, Volume 22, Issue 3 Supplement, pp 382-390,
Open Access This content is freely available online to anyone, anywhere at any time.
Date: 16 Nov 2007

Patient Complexity: More Than Comorbidity. The Vector Model of Complexity

Abstract

BACKGROUND

The conceptualization of patient complexity is just beginning in clinical medicine.

OBJECTIVES

This study aims (1) to propose a conceptual approach to complex patients; (2) to demonstrate how this approach promotes achieving congruence between patient and provider, a critical step in the development of maximally effective treatment plans; and (3) to examine availability of evidence to guide trade-off decisions and assess healthcare quality for complex patients.

METHODS/RESULTS

The Vector Model of Complexity portrays interactions between biological, socioeconomic, cultural, environmental and behavioral forces as health determinants. These forces are not easily discerned but exert profound influences on processes and outcomes of care for chronic medical conditions. Achieving congruence between patient, physician, and healthcare system is essential for effective, patient-centered care; requires assessment of all axes of the Vector Model; and, frequently, requires trade-off decisions to develop a tailored treatment plan. Most evidence-based guidelines rarely provide guidance for trade-off decisions. Quality measures often exclude complex patients and are not designed explicitly to assess their overall healthcare.

CONCLUSIONS/RECOMMENDATIONS

We urgently need to expand the evidence base to inform the care of complex patients of all kinds, especially for the clinical trade-off decisions that are central to tailoring care. We offer long- and short-term strategies to begin to incorporate complexity into quality measurement and performance profiling, guided by the Vector Model. Interdisciplinary research should lay the foundation for a deeper understanding of the multiple sources of patient complexity and their interactions, and how provision of healthcare should be harmonized with complexity to optimize health.