Understanding U.S. Health Systems: Using Mixed Methods to Unpack Organizational Complexity

M. Ridgely, E. Duffy, Laura J. Wolf, M. Vaiana, D. Scanlon, Christine Buttorff, Brigitt Leitzell, S. Ahluwalia, L. Hilton, D. Agniel, A. Haviland, C. Damberg
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引用次数: 17

Abstract

Introduction: As hospitals and physician organizations increasingly vertically integrate, there is an important opportunity to use health systems to improve performance. Prior research has largely relied on secondary data sources, but little is known about how health systems are organized “on the ground” and what mechanisms are available to influence physician practice at the front line of care. Methods: We collected in-depth information on eight health systems through key informant interviews, descriptive surveys, and document review. Qualitative data were systematically coded. We conducted analyses to identify organizational structures and mechanisms through which health systems influence practice. Results: As expected, we found that health systems vary on multiple dimensions related to organizational structure (e.g., size, complexity) which reflects history, market and mission. With regard to levers of influence, we observed within-system variation both in mechanisms (e.g., employment of physicians, system-wide EHR, standardization of service lines) and level of influence. Concepts such as “core” versus “peripheral” were more salient than “ownership” versus “contract.” Discussion: Data from secondary sources can help identify and map health systems, but they do not adequately describe them or the variation that exists within and across systems. To examine the degree to which health systems can influence performance, more detailed and nuanced information on health system characteristics is necessary. Conclusion: The mixed-methods data accrual approach used in this study provides granular qualitative data that enables researchers to describe multi-layered health systems, grasp the context in which they operate, and identify the key drivers of performance.
了解美国卫生系统:用混合方法破解组织复杂性
引言:随着医院和医生组织日益垂直整合,利用卫生系统来提高绩效是一个重要的机会。先前的研究在很大程度上依赖于二级数据来源,但对卫生系统是如何“实地”组织的,以及有什么机制可以影响医疗一线的医生实践,却知之甚少。方法:我们通过关键信息提供者访谈、描述性调查和文献综述,收集了八个卫生系统的深入信息。对定性数据进行了系统编码。我们进行了分析,以确定卫生系统影响实践的组织结构和机制。结果:正如预期的那样,我们发现卫生系统在与反映历史、市场和使命的组织结构(如规模、复杂性)相关的多个维度上存在差异。关于影响杠杆,我们观察到系统内机制(例如,医生的雇用、全系统的EHR、服务线的标准化)和影响水平的变化。“核心”与“外围”等概念比“所有权”与“合同”更为突出。讨论:来自二级来源的数据可以帮助识别和绘制卫生系统,但它们不能充分描述它们或系统内部和系统之间存在的变化。为了研究卫生系统对绩效的影响程度,有必要提供关于卫生系统特征的更详细、更细致的信息。结论:本研究中使用的混合方法-数据累积方法提供了细粒度的定性数据,使研究人员能够描述多层次的卫生系统,掌握其运作的背景,并确定绩效的关键驱动因素。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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