Preventing Wrong-Drug and Wrong-Patient Errors with Indication Alerts in CPOE Systems

在住院和住院患者中，错误药物和错误患者错误的发生率约为千分之一。 门诊设置，在美国每年导致数百万潜在的有害差错。准确的问题清单 通过允许电子病历(EMR)提醒，帮助防止错误的药物和错误的患者错误 当订单与问题清单不匹配时，开具处方。不幸的是，问题清单往往是不准确的。 当药品订单不匹配时，指示警报提示处方人员将新问题添加到问题列表中 问题清单。适应症警报还通过以下方式促进对错误药物和错误患者错误的自我拦截 增强态势意识。可以以自动方式测量两种类型的自拦截事件： (A)放弃并重新订购-处方开具人在签署错误的订单之前开始然后放弃，然后重新- 订购正确的药物或患者；或(B)撤回并重新订购-处方人员很快取消了错误的订单 签字后，然后为正确的药物或患者重新订购。以前的工作使用的是放弃和重新排序 以及收回和重新排序方法，以衡量几种干预措施的有效性，包括适应症 警报，在减少错误的药物和错误的病人错误，但这项工作是有限的。首先，只有少数人 对药物进行了研究。其次，之前的研究是在一个医疗中心进行的，只涉及一个 商业电子病历。第三，在2016年之前，没有经过验证的、由国家质量论坛认可的用于 估计误开病人医嘱的比率。第四，前人对指征警示的研究仅采用后测 设计，因此无法测试自我拦截率是否高于基线。建议数 Project解决了早期工作中的这些限制，并填补了有关如何防止 错误的药物和错误的病人，以及如何提高问题列表的完整性。该项目的 具体目标是：1.在芝加哥的一家医院和纽约市的六家医院，使用两个商业EMR系统， 对容易出现外观相似和声音相似错误的药物实施一组30-50个适应症警报。 2.采用中断时间序列研究设计，量化指示警报对(A)合并率的影响 自我截获的错误药品和错误患者的计算机化处方订单录入(CPOE)错误和(B) 单独查看每种类型的错误率。据预测，指示警报将增加组合 自我拦截的错误药物和错误患者的错误率约为25%，从每100,000个事件中的158个事件增加到196个事件 订单，并将增加每种类型的自截获速度时，单独查看，由 放弃并重新排序事件和撤回并重新排序事件的总和增加。3.评估适应症的影响 在遇到包括CPOE的情况时，有关将新诊断添加到问题列表的可能性的警报。它是 预计指示警报会将问题列入问题列表的可能性增加一倍 遇到包括CPOE在内的问题，在12%的干预前订单和25%的干预订单期间出现新问题 干预后的命令。干预应该增加知识，提高质量和患者安全。

Wrong-drug and wrong-patient errors occur at a rate of roughly one per thousand orders in inpatient and outpatient settings, resulting in millions of potentially harmful errors annually in the US. Accurate problem lists help prevent wrong-drug and wrong-patient errors by allowing the electronic medical record (EMR) to remind prescribers when orders do not match the problem list. Unfortunately, problem lists are often inaccurate. Indication alerts prompt prescribers to add new problems to the problem list when a drug order does not match the problem list. Indication alerts also promote self-interception of wrong-drug and wrong-patient errors by increasing situation awareness. Two types of self-interception events can be measured in an automated way: (a) abandon-and-reorder—a prescriber starts then abandons an incorrect order before signing it, and then re- orders for the correct drug or patient; or (b) retract-and-reorder—a prescriber cancels an incorrect order soon after signing it, and then re-orders for the correct drug or patient. Previous work used the abandon-and-reorder and retract-and-reorder methods to measure the effectiveness of several interventions, including indication alerts, in reducing wrong-drug and wrong-patient errors, but that work was limited. First, only a small number of drugs were studied. Second, prior studies were done at a single medical center and involved only one commercial EHR. Third, until 2016, there was no validated, National Quality Forum-endorsed instrument for estimating the rate of wrong-patient orders. Fourth, the prior studies of indication alerts used posttest only designs and therefore could not test for an increase in the self-interception rate over baseline. The proposed project addresses these limitations in the earlier work and fills important gaps in knowledge about how to prevent wrong-drug and wrong-patient errors and how to improve the completeness of problem lists. The project's Specific Aims are: 1. At one hospital in Chicago and six in New York City, using two commercial EMR systems, implement a set of 30-50 indication alerts for medications that are vulnerable to look-alike and sound-alike errors. 2. Using an interrupted time series study design, quantify the effect of indication alerts on (a) the combined rate of self-intercepted wrong-drug and wrong-patient computerized prescriber order entry (CPOE) errors and (b) on the rate of each type of error viewed separately. It is predicted that indication alerts will increase the combined rate of self-intercepted wrong-drug and wrong-patient errors by roughly 25%, from 158 to 196 events per 100,000 orders, and will increase the self-interception rate of each type when viewed separately, as measured by an increase in the sum of abandon-and-reorder and retract-and-reorder events. 3. Assess the impact of indication alerts on the probability of adding new diagnoses to the problem list during encounters that include CPOE. It is predicted that indication alerts will double the likelihood that a problem is placed on the problem list during encounters that include CPOE, with new problems being placed during 12% of pre-intervention orders and 25% of post-intervention orders. The intervention should add to knowledge and improve quality and patient safety.