Counter Bias Training Simulation (CBTsim) Healthcare: A Novel Approach for Reducing the Impact of Implicit Bias on Healthcare Delivery

摘要 临床医生与患者建立关系和治疗患者的方式存在偏见，这是基于患者种族、民族、 性别、社会经济地位、LGBTQ+地位、残疾、成瘾和其他因素。隐式或 无意识的偏见(我们的信仰、态度和价值观影响我们看待世界的方式 以及其中的人)广泛存在，并影响患者的健康结果。我们的团队已经开发出了计数器 偏向培训模拟(CBTsim)，一种创新和独特的培训计划，使用模拟来 减少隐性偏见对人们如何互动和做出影响他人的决策的影响。版本 已为警务和军事专业人员开发，以增强他们与不同类型的 以不偏不倚的方式组织公民团体。CBTsim与医疗保健有很大的相关性，对此存在偏见 培训通常是以视频或讲座为基础的，在减少偏见对 患者-临床医生关系(PCR)和随之而来的医疗保健服务。这项研究的目的是 开发“CBTsim Healthcare”并评估其在减少护士对待护士的偏见方面的有效性 病人。这项拟议的研究将在华盛顿州立大学护理学院联合进行 斯波坎的模拟实验室和普罗维登斯医疗中心。一是发展CBTsim Healthcare 情景基于对有关医疗保健差异的文献的广泛回顾。然后，我们将进行一次 对100名护士进行随机对照试验，以检验CBTsim Healthcare的有效性。护士将会收到 2小时的基线测试，然后50人(治疗组)将接受4小时的CBTsim医疗培训和 另外50人(控制组)将观看一段1小时的关于医疗保健中的隐性偏见的视频，这是目前的典型情况 这是标准做法。然后，所有100名护士将接受2小时的干预后测试。测试将包括 测量内隐偏见的内隐联想测验(IAT)，测量偏见的问卷，以及患者 使用模拟人体模型测试PCR和医疗保健其他方面的偏差的护理场景 送货。最后，我们将跟踪治疗和对照组护士在医院的6个月后 使用患者护理满意度量表评估医疗保健差异的干预措施 (定量测量)和用于记录PCR经验的叙述(定性测量)。全部主修 卫生机构已经确定了医疗保健中隐性偏见的减少以及由此产生的少数群体健康 不平等是一个极其重要和紧迫的问题。健康差距的经济影响是 估计每年2300亿美元，社会正义的影响是不可估量的。我们目前对护士的关注是 由于我们现有的关系(例如，在AHRQ R01 HS025965-01上)，然而，我们预计CBTsim 医疗保健可以针对其他医疗保健专业群体进行修改，并可能产生革命性的 对减少全国医疗保健服务中的偏见的影响。

ABSTRACT Bias in how clinicians form relationships with and treat patients exists based on patient race, ethnicity, gender, socio-economic status, LGBTQ+ status, disability, addiction and other factors. Implicit or unconscious biases (the ways in which our beliefs, attitudes, and values influence how we see the world and the people in it) are widespread and affect patients’ health outcomes. Our team has developed Counter Bias Training Simulation (CBTsim), an innovative and unique training program that uses simulation to reduce the impact of implicit bias on how people interact and make decisions that affect others. Versions have been developed for policing and military professionals to enhance their ability to interact with diverse groups of citizens in unbiased ways. CBTsim has great relevance to healthcare, for which existing bias trainings are typically video or lecture based, and may be ineffective at reducing the impact of bias on patient-clinician relationships (PCR) and consequent healthcare delivery. The purpose of this study is to develop “CBTsim Healthcare” and evaluate its effectiveness at reducing bias in how nurses treat their patients. The proposed study will be jointly conducted in the Washington State University College of Nursing Simulation Lab and Providence Medical Center in Spokane. First, we will develop CBTsim Healthcare scenarios based on an extensive review of the literature on healthcare disparities. Then, we will conduct a randomized control trial with 100 nurses to test the effectiveness of CBTsim Healthcare. Nurses will receive 2 hours of baseline testing, then 50 (treatment group) will receive a 4-hour CBTsim Healthcare training and the other 50 (control group) will watch a 1-hour video on implicit bias in healthcare, typical of current standard practice. Then, all 100 nurses will receive 2 hours of post-intervention testing. Testing will include the implicit association test (IAT) to measure implicit bias, questionnaires to measure prejudice, and patient care scenarios using simulation mannequins to test for bias in PCR and other aspects of healthcare delivery. Finally, we will track treatment and control group nurses in the hospital for 6 months following the intervention to assess disparities in healthcare, measured using patient satisfaction with nursing care scales (quantitative measure) and narratives to document experience of PCR (qualitative measure). All major health agencies have identified reduction of implicit bias in healthcare and resulting minority health disparities as a matter of extreme importance and urgency. The economic impact of health disparities is an estimated $230 billion a year, and the social justice impact is immeasurable. Our current focus on nurses is due to our existing relationship (e.g., on AHRQ R01 HS025965-01), however we anticipate that CBTsim Healthcare could be modified for other healthcare professional groups and could have a revolutionary impact on reducing bias in healthcare delivery nationwide.