Exploring the Basis of Practice Variation Among Experts in a Medical Specialty: A Mixed-methods Study
2019-08-01T00:00:00Z (GMT) by
Background: How physicians navigate the uncertainty of diagnosis and management of medical conditions with limited evidence is largely unknown. One lens to look at uncertainty in medicine is through evaluating practice variation among physicians, or their differences in their clinical management among cases where a single, acceptable answer is not known in the general medical community. The purpose of this thesis study is to (1) determine the quantity and type of practice variation that exists among thrombosis experts, (2) determine the level of acceptability of practice variation among thrombosis experts, and (3) identify any guiding principles that specialists used when making decisions in areas of clinical uncertainty. Methods: Five challenging clinical vignettes were presented to thrombosis experts in Ottawa, Canada in semi-structured interviews. The same case vignettes and all management options chosen in the interviews were included in an anonymous survey to the same experts, to delineate the acceptability of other experts’ answers. Results: Ten (100%) thrombosis specialists completed interviews and eight completed the follow-up survey. Complete consensus where all specialists recommended a management option was reached in only three (3.4%) items. Despite wide practice variation, there was a high level of acceptability of the different management options among experts. Analysis of interview data identified how experts managed clinical uncertainty, which included: (1) knowing the latest evidence or relying on colleagues’ expertise, (2) using past experiences, (3) using clinical gestalt and common sense, (4) weighing the benefits against the risks, and (5) improving the patient experience. Conclusions: By better defining the nature and acceptability of practice variation in medical specialties, insights into how to improve the instruction and assessment of learners in situations when uncertainty exists may be possible.