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A synthesis of theoretical models to guide decision support interventions for surrogate decision-making at adult end-of-life

James Dionne-Odom, Marie Bakitas

Abstract


Background: Surrogate decision-makers (SDMs) take part in 1.5 million end-of-life (EOL) decisions per year. Most surrogates find the role burdensome, often do make decisions concordant with patients’ wishes and suffer negative psychological after-effects months to years subsequently. 

Objective: To review the literature and synthesize theoretical models of surrogate decision-making at adult EOL to identify an explanatory foundation for decision support interventions. 

Design: Literature published up to December 2011 was reviewed using PubMed, MEDLINE, CINAHL and EMBASE using the search terms “surrogate,” “proxy,” “end-of-life,” “life support withdrawal/withholding” and “decision-making”. 

Study selection: Selection criteria included: (a) studies or integrative reports of the empirical and theoretical research relevant to EOL surrogate decision-making; (b) description of a model of how SDMs make decisions for decisionally-incapacitated adults at EOL and (c) diagrammatic depictions of SDM models specifying key concepts and theoretical relationships. 

Results: Eight theoretical models met the selection criteria.

Data synthesis: Five key insights were that: 1) more consistency is needed between diagrammatic maps of surrogate decision-making and model descriptions; 2) models focused on description of concepts with a lesser focus on relational linkages and propositions; 3) there is a need for greater integration of ethical concepts and their relationships in conceptual maps; 4) there has been little emphasis on how theoretical frameworks might guide intervention development and 5) minimal attempts have been made to situate surrogate decision-making within a grander conceptual framework of decision-making. 

Conclusions: Several theories of surrogate decision-making at adult EOL have been proposed, but further theory development is needed for these models to serve as the foundation for designing decision support interventions.

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References


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DOI: http://dx.doi.org/10.5750/ejpch.v1i1.661

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