South Texas Veterans Health Care System, University of Texas Health Science Center at San Antonio, San Antonio TX, 78229, USA.
Implement Sci. 2007 Aug 28;2:28. doi: 10.1186/1748-5908-2-28.
Despite the development of several models of care delivery for patients with chronic illness, consistent improvements in outcomes have not been achieved. These inconsistent results may be less related to the content of the models themselves, but to their underlying conceptualization of clinical settings as linear, predictable systems. The science of complex adaptive systems (CAS), suggests that clinical settings are non-linear, and increasingly has been used as a framework for describing and understanding clinical systems. The purpose of this study is to broaden the conceptualization by examining the relationship between interventions that leverage CAS characteristics in intervention design and implementation, and effectiveness of reported outcomes for patients with Type II diabetes.
We conducted a systematic review of the literature on organizational interventions to improve care of Type II diabetes. For each study we recorded measured process and clinical outcomes of diabetic patients. Two independent reviewers gave each study a score that reflected whether organizational interventions reflected one or more characteristics of a complex adaptive system. The effectiveness of the intervention was assessed by standardizing the scoring of the results of each study as 0 (no effect), 0.5 (mixed effect), or 1.0 (effective).
Out of 157 potentially eligible studies, 32 met our eligibility criteria. Most studies were felt to utilize at least one CAS characteristic in their intervention designs, and ninety-one percent were scored as either "mixed effect" or "effective." The number of CAS characteristics present in each intervention was associated with effectiveness (p = 0.002). Two individual CAS characteristics were associated with effectiveness: interconnections between participants and co-evolution.
The significant association between CAS characteristics and effectiveness of reported outcomes for patients with Type II diabetes suggests that complexity science may provide an effective framework for designing and implementing interventions that lead to improved patient outcomes.
尽管已经开发出几种用于慢性病患者的护理提供模式,但并未实现结果的持续改善。这些不一致的结果可能与模型本身的内容关系不大,而是与将临床环境概念化为线性、可预测系统的基本原理有关。复杂适应系统(CAS)科学表明,临床环境是非线性的,并且越来越多地被用作描述和理解临床系统的框架。本研究的目的是通过检查利用干预设计和实施中的 CAS 特征的干预措施与报告的 II 型糖尿病患者结果的有效性之间的关系,来拓宽概念化。
我们对改善 II 型糖尿病护理的组织干预措施的文献进行了系统回顾。对于每项研究,我们记录了糖尿病患者的测量过程和临床结果。两位独立的审阅者为每项研究评分,反映了组织干预措施是否反映了复杂适应系统的一个或多个特征。通过将每项研究的结果评分标准化为 0(无效果)、0.5(混合效果)或 1.0(有效),来评估干预的有效性。
在 157 项可能符合条件的研究中,有 32 项符合我们的入选标准。大多数研究被认为在其干预设计中至少利用了一个 CAS 特征,并且 91%的研究被评为“混合效果”或“有效”。每个干预措施中存在的 CAS 特征数量与有效性相关(p = 0.002)。两个单独的 CAS 特征与有效性相关:参与者之间的相互联系和共同进化。
CAS 特征与 II 型糖尿病患者报告结果的有效性之间的显著关联表明,复杂性科学可能为设计和实施可改善患者结果的干预措施提供有效的框架。