How is routinely-collected health data used to investigate the impact of automatic cognitive processes on healthcare professional behaviour? A scoping review


KT interventions often focus on healthcare professionals’ deliberative, analytical thinking as a route to achieving practice change, e.g. educational meetings that provide instruction but leave attendees to integrate the lessons into practice. Such deliberative mechanisms are clearly an important element of practice change. However, human behaviour is guided by two cognitive systems: one involving deliberative processes, the other involving automatic processes (e.g. habits, cognitive heuristics). Automatic processes are under-considered in theoretical frameworks commonly used to explore barriers to change. These processes can be difficult to study, since they are not easily observed or self-reported on. Routinely-collected health data can provide a new window into these processes. Such data provide a means to explore processes that are common across all human decision makers and may have substantial impact on real-world clinical behaviour, through investigation across large samples. We are conducting a scoping review to map the ways in which routine data has been used to investigate the impact of automatic processes on clinical practice.


We ran electronic searches in MEDLINE, EMBASE, CINAHL, and PsycINFO. Records are being screened by two independent reviewers. Data will be extracted from included articles relating to clinical contexts (setting, provider and patient group, clinical behaviours investigated), methods used (data sources, steps in analysis procedure), automatic processes investigated, outcomes, and study results. We will compile a detailed descriptive and narrative summary of included studies, informed by qualitative content analysis techniques.


The searches identified 17,696 unique records. Title/abstract screening is nearing completion. Relevant studies identified so far investigated the availability heuristic (the tendency to assess the likelihood of an event based on the ease with which previous occurrences come to mind), e.g., using billing data to show that physicians were less likely to appropriately prescribe warfarin after another patient had a major adverse bleeding event associated with warfarin (a rare but salient and easily remembered event). Others have investigated the impact of decision fatigue (depletion of abilities to engage in reflective thinking over time, which leads to reliance on automatic processes), e.g., using electronic health record data to show that the likelihood of family physicians prescribing opioids increased as the workday progressed, consistent with cognitive fatigue limiting capacity to make effortful decisions around reducing opioid therapy. Finally, studies have focused on the representativeness heuristic (the tendency to classify an event into a category based on how typical it is of the category), e.g., a study of triage decisions found that despite transfer to a trauma centre being appropriate for all patients studied, patients judged as not typically representative were less likely to be transferred.


This scoping review will demonstrate ways in which routine data has been used to test hypotheses related to automatic decision-making processes by healthcare professionals, which will support the generation of new hypotheses and help guide the prioritization of next steps for future research. Further work may ultimately support the development of novel or adapted KT interventions to target these processes as routes to improving quality of care.


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