Manifestations and implications of uncertainty for improving healthcare systems: an analysis of observational and interventional studies grounded in complexity science.

Luci K Leykum,Paul A Nutting,William L Miller,Reuben R Mcdaniel,Benjamin F Crabtree,Michael Parchman,Kurt C Stange,Holly J Lanham,Jacqueline A Pugh,Ruth A Anderson

doi:10.1186/s13012-014-0165-1

Abstract

BackgroundThe application of complexity science to understanding healthcare system improvement highlights the need to consider interdependencies within the system. One important aspect of the interdependencies in healthcare delivery systems is how individuals relate to each other. However, results from our observational and interventional studies focusing on relationships to understand and improve outcomes in a variety of healthcare settings have been inconsistent. We sought to better understand and explain these inconsistencies by analyzing our findings across studies and building new theory.MethodsWe analyzed eight observational and interventional studies in which our author team was involved as the basis of our analysis, using a set theoretical qualitative comparative analytic approach. Over 16 investigative meetings spanning 11 months, we iteratively analyzed our studies, identifying patterns of characteristics that could explain our set of results.Our initial focus on differences in setting did not explain our mixed results. We then turned to differences in patient care activities and tasks being studied and the attributes of the disease being treated. Finally, we examined the interdependence between task and disease.ResultsWe identified system-level uncertainty as a defining characteristic of complex systems through which we interpreted our results. We identified several characteristics of healthcare tasks and diseases that impact the ways uncertainty is manifest across diverse care delivery activities. These include disease-related uncertainty (pace of evolution of disease and patient control over outcomes) and task-related uncertainty (standardized versus customized, routine versus non-routine, and interdependencies required for task completion).ConclusionsUncertainty is an important aspect of clinical systems that must be considered in designing approaches to improve healthcare system function. The uncertainty inherent in tasks and diseases, and how they come together in specific clinical settings, will influence the type of improvement strategies that are most likely to be successful. Process-based efforts appear best-suited for low-uncertainty contexts, while relationship-based approaches may be most effective for high-uncertainty situations.Electronic supplementary materialThe online version of this article (doi:10.1186/s13012-014-0165-1) contains supplementary material, which is available to authorized users.

Highlights

The application of complexity science to understanding healthcare system improvement highlights the need to consider interdependencies within the system
Two studies were excluded from the sample we analyzed: one because it was an evaluation of the National Demonstration Project, an intervention to implement the patientcentered medical home model of care launched by the American Academy of Family Practice that was unrelated to our other work, and the other because it looked at relationships between and among practices and was duplicative with other studies in terms of setting and outcomes [33,34]
We found that interventions that were more congruent with a complexity science approach were more likely to be effective, but the specific aspects of complexity science that were important in intervention success differed between diabetes and congestive heart failure

Summary

Introduction

The application of complexity science to understanding healthcare system improvement highlights the need to consider interdependencies within the system. Examples include the application of manufacturing and engineering principles such as Six Sigma and Lean Management [9], as well as greater efforts to understand how local contexts influence outcomes of improvement initiatives [10]. In another approach, complexity science is being applied to healthcare settings [11,12,13]. System outcomes emerge from the interactions among elements of the system and from the local patterns of self-organization [19,20] These outcomes in turn create feedback loops that impact how the system evolves over time [16]. Self-organization, feedback loops, and the evolutionary nature of complex systems contribute to their unpredictability [20]

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