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Organizational complexity and demographic scale in primary states.

ABSTRACT: The relationship between organizational complexity and demographic scale is an enduring research problem at the intersection of the natural and social sciences and has far reaching implications for the study of social evolution, particularly the emergence and collapse of complex social organizations such as chiefdoms, states and empires. Anthropological models of social evolution universally assume that population growth plays a critical role in the development of organizational complexity; however, the relationship between organizational complexity and demographic scale has not been formalized and cross-culturally validated. There is a rich yet unsystematized body of diachronic organizational and demographic data describing the evolution of organizational complexity in 10 archaeologically known cases of primary state formation. Using this dataset, this essay proposes and tests a complex network model that describes state societies as discrete, self-similar, hierarchical social networks. The model accurately describes how organizational complexity and population scale in all cases. The complex network architecture of state societies suggests that further advances in our understanding of modern social organization may be found by a deeper investigation of the role of human nature in the evolution of human societies.

SUBMITTER: Sandeford DS

PROVIDER: S-EPMC5990841 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature

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Organizational complexity and demographic scale in primary states.

Sandeford David S DS

Royal Society open science 20180502 5

The relationship between organizational complexity and demographic scale is an enduring research problem at the intersection of the natural and social sciences and has far reaching implications for the study of social evolution, particularly the emergence and collapse of complex social organizations such as chiefdoms, states and empires. Anthropological models of social evolution universally assume that population growth plays a critical role in the development of organizational complexity; howe ...[more]

PMID: 29892345

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Project description:BACKGROUND:Most patients with type 2 diabetes have suboptimal control of their glucose, blood pressure (BP), and lipids - three risk factors for diabetes complications. Although the chronic care model (CCM) provides a roadmap for improving these outcomes, developing theoretically sound implementation strategies that will work across diverse primary care settings has been challenging. One explanation for this difficulty may be that most strategies do not account for the complex adaptive system (CAS) characteristics of the primary care setting. A CAS is comprised of individuals who can learn, interconnect, self-organize, and interact with their environment in a way that demonstrates non-linear dynamic behavior. One implementation strategy that may be used to leverage these properties is practice facilitation (PF). PF creates time for learning and reflection by members of the team in each clinic, improves their communication, and promotes an individualized approach to implement a strategy to improve patient outcomes. SPECIFIC OBJECTIVES:The specific objectives of this protocol are to: evaluate the effectiveness and sustainability of PF to improve risk factor control in patients with type 2 diabetes across a variety of primary care settings; assess the implementation of the CCM in response to the intervention; examine the relationship between communication within the practice team and the implementation of the CCM; and determine the cost of the intervention both from the perspective of the organization conducting the PF intervention and from the perspective of the primary care practice. INTERVENTION:The study will be a group randomized trial conducted in 40 primary care clinics. Data will be collected on all clinics, with 60 patients in each clinic, using a multi-method assessment process at baseline, 12, and 24 months. The intervention, PF, will consist of a series of practice improvement team meetings led by trained facilitators over 12 months. Primary hypotheses will be tested with 12-month outcome data. Sustainability of the intervention will be tested using 24 month data. Insights gained will be included in a delayed intervention conducted in control practices and evaluated in a pre-post design. PRIMARY AND SECONDARY OUTCOMES:To test hypotheses, the unit of randomization will be the clinic. The unit of analysis will be the repeated measure of each risk factor for each patient, nested within the clinic. The repeated measure of glycosylated hemoglobin A1c will be the primary outcome, with BP and Low Density Lipoprotein (LDL) cholesterol as secondary outcomes. To study change in risk factor level, a hierarchical or random effect model will be used to account for the nesting of repeated measurement of risk factor within patients and patients within clinics.

Project description:BackgroundDespite 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.MethodsWe 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).ResultsOut 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.ConclusionThe 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.

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Organizational complexity and demographic scale in primary states.

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Organizational complexity and demographic scale in primary states.

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