Abstract
BackgroundClinical experts’ cognitive mechanisms for managing complexity have implications for the design of future innovative healthcare systems. The purpose of the study is to examine the constituents of decision complexity and explore the cognitive strategies clinicians use to control and adapt to their information environment.MethodsWe used Cognitive Task Analysis (CTA) methods to interview 10 Infectious Disease (ID) experts at the University of Utah and Salt Lake City Veterans Administration Medical Center. Participants were asked to recall a complex, critical and vivid antibiotic-prescribing incident using the Critical Decision Method (CDM), a type of Cognitive Task Analysis (CTA). Using the four iterations of the Critical Decision Method, questions were posed to fully explore the incident, focusing in depth on the clinical components underlying the complexity. Probes were included to assess cognitive and decision strategies used by participants.ResultsThe following three themes emerged as the constituents of decision complexity experienced by the Infectious Diseases experts: 1) the overall clinical picture does not match the pattern, 2) a lack of comprehension of the situation and 3) dealing with social and emotional pressures such as fear and anxiety. All these factors contribute to decision complexity. These factors almost always occurred together, creating unexpected events and uncertainty in clinical reasoning. Five themes emerged in the analyses of how experts deal with the complexity. Expert clinicians frequently used 1) watchful waiting instead of over- prescribing antibiotics, engaged in 2) theory of mind to project and simulate other practitioners’ perspectives, reduced very complex cases into simple 3) heuristics, employed 4) anticipatory thinking to plan and re-plan events and consulted with peers to share knowledge, solicit opinions and 5) seek help on patient cases.ConclusionThe cognitive strategies to deal with decision complexity found in this study have important implications for design future decision support systems for the management of complex patients.Electronic supplementary materialThe online version of this article (doi:10.1186/s12911-015-0221-z) contains supplementary material, which is available to authorized users.
Highlights
Clinical experts’ cognitive mechanisms for managing complexity have implications for the design of future innovative healthcare systems
Current Clinical Decision Support Systems (CDSS) tools in Electronic Health Record (EHR) systems may not be suitable to assist with complex reasoning because they do not support both the automatic pattern matching of experts and high-level deliberative reasoning required in complex cases [8, 9]
Factors associated with decision-making complexity The following themes were identified from the factors contributing to decision-making complexity: 1) the overall clinical picture does not match the pattern, 2) a lack of comprehension of the situation and 3) dealing with social and emotional pressures
Summary
Clinical experts’ cognitive mechanisms for managing complexity have implications for the design of future innovative healthcare systems. Current CDSS tools in EHR systems may not be suitable to assist with complex reasoning because they do not support both the automatic pattern matching of experts and high-level deliberative reasoning required in complex cases [8, 9]. Better understanding of automatic pattern matching and analytical reasoning may guide the selection of optimum decision support tools that aid clinicians’ cognition. Clinical reasoning is a complex process that uses cognition, metacognition and discipline-specific knowledge to gather and analyze patient information, weigh alternatives and evaluate the best possible treatment regimen [11]. Rasmussen’s SRK (skill, rule, knowledge) model has gained popularity in the human factors field [13] According to this model, people use skill- and rule-based methods of decision-making when the task is less complex and previous experience can help. Helps to generate expectations for other cues not previously considered and guides the observation of changes in system variables [15]
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