Kianmehr Hamed, Sabounchi Nasim S, Sabounchi Shabnam Seyedzadeh, Cosler Leon E
Thomas J. Watson School of Engineering and Applied Science, Binghamton University, Binghamton, New York.
College of Community and Public Affairs, Binghamton University, Binghamton, New York.
J Eval Clin Pract. 2020 Jun;26(3):1054-1064. doi: 10.1111/jep.13203. Epub 2019 Jun 17.
RATIONALE, AIMS, AND OBJECTIVES: Inappropriate antibiotic prescribing is still a major concern that can lead to devastating outcomes including antibiotic resistance. This study aimed to simulate the antibiotic prescribing behaviour by providers for acute respiratory tract infections (ARTIs) and to evaluate the impact of patient expectation, provider's perception of patient's expectation to receive a prescription, and patient's risk for bacterial infection, on the decision to prescribe.
We developed a unique system dynamics (SD) simulation model based on the significant factors that impact the interaction between provider and patient during visits for ARTIs and the decision to prescribe antibiotics. In order to validate the model for different age groups and regions in the United States, we used the sample of 53 000 ARTI patient visits made at outpatient settings between 1993 and 2015, based on the National Ambulatory Medical Care Survey (NAMCS).
Simulation results reveal that physician diagnosis for prescribing antibiotics is based on physician's experience from their prior prescribing behaviour, their perception of patient's infection risk, and patient's expectation to receive antibiotics. Also, there are some variations depending on patient's age and residential region. The simulation analysis also depicts the decreasing trend in patient's expectation over the past two decades for most age groups and regions.
Given the high number of unnecessary prescriptions for ARTI, we found that policies are needed to influence provider's prescribing behaviour through patient's expectation and provider's perception regarding those expectations. Our simulation framework can further be used by policymakers to design and evaluate interventions that may modify the interaction between health providers and patients to optimize antibiotic prescriptions among ARTI patients for different regions and age groups.
原理、目的和目标：不恰当的抗生素处方仍然是一个主要问题，可能导致包括抗生素耐药性在内的灾难性后果。本研究旨在模拟医疗服务提供者针对急性呼吸道感染（ARTIs）的抗生素处方行为，并评估患者期望、医疗服务提供者对患者接受处方的期望认知以及患者细菌感染风险对处方决策的影响。
我们基于影响急性呼吸道感染就诊期间医疗服务提供者与患者互动以及抗生素处方决策的重要因素，开发了一个独特的系统动力学（SD）模拟模型。为了验证该模型在美国不同年龄组和地区的有效性，我们使用了基于国家门诊医疗调查（NAMCS）的1993年至2015年间门诊设置的53000例急性呼吸道感染患者就诊样本。
模拟结果显示，医生开具抗生素的诊断基于其先前处方行为的经验、对患者感染风险的认知以及患者对抗生素的期望。此外，根据患者年龄和居住地区存在一些差异。模拟分析还描绘了过去二十年中大多数年龄组和地区患者期望的下降趋势。
鉴于急性呼吸道感染存在大量不必要的处方，我们发现需要通过患者期望以及医疗服务提供者对这些期望的认知来制定政策，以影响医疗服务提供者的处方行为。我们的模拟框架可进一步被政策制定者用于设计和评估干预措施，这些干预措施可能会改变医疗服务提供者与患者之间的互动，以优化不同地区和年龄组急性呼吸道感染患者的抗生素处方。
