Using risk models to identify and prioritize outpatient 'high-alert' medications

描述(由申请人提供)：安全用药实践研究所(ISMP)已与代表20%美国药店的CVS和Walgreens药店-以及结果工程学公认的风险评估建模领域的领导者-联手为门诊药店分发的高警戒药物设计风险模型。来自可用来源的数据-包括错误数据库、科学文献以及从药物安全专家和焦点小组收集的信息-将被用于识别从门诊药店分发的药物的初步清单，这些药物如果涉及用药错误，将具有对患者造成严重伤害或死亡的高风险。将使用四种工具开发这些“高警戒”药物的综合风险模型：过程图、控制系统图、改进的失效模式和影响分析以及社会技术概率风险评估(ST-PRA)。由药剂师组成的焦点小组从便利的药房样本中随机挑选出来，将为构建故障树提供投入，这些故障树将用于创建风险模型。将使用焦点小组和其他可靠的数据来源来估计这些过程中的失败频率、错误结果的严重性和分发频率。对于每个故障树，工程软件(Relex)将识别并排序 所有故障组合导致有害错误的概率。对所产生的风险模型的验证将通过对6家药店进行分层随机抽样的直接观察和访谈来完成，不包括最初建立模型过程中涉及的商店。从经过验证的风险模型中，将确定具有最大可能性造成最大伤害的高警戒药物的子集，并通过专家咨询小组确定降低风险策略的目标。知道哪些药物造成最大伤害的可能性最高，以及最有可能导致这些药物错误的行为和系统特征，门诊药房将能够使用这些模型来评估其风险并相应调整其流程，从而将宝贵的资源引导到最有可能影响患者安全的干预措施中。

DESCRIPTION (provided by the applicant): The Institute for Safe Medication Practices (ISMP) has joined forces with CVS and Walgreens pharmacies representing 20 percent of US pharmacies-and Outcome Engineering-recognized leaders in risk assessment modeling-to design risk models for high-alert medications dispensed from ambulatory pharmacies. Data from available sources-including error databases, scientific literature, and information gleaned from medication safety experts and focus groups-will be used to identify a preliminary list of medications dispensed from ambulatory pharmacies that carry a high risk of causing serious injury or death to a patient if they are involved in a medication error. Comprehensive risk models for these "high-alert" medications will be developed using four tools: process mapping, control system mapping, modified failure mode and effects analysis, and socio-technical probabilistic risk assessment (ST-PRA). Focus groups comprising pharmacists randomly selected from a convenience sample of pharmacies will provide input into the construction of fault trees that will be used to create the risk models. Focus groups and other reliable data sources will be used to estimate the frequency of failures within these processes, the severity of error outcomes, and dispensing frequency. For each fault tree, engineering software (Relex) will identify and rank the probability of all the failure combinations leading up to a harmful error. Validation of the resulting risk models will be accomplished through direct observation and interviews conducted at a stratified, random sample of six pharmacies, excluding stores involved in the initial model-building process. From the validated risk models, a subset of high-alert medications with the greatest probability of causing the greatest amount of harm will be determined and targeted for the identification of risk reduction strategies via an expert advisory panel. Knowing the medications with the highest probability of causing the greatest amount of harm, and the behaviors and system characteristics that are most likely to lead to errors with these medications, ambulatory pharmacies will be able to use the models to assess their risk and adjust their processes accordingly, thereby channeling precious resources into interventions most likely to impact patient safety.