Analese Patteuw
Research Mentor(s): Rama Musalia
Mentor Department: Department of Learning Health Sciences
Authors: Rama Mwenesi Musalia, Analese Patteuw, Morgan Polansky
Session: Session 3 (11:00am – 11:50am)
Presentation Type: Poster 120
Abstract
The RMM lab works to solve complex problems and decrease preventable adverse events in healthcare. In other words, this lab attempts to improve patient safety in the perioperative space by learning from adverse events that have previously occurred. In addition to this, the RMM lab works to transform how problems in healthcare are approached. It does so by implementing a multidisciplinary approach, including engineering, design, public health, and systems thinking. The lab also integrates the Learning Health System (LHS) model so that the work and findings of this lab are consistently being reviewed, refined and reimplemented into practice. The LHS involves iterative cycles of improvement and development through translation of knowledge to practice, practice to data, data to knowledge, and the process continues as such. Our project, part of a larger study, investigated the complexities of perioperative safety within a low-resource mission clinic in Kenya, focusing on the perspectives of frontline healthcare workers. As part of a broader assessment of patient safety infrastructure, this qualitative exploration aimed to illuminate the social dynamics and behavioral patterns that influence surgical practices in a challenging healthcare environment. We transcribed and thematically analyzed semi-structured and unstructured interviews conducted with frontline staff to identify key themes related to patient safety, including communication practices, teamwork dynamics, and the impact of resource limitations on care delivery. Findings revealed important insights into the barriers and facilitators of effective perioperative care, and by amplifying the voices of frontline staff, we hope to inform the design of future interventions and policy decisions that can improve patient outcomes in similar healthcare contexts. In the future, the RMM lab and its work can be used to create sustainable and scalable solutions so that healthcare systems around the world can learn from each individual patient and approach complex problems in a new, yet profound way.
