Risk Models to Improve Long-Term Care Medication Safety

提高长期护理用药安全性的风险模型

• 批准号: 6780685
• 负责人: GRANT K HIGGINSON
• 金额: $ 1.65万
• 依托单位: OREGON STATE DEPT OF HUMAN SERVICES
• 依托单位国家: 美国
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-09-30 至 2004-09-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6780685
• 关键词: Risk; Models; Improve; Long; Term

DESCRIPTION: (Provided by the Applicant) Most patient safety improvement occurs incrementally within single institutions. A new method to develop comprehensive statewide risk models that could be exported to large segments of the health care industry is evaluated. Working hypothesis: Sociotechnical probabilistic risk assessment (ST-PRA) can create risk models identifying common medication system and behavioral elements that raise the risk of serious errors and these risk models can be used to design statewide risk reduction programs for nursing and community based care (CBD) long term care facilities. These facilities need robust, well-designed medication systems because they serve a growing and often frail population, administer highly toxic drugs, and must perform to high standards using an unstable and sometimes minimally skilled labor force. Design: Developmental study. Methods: This project uses four tools--process mapping, control system mapping, failure modes and effects analysis (FMEA), and socio-technical probabilistic risk assessment (ST-PRA)--to create two comprehensive probability risk assessment (ST-PRA) models, one for nursing facilities (NFs) and one for CBC (residential care/assisted living) facilities, to identify processes and behaviors that increase the risk of wrong drug, wrong dose, wrong patient medication delivery errors in LTC facilities. The NF risk assessment model is created by focus groups of staff, pharmacists, and physicians from nine randomly selected facilities in a stratified sample of three large, volunteer LTC chains. The CBC process is similar, with nine randomly drawn CBC facilities. Focus groups of CBC residents and their families will be invited to provide input into the CBC model. Appropriate human subjects and privacy protections will be in place. Models are validated in stratified, random samples of nursing and community-based care facilities to determine whether each model is representative of medication delivery systems in the respective types of facilities, using a combination of structured focus groups and direct observation. Recommendations for interventions to address the systems and behavioral risks identified will be made and lessons learned while undertaking this large-scale, multi-facility ST-PRA project will be reported.

描述：（由申请人提供）大多数患者安全性改善在单个机构内逐渐发生。 一种新的方法来开发全面的全州范围内的风险模型，可以出口到大部分的医疗保健行业进行评估。 工作假设：社会技术概率风险评估（ST-PRA）可以创建风险模型，识别常见的药物系统和行为元素，提高严重错误的风险，这些风险模型可以用来设计全州范围内的风险降低计划，护理和社区护理（CBD）长期护理设施。这些设施需要强大的，精心设计的药物系统，因为他们服务于不断增长的，往往是脆弱的人口，管理剧毒药物，必须使用不稳定的，有时是最低技能的劳动力来执行高标准。设计：发展性研究。研究方法：本计画使用四种工具--流程图、控制系统图、失效模式与影响分析（FMEA）、社会技术机率风险评估（ST-PRA）--建立两个综合机率风险评估（ST-PRA）模型，一个用于护理设施（NF），一个用于CBC（住宿护理/辅助生活）设施，以确定增加错误药物，错误剂量风险的过程和行为，LTC机构中的错误患者药物输送错误。NF风险评估模型是由工作人员，药剂师和医生的焦点小组从九个随机选择的设施在三个大型，志愿者LTC链分层样本创建的。CBC过程类似，有9个随机抽取的CBC设施。CBC居民及其家庭的焦点小组将被邀请为CBC模式提供投入。适当的人类受试者和隐私保护将到位。模型进行了验证，分层，随机样本的护理和社区护理设施，以确定每个模型是否是代表药物输送系统在各自类型的设施，使用结构化的焦点小组和直接观察的组合。将提出干预措施的建议，以解决所确定的系统和行为风险，并报告在开展这一大规模、多设施的ST-PRA项目时吸取的经验教训。