Realist analysis of streaming interventions in emergency departments
Realist analysis of streaming interventions in emergency departments
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DOI：
10.1136/leader-2020-000369
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发表时间：
2021-09-01
期刊：
影响因子：
2.7
通讯作者：
Kreindler, Sara Adi
中科院分区：
文献类型：
--
作者：
Anwar, Mohammed Rashidul;Rowe, Brian H.;Kreindler, Sara Adi
背景：许多急诊科（ED）干预措施旨在解决（过度）拥挤的复杂问题是基于流的原则：将不同的患者群体引导到不同的护理过程。尽管流式治疗的理论基础是坚实的，但关于这些干预措施有效性的证据仍然没有定论。方法本定性研究以人口-能力-过程模型为基础，试图确定流动干预如何、为什么以及在什么条件下可能有效。数据来自一项更广泛的研究，通过对各级管理人员的深入访谈，探索了加拿大西部的患者流量策略。我们对讨论相关干预措施（快速通道/次要治疗区域、快速评估区域、各种短期停留单元）的98名参与者的访谈数据进行了现实分析，重点关注他们对举措感知结果的解释。结果流动干预措施的基本特征包括：隔离指定人群（population）、提供专用空间和资源（capacity）、快速循环时间（process）。这些特征支持关键的影响机制：患者只等待他们需要的服务；减少了患者的可变性；消除了步骤之间的滞后时间；提供者态度的改变促进了及时出院。相反，报告的失败通常是由于在修井设计和/或实施过程中忽视了其中一个方面。与会者还确定了成功的重要背景障碍，特别是缺乏外流地点和需求超过能力。然而，失败通常归因于干预缺陷，而不是环境因素。虽然流干预有可能减少拥挤，但基于理论的干预依赖于其实施者对理论的坚持。如果以一种与它们所依据的理论不一致的方式进行操作，流干预就不能期望产生预期的结果。
Background Several of the many emergency department (ED) interventions intended to address the complex problem of (over)crowding are based on the principle of streaming: directing different groups of patients to different processes of care. Although the theoretical basis of streaming is robust, evidence on the effectiveness of these interventions remains inconclusive.Methods This qualitative research, grounded in the population-capacity-process model, sought to determine how, why and under what conditions streaming interventions may be effective. Data came from a broader study exploring patient flow strategies across Western Canada through in-depth interviews with managers at all levels. We undertook realist analysis of interview data from the 98 participants who discussed relevant interventions (fast-track/minor treatment areas, rapid assessment zones, diverse short-stay units), focusing on their explanations of initiatives' perceived outcomes.Results Essential features of streaming interventions included separation of designated populations (population), provision of dedicated space and resources (capacity) and rapid cycle time (process). These features supported key mechanisms of impact: patients wait only for services they need; patient variability is reduced; lag time between steps is eliminated; and provider attitude change promotes prompt discharge. Conversely, reported failures usually involved neglect of one of these dimensions during intervention design and/or implementation. Participants also identified important contextual barriers to success, notably lack of outflow sites and demand outstripping capacity. Nonetheless, failure was more commonly attributed to intervention flaws than to context factors.Conclusions While streaming interventions have the potential to reduce crowding, a theory-based intervention relies on its implementers' adherence to the theory. Streaming interventions cannot be expected to yield the desired results if operationalised in a manner incongruent with the theory on which they are supposedly based.