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Selecting interventions to improve patient-relevant outcomes in health care for aortic valve disease – the Intervention Selection Toolbox
BMC Health Services Research ( IF 2.8 ) Pub Date : 2020-03-19 , DOI: 10.1186/s12913-020-05090-z
Nina Zipfel , A. Stef Groenewoud , Benno J. W. M. Rensing , Edgar J. Daeter , Lea M. Dijksman , Jan-Henk E. Dambrink , Philip J. van der Wees , Gert P. Westert , Paul B. van der Nat

Measuring and improving outcomes is a central element of value-based health care. However, selecting improvement interventions based on outcome measures is complex and tools to support the selection process are lacking. The goal was to present strategies for the systematic identification and selection of improvement interventions applied to the case of aortic valve disease and to combine various methods of process and outcome assessment into one integrated approach for quality improvement. For this case study a concept-driven mixed-method approach was applied for the identification of improvement intervention clusters including: (1) benchmarking outcomes, (2) data exploration, (3) care delivery process analysis, and (4) monitoring of ongoing improvements. The main outcome measures were long-term survival and 30-day mortality. For the selection of an improvement intervention, the causal relations between the potential improvement interventions and outcome measures were quantified followed by a team selection based on consensus from a multidisciplinary team of professionals. The study resulted in a toolbox: the Intervention Selection Toolbox (IST). The toolbox comprises two phases: (a) identifying potential for improvement, and (b) selecting an effective intervention from the four clusters expected to lead to the desired improvement in outcomes. The improvements identified for the case of aortic valve disease with impact on long-term survival in the context of the studied hospital in 2015 include: anticoagulation policy, increased attention to nutritional status of patients and determining frailty of patients before the treatment decision. Identifying potential for improvement and carefully selecting improvement interventions based on (clinical) outcome data demands a multifaceted approach. Our toolbox integrates both care delivery process analyses and outcome analyses. The toolbox is recommended for use in hospital care for the selection of high-impact improvement interventions.

中文翻译:

选择干预措施以改善主动脉瓣疾病的患者相关医疗结果–干预选择工具箱

衡量和改善结果是基于价值的医疗保健的核心要素。但是,基于结果度量选择改进干预措施很复杂,并且缺乏支持选择过程的工具。目标是提出策略,用于系统地识别和选择适用于主动脉瓣疾病的改善干预措施,并将各种过程和结果评估方法结合为一种提高质量的综合方法。在本案例研究中,采用了一种概念驱动的混合方法方法来识别改善干预措施集群,包括:(1)基准结果,(2)数据探索,(3)护理过程分析以及(4)持续监控改进。主要结局指标是长期生存率和30天死亡率。在选择改进干预措施时,对潜在的改进干预措施与结果度量之间的因果关系进行了量化,然后根据多学科专业团队的共识进行了团队选择。该研究产生了一个工具箱:干预选择工具箱(IST)。该工具箱包括两个阶段:(a)识别改进的潜力,以及(b)从预期将导致预期的结果改善的四个集群中选择有效的干预措施。在2015年所研究的医院中,确定的对主动脉瓣膜疾病影响长期生存的改进措施包括:抗凝政策,对患者营养状况的更多关注以及在做出治疗决定之前确定患者的身体虚弱。识别潜在的改进并基于(临床)结果数据仔细选择改进干预措施,需要采取多方面的方法。我们的工具箱整合了护理过程分析和结果分析。建议将该工具箱用于医院护理,以选择影响较大的改善措施。
更新日期:2020-03-20
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