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.

Public Health Relevance

Antibiotic overuse has widespread and devastating consequences for human health, including adverse effects and antibiotic resistance. Currently in the US, 23,000 people die annually due to infections from resistant organisms, and this number continues to rise. Antibiotic overuse is especially problematic among older adults in nursing homes, where at any one time over 10% of NH residents are taking antibiotics (most commonly for urinary tract infections), and up to 75% of all prescriptions likely represent antibiotic overue. There is an urgent need to preserve the current antibiotic supply and evaluating nurses' and physicians' antibiotic decision- making process around suspected urinary tract infections in nursing home residents is vital to that effort.

National Institute of Health (NIH)
Agency for Healthcare Research and Quality (AHRQ)
Research Project (R01)
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Healthcare Patient Safety and Quality Improvement Research (HSQR)
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Miller, Melissa
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University of North Carolina Chapel Hill
Family Medicine
Schools of Medicine
Chapel Hill
United States
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