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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

基本信息

批准号：
10013218
负责人：
BRUCE L. LAMBERT
金额：
$ 39.16万
依托单位：
NORTHWESTERN UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2016
资助国家：
美国
起止时间：
2016-09-30 至 2022-09-29
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10013218
关键词：
Preventing Wrong Drug Patient Errors

项目摘要

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.

错误的药物和错误的病人错误发生率约为千分之一的订单在住院和门诊设置，导致数百万潜在的有害错误，每年在美国。准确的问题列表通过允许电子病历（EMR）提醒，当订单与问题列表不匹配时，处方医生会进行检查。不幸的是，问题列表往往不准确。当药物订单不匹配时，指示警报提示处方医生将新问题添加到问题列表中问题列表。适应症警报还促进了对错误药物和错误患者错误的自我拦截，提高形势意识。可以自动测量两种类型的自拦截事件： (a)放弃和重新排序-开处方者开始，然后在签署之前放弃不正确的订单，然后重新排序- 正确药物或患者的订单;或（B）撤回和重新订购-处方者很快取消不正确的订单然后再给正确的药物或病人重新订购。以前的工作使用的放弃和重新排序和撤回和重新排序方法来衡量几种干预措施的有效性，包括适应症警报，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，只有少数药物进行了研究。其次，以前的研究是在一个单一的医疗中心进行的，只涉及一个商业EHR第三，直到2016年，没有经过验证的，国家质量论坛认可的仪器，估计错误医嘱的比例第四，适应症警报的先前研究仅使用后测设计，因此无法测试自拦截率超过基线的增加。拟议该项目解决了早期工作中的这些局限性，填补了有关如何预防错误的药物和错误的病人错误，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，使用两种商业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.使用中断时间序列研究设计，量化适应症警报对（a）合并发生率的影响自我拦截的错误药物和错误患者计算机处方者订单输入（CPOE）错误和（B）每种类型的错误率分别查看。据预测，适应症警报将增加自我截获错误药物和错误患者错误的发生率约为25%，从每10万例158例事件降至196例命令，并将增加每种类型的自我拦截率时，单独查看，作为衡量，放弃并重新排序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的问题时，针对向问题列表中添加新诊断的可能性发出警报。是预测，指示警报将使问题被列入问题列表的可能性增加一倍，包括CPOE在内的遭遇，在干预前订单中有12%和25%的新问题干预后的命令。干预措施应增加知识，提高质量和患者安全。