Vitali Sintchenko1, Enrico W Coiera. 1. Centre for Health Informatics, University of New South Wales, Sydney 2052, Australia. v.sintchenko@unsw.edu.au
Abstract
OBJECTIVE: To describe a model for analysing complex medical decision making tasks and for evaluating their suitability for automation. METHOD: Assessment of a decision task's complexity in terms of the number of elementary information processes (EIPs) and the potential for cognitive effort reduction through EIP minimisation using an automated decision aid. RESULTS: The model consists of five steps: (1) selection of the domain and relevant tasks; (2) evaluation of the knowledge complexity for tasks selected; (3) identification of cognitively demanding tasks; (4) assessment of unaided and aided effort requirements for this task accomplishment; and (5) selection of computational tools to achieve this complexity reduction. The model is applied to the task of antibiotic prescribing in critical care and the most complex components of the task identified. Decision aids to support these components can provide a significant reduction of cognitive effort suggesting this is a decision task worth automating. CONCLUSION: We view the role of decision support for complex decision to be one of task complexity reduction, and the model described allows for task automation without lowering decision quality and can assist decision support systems developers.
OBJECTIVE: To describe a model for analysing complex medical decision making tasks and for evaluating their suitability for automation. METHOD: Assessment of a decision task's complexity in terms of the number of elementary information processes (EIPs) and the potential for cognitive effort reduction through EIP minimisation using an automated decision aid. RESULTS: The model consists of five steps: (1) selection of the domain and relevant tasks; (2) evaluation of the knowledge complexity for tasks selected; (3) identification of cognitively demanding tasks; (4) assessment of unaided and aided effort requirements for this task accomplishment; and (5) selection of computational tools to achieve this complexity reduction. The model is applied to the task of antibiotic prescribing in critical care and the most complex components of the task identified. Decision aids to support these components can provide a significant reduction of cognitive effort suggesting this is a decision task worth automating. CONCLUSION: We view the role of decision support for complex decision to be one of task complexity reduction, and the model described allows for task automation without lowering decision quality and can assist decision support systems developers.