Nurse and Physician Decision-making for Suspected Urinary Tract Infections in Nursing Homes: Potential Targets to Reduce Antibiotic Overuse

 描述（由申请人提供）：在感染管理中过度使用抗生素对人类健康具有广泛和破坏性的后果，包括不良反应和抗生素耐药性。抗生素过度使用在养老院（NHs）的老年人中尤其成问题，在任何时候都有超过10%的人服用抗生素，高达75%的抗生素处方可能代表过度使用。临床决策支持系统（CDSS），其中计算机化的警报和提醒提供了真实的时间，似乎有前途，但迄今为止的结果在NHs一直乏善可陈。这种不一致的一个关键原因可能是NH环境中处方决策的实际决策过程。根据双过程理论，个体通过两种方式做出决策，一种是权衡不同类型的信息，另一种是可能被非证据信息误导的直觉方式。目前的决策支持工具侧重于循证信息，只有考虑到非循证信息的影响，CDSS的全部好处才可能实现。我们建议对1756名提供者进行一项基于互联网的跨部门全国调查，以确定影响NHS抗生素处方的最重要信息，包括证据和非证据信息。为了量化这些不同类型信息的相对重要性，我们将使用离散选择实验（DCE）。由于大多数NH处方决策是基于医生（通常不在现场）和NH护士之间的沟通，因此调查将包括878名护士（检查影响他们与医生沟通抗生素潜在需求的决定的信息）和878名医生（负责处方）。为了评估时间压力对循证和非循证信息相对重要性的影响，受访者将被分为高和低时间压力组。此外，基于研究表明，人格特质与直觉决策有关，我们还探讨了个性与沟通和处方决策的关系。最后，因为有许多情况和条件下，抗生素处方在NH，我们专注于最常见的，即尿路感染（UTI）。总之，本提案的目的是了解最重要的信息，（a）护士沟通有关抗生素相关的决定，（B）医生的相关抗生素处方的决定，NH居民怀疑UTI，以及时间和人格特质的影响，这一信息。我们相信，了解护士和医生实际做出决策的方式将为改善临床决策支持提供更准确的目标。拟议的研究解决了国家优先事项和医疗保健研究和质量机构的使命，重点是NHS的抗生素管理和感染管理。我们的长期目标是开发一个基于互联网的临床决策支持系统，以减少抗生素的过度使用。

 DESCRIPTION (provided by applicant): The overuse of antibiotics in infection management has widespread and damaging consequences for human health, including adverse effects and antibiotic resistance. Antibiotic overuse is especially problematic among older adults in nursing homes (NHs), where at any one time over 10% are taking antibiotics and up to 75% of all antibiotic prescriptions likely represent overuse. Clinical decision support systems (CDSS), in which computerized alerts and reminders are provided in real time, appear promising, but to date results in NHs have been lackluster. A key reason for this poor alignment may be the actual decisional processes that underlie prescribing decisions in the NH setting. According to dual process theory, individuals make decisions through both deliberative ways where they weigh different types of information and intuitive ways where they may be misled by non-evidence-based information. Current decision support tools focus on evidence-based information, and the full benefit of CDSS may only be realized once the influence of non-evidenced-based information is taken into account. We propose to conduct a cross-sectional, internet-based, national survey of 1756 providers to identify the most important information, both evidence and non-evidence-based, influencing antibiotic prescribing in NHs. To quantify the relative importance of these different types of information, we will use a discrete choice experiment (DCE). Because most NH prescribing decisions are based on communications between physicians (who are often offsite) and NH-based nurses, the survey will include 878 nurses (examining the information that influences their decisions to communicate with physicians about the potential need for antibiotics) and 878 physicians (who are responsible for prescribing). To evaluate the effect of time pressure on the relative importance of evidence-based and non-evidence-based information, respondents will be divided into high and low time pressure groups. In addition, based on studies demonstrating that personality traits relate to intuitive decision-making, we also explore personality in relation to communication and prescribing decisions. Finally, because there are many circumstances and conditions for which antibiotics are prescribed in NHs, we focus on the most common, namely urinary tract infections (UTI). In summary, the objectives of this proposal are to understand the information most important to (a) nurses' communication about antibiotic-related decisions, and (b) physicians' related antibiotic prescribing decisions for NH residents with suspected UTIs, and the influence of time and personality traits on this information. We believe that knowledge about the way that nurses' and physicians' actually make decisions will provide more accurate targets for improving clinical decision support. The proposed research addresses a national priority and the mission of the Agency for Healthcare Research and Quality with its focus on antibiotic stewardship and infection management in NHs. Our long-term goal is to develop an internet-based clinical decision support system to reduce antibiotic overuse in NHs.