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Article Title

A Systematic Review of Conceptual Frameworks of Medical Complexity and New Model Development

Publication Date

8-15-2016

Keywords

multimorbidity, resilience

Abstract

Background/Aims: Patient complexity entails multimorbidity as well as interacting contextual factors that affect patient risk for adverse outcomes. Our objective was to develop a conceptual model of factors relating to patients’ medical complexity by addressing gaps identified in a review of published conceptual models.

Methods: We searched for English-language MEDLINE papers published between Jan. 1, 2004, and Jan. 16, 2014. Two reviewers independently evaluated abstracts. All authors contributed to the development of the conceptual model in an iterative process.

Results: From 1,606 identified abstracts, six conceptual models were selected. One additional model was identified through reference review. These models included works by Capobianco and Lio (2013), Giovannetti et al. (2013), Grembowski et al. (2014), Holzhausen et al. (2011), Piette and Kerr (2006), Safford et al. (2007) and Shippee et al. (2010). Each model had strengths, but several constructs were not fully considered: 1) contextual factors such as interpersonal, organizational or community relations; 2) dynamics of complexity as a condition that fluctuates over time; 3) patient preferences; 4) acute health shocks and medical events that influence health; and 5) resilience in the face of such stressors. To build on prior literature while addressing these gaps, we developed a new model, the “Cycle of Complexity.” This model illustrates relationships between acute shocks and health events, health care access and utilization, patient workload and capacity, and patient preferences in the context of interpersonal, organizational and community factors. It can be used to understand within-person changes over time as well as between-person differences at a single point in time. The Cycle of Complexity can be applied to evaluation of multiple outcomes and acknowledges possible health decline as well as resilience.

Conclusion: The Cycle of Complexity model may inform studies on the etiology of and changes in patient complexity, the relationship between complexity and patient outcomes, and intervention development to improve modifiable elements of care for complex patients. The model may be helpful in applying big data resources to address research questions relevant to medically complex populations.

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