Publications by authors named "O Perry"
The Incident Commander (IC) decision-making process has previously been modeled primarily by qualitative evaluation methods, which has made it difficult to generalize an objective model. In this study, we took a quantitative approach to elucidate a decision-making model based on the "dual-process" model that consists of instantaneous decisions ("System 1") and considered decisions ("System 2") to gain new insights regarding the IC decision-making process. High-fidelity simulation data from eight mass-casualty incidents (MCIs) were analyzed in two categories.
View Article and Find Full Text PDFIntroduction: Artificial intelligence and computer vision are revolutionizing the way we perceive video analysis in minimally invasive surgery. This emerging technology has increasingly been leveraged successfully for video segmentation, documentation, education, and formative assessment. New, sophisticated platforms allow pre-determined segments chosen by surgeons to be automatically presented without the need to review entire videos.
View Article and Find Full Text PDFBackground: Immigrant Latinas are at higher risk for postpartum depression (PPD) than the general perinatal population, yet face numerous barriers to accessing mental health services. The goal of this study was to pilot an enhanced virtual group delivery of a PPD prevention program, Mothers and Babies (MB), among immigrant Latinas engaged in early childhood programming.
Methods: Forty-nine Spanish-speaking mothers participated in one of four MB virtual groups, facilitated by trained bilingual staff at affiliated early learning centers.
Article Synopsis
- The research aims to create a new model called Dynamic Communication Quantification (DCQ) to measure information management in emergency medical services (EMS) during mass-casualty incidents (MCIs).
- The DCQ model was tested in MCI simulations from 2014 to 2019 and successfully identified specific factors causing information gaps, which traditional methods did not reveal.
- By quantifying information management effectively, the DCQ model enhances training simulations for MCI team leaders, ultimately aiming to improve their performance in real-life emergency situations.
Background And Purpose: Accurate prehospital diagnosis of stroke by emergency medical services (EMS) can increase treatments rates, mitigate disability, and reduce stroke deaths. We aimed to develop a model that utilizes natural language processing of EMS reports and machine learning to improve prehospital stroke identification.
Methods: We conducted a retrospective study of patients transported by the Chicago EMS to 17 regional primary and comprehensive stroke centers.