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

通过 CPOE 系统中的指示警报防止错误药物和错误患者错误

• 批准号: 9356494
• 负责人: BRUCE L. LAMBERT
• 金额: $ 39.65万
• 依托单位: NORTHWESTERN UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-30 至 2021-09-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9356494
• 关键词: Preventing; Wrong; Drug; Patient; Errors

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.

住院病人和病人中，错误用药和错误病人错误的发生率约为千分之一。 门诊环境，导致美国每年出现数百万个潜在有害的错误。 通过电子病历（EMR）提醒，帮助防止错误用药和错误患者错误 不幸的是，问题列表通常不准确。 当药品订单不匹配时，指示警报会提示处方者将新问题添加到问题列表中 问题列表还可以促进对错误药物和错误患者错误的自我拦截。 可以通过自动方式测量两种类型的自我拦截事件： (a) 放弃并重新订购——处方者在签署之前开始然后放弃不正确的订单，然后重新订购 针对正确的药物或患者的订单；或 (b) 撤回并重新订购——处方者很快取消了不正确的订单 签署后，然后重新订购正确的药物或患者。以前的工作使用放弃并重新订购。 以及收回和重新排序方法来衡量多种干预措施的有效性，包括适应症 警报，以减少错误药物和错误患者的错误，但这项工作首先是有限的。 其次，先前的药物研究是在一个医疗中心进行的，并且只涉及一个。 第三，直到 2016 年，还没有经过验证的、国家质量论坛认可的工具。 第四，先前的适应症警报研究仅使用后测试。 设计，因此无法测试自拦截率相对基线的增加。 项目解决了早期工作中的这些限制，并填补了有关如何预防的知识空白 错误的药物和错误的患者错误以及如何提高问题列表的完整性。 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， 针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 个指示警报。 2. 使用间断时间序列研究设计，量化指示警报对 (a) 组合速率的影响 自我拦截的错误药物和错误患者计算机化处方者医嘱输入 (CPOE) 错误的数量以及 (b) 单独查看每种类型的错误率预计指示警报将增加组合的错误率。 自我拦截错误药物和错误患者错误的发生率降低约 25%，从每 100,000 人中 158 起事件减少到 196 起 订单，并且在单独查看时会增加每种类型的自拦截率（通过测量） 放弃并重新订购和撤回并重新订购事件总数的增加 3. 评估指示的影响。 关于在包含 CPOE 的情况下将新诊断添加到问题列表的可能性的警报。 预测指示警报将使问题被列入问题列表的可能性加倍 包括 CPOE 在内的遭遇，其中 12% 的干预前订单和 25% 的订单中出现了新问题 干预后的命令应增加知识并提高质量和患者安全。