是最优的，因为 不存在针对患者并存情况进行调整的方法。我们的长期目标是提高质量和 公开报道的医疗质量指标的有效性，包括医疗保健相关感染和 抗菌药物利用数据。这项建议的总体目标是确定哪些并存情况 应用于抗菌药物使用数据的风险调整。我们的中心假设是并存 由ICD代码识别的疾病，可从美国各地的医院通过电子方式轻松获得 国家可以用来改进抗菌剂使用指标的风险调整。这样做的理由是 建议需要进一步改进抗菌药物使用的风险调整方法 指标。现在是制定适当的抗菌药物使用风险调整措施的理想时机，因为 CMS尚未将抗生素使用纳入其基于价值的采购系统。并存情况是一种 合乎逻辑的起点，因为它们已被证明是其他传染病结果的重要预测因素 而且很容易获得。我们计划测试我们的中心假设，从而实现这一目标 通过追求以下具体目标提出建议：目标1：对入院的成年患者进行队列研究 全美多家医院确定哪些通过电子手段获得的合并症是有风险的 不同抗生素利用指标的影响因素。目标2：证明使用共同竞标进行风险调整 条件影响医院抗菌药物使用的排名。这项研究的预期结果是 使用ICD代码识别可用于风险调整抗菌药物使用的并存情况 数据。美国疾病控制与预防中心和社区卫生服务中心实施这些措施将产生更多有效的公开数据。这个 我们研究的意义在于，它将识别容易获得的电子共病条件，这些条件可能 用于更好地风险调整这些抗生素利用指标。拟议的研究是创新的，因为没有 其中一个已经探索了ICD代码用于抗菌药物使用数据的风险调整。这部作品 挑战现有的抗菌剂使用指标风险调整范式。此外，这个项目 有可能显著影响抗菌药物的使用指标和按绩效付费的方法。

Each year in the United States at least 2 million people become infected with antibiotic-resistant bacteria and at least 23,000 people die as a direct result. Overuse of antibiotics is a key factor driving the emergence of antibiotic-resistant bacteria. This has led federal agencies to recommend acute care facilities have antimicrobial stewardship programs and record antimicrobial utilization data using the National Quality Forum (NQF)-endorsed Centers for Disease Control and Prevention (CDC) National Healthcare Safety Network (NHSN) Antimicrobial Use Measure. Many believe that it is inevitable that antimicrobial utilization data will be used to judge performance of hospitals and as a pay-for-performance outcome. The gap in knowledge is that current risk adjustment methods used by the CDC for antimicrobial utilization data are sub-optimal because methods to adjust for patient comorbid conditions do not exist. Our long-term goal is to improve the quality and validity of publicly reported metrics for healthcare quality including healthcare-associated infection and antimicrobial utilization data. The overall objective of this proposal is to determine which comorbid conditions should be used for risk adjustment of antimicrobial utilization data. Our central hypothesis is that comorbid conditions identified by ICD codes that are easily obtained electronically from hospitals across the United States can be used to improve risk adjustment of antimicrobial utilization metrics. The rationale for this proposal is the need for further advancement in risk adjustment methodology for antimicrobial utilization metrics. Now is the ideal time to establish appropriate risk adjustment measures for antimicrobial use because CMS has not yet incorporated antibiotic use into its value-based purchasing system. Comorbid conditions are a logical starting point as they have been proven to be significant predictors of other infectious disease outcomes and are easy to obtain. We plan to test our central hypothesis and, thereby, accomplish the objective of this proposal by pursuing the following specific aims: Aim 1: Perform a cohort study of adult patients admitted to multiple hospitals across the United States to determine which electronically obtained comorbidities are risk factors for different antibiotic utilization metrics. Aim 2: Demonstrate that risk adjustment using comorbid conditions affect hospital rankings of antimicrobial utilization. The expected outcome of this research is the identification of comorbid conditions using ICD codes for that can be used to risk adjust antimicrobial utilization data. The implementation of these by the CDC and CMS will lead to more valid publically available data. The significance of our research is that it will identify easily available electronically comorbid conditions that could be used to better risk adjust these antibiotic utilization metrics. The proposed research is innovative in that no one has explored the use of ICD codes for risk adjustment of antimicrobial utilization data. This work challenges the existing paradigm of risk adjustment of antimicrobial utilization metrics. In addition, this project has the potential to significantly impact antimicrobial utilization metrics and pay-for-performance methods.