Lost in translation? Information exchange during breast cancer care and implications for technology design.

Abstract

1558 Background: Almost 4 million women in the US have had breast cancer, with 300,000 women diagnosed each year. Treatment decision-making following breast cancer diagnosis can be challenging as there are often several clinically appropriate treatment options, each with differing impacts on patient short- and long-term quality of life. Further, patients must manage this information during a time of high emotional burden, which can hinder decision-making. Not surprisingly, studies have found that less than 50% of patients believe their treatment plan aligns with their preferences. Numerous decision aids have been developed to support patient decision-making. However, sustained use of decision aids is limited due to poor integration in clinical workflow. We need to better understand the complex workflows and needs of decision aid users - patients and breast cancer clinicians. In this study, we investigate the information exchange between patients and clinicians when making breast cancer treatment decisions and identify design requirements for patient-centered decision support tools. Methods: This study is part of a larger research project to design a COMputerized PAtient-centered Collaborative Technology (COMPACT) to support personalized breast cancer decision-making. We observed clinicians at one academic medical center’s breast center from February-August 2022. Participating clinicians were followed throughout their clinical shift. Using a tablet computer and smart pencil, we recorded all aspects of clinician workflow. Written notes were transcribed and uploaded into Dedoose. Two human factors researchers inductively coded each observation in a consensus-based process. Results: We observed 79 hours of clinical care and 119 patient encounters across 20 observations of various clinical roles. Overall, we found that patients with breast cancer must manage an enormous amount of highly complex information that includes technical medical terms, mathematical concepts (e.g., risk), and uncertainty. We identified over 20 distinct decisions that patients face during their cancer journey, which were interdependent and evolved over time. We identified numerous patient-specific factors that influenced treatment decision-making, such as the patient’s work and family circumstances. For instance, one patient was organizing a conference and needed to plan her surgery around this event. Patients were frequently asked to remember and relay information across members of the care team, indicating inadequate system support of information transfer within the large cancer team. Based on these findings, we identified design requirements for interventions to support care coordination and patient-centered decision-making. Conclusions: These findings can inform the design of interventions to support breast cancer decision-making and improve patient-centered cancer care.