Expert decision-making in clinicians: an auto-analytic ethnographic study of operational decision-making in urgent care

Objective
To conceptualise the cognitive processes of early expert decision-making in urgent care.

Background
Expert clinicians in the UK frequently determine suitable urgent care patient pathways via telephone triage. This strategy is promoted by policymakers but how it is performed, and its effectiveness has not been evaluated. Evaluation of early senior decision-making requires knowledge of decision-processes, influences, and goals. Previous research has focused on diagnostic decision-making and rarely studied clinicians in the field.

Method
We triangulated analytic autoethnography of early expert decision-making with focused ethnography of experts and trainee doctors performing the task. The study took place in a medium-sized Acute Medical Unit which provided internal medical emergency care for a mixed urban and rural population in the UK. A grounded theoretical model of expert decision-making was created via Gioia Methodology. Decision types were categorised to identify differences in solutions as well as decision processes.

Results
The hallmarks of intuitive decision-making were found in most expert decisions. Experts made intuitive use of pattern-matching to extract key data from large volumes of information which triggered the spontaneous manifestation of solutions. Solutions were holistic and usually solitary. Upon manifestation solutions were consciously tested for viability with emotional affect playing a key role. Expert solutions were previously applied ones but were frequently entirely novel. Novel solution generation was not a feature of trainee decisions but moments of intuition were. Expert goals varied between optimal care for individual patients, system-wide efficiency, and equity of care. The decision environment had a large influence upon experts.

Conclusion
Expert clinicians employ intuitive decision-making supported by rational analysis in early urgent care decision-making. Expert solutions generated in this manner are pragmatic rather than optimal, context dependent, and seek to achieve goals which vary from moment-moment. Findings are crucial to inform research evaluating the effectiveness of early expert decision-making in urgent care as it is a high cost strategy. They also have implications for methodological approaches in future studies of expert clinical decision-making, developing artificial expert systems, and clinician training.