Understanding U.S. Health Systems

Using Mixed Methods to Unpack Organizational Complexity

Published in: eGEMs (Generating Evidence & Methods to Improve Patient Outcomes), Volume 7, Number 1, Page 39. doi: 10.5334/egems.302

Posted on RAND.org on November 26, 2019

by M. Susan Ridgely, Erin L. Duffy, Laura J. Wolf, Mary E. Vaiana, Dennis P. Scanlon, Christine Buttorff, Brigitt Leitzell, Sangeeta C. Ahluwalia, Lara Hilton, Denis Agniel, Amelia Haviland, Cheryl L. Damberg

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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.

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