Extended abstracts on Human factors in computing systems. CHI Conference最新文献

We describe the challenges of deploying a digital checklist for medical emergencies during an in-the-wild design and evaluation study. The in-the-wild approach allowed for many design iterations to meet the requirements of a safety-critical setting, while also providing lessons for designing in the wild. We faced two major challenges: working with research coordinators as study mediators and adapting training strategies to busy user schedules. We discuss these challenges and approaches to addressing them.

In this ongoing study, we aim to redesign an existing dynamic digital checklist application (app) for trauma resuscitations in a regional trauma center. The design followed an iterative, user-centered approach. Trauma team physician leaders and research coordinators at the center participated in a survey and usability study to provide feedback for improving the user interface. Proper optimization of the user experience is necessary for future adoption of the digital checklist. This study lays the groundwork for in situ use and evaluation of the checklist by trauma team members.

Deep learning is the driving force behind many recent technologies; however, deep neural networks are often viewed as "black-boxes" due to their internal complexity that is hard to understand. Little research focuses on helping people explore and understand the relationship between a user's data and the learned representations in deep learning models. We present our ongoing work, ShapeShop, an interactive system for visualizing and understanding what semantics a neural network model has learned. Built using standard web technologies, ShapeShop allows users to experiment with and compare deep learning models to help explore the robustness of image classifiers.

We describe a pilot study of designing and evaluating a digital checklist for medical emergencies based on participation of medical-expert researchers who used the checklist during actual trauma resuscitations. The participation of the researchers revealed challenges and insights for designing in the wild, as well as next steps for using our emerging technology in real scenarios.

Clinical Decision Support Systems (CDSS) are tools that assist healthcare personnel in the decision-making process for patient care. Although CDSSs have been successfully deployed in the clinical setting to assist physicians, few CDSS have been targeted at professional nurses, the largest group of health providers. We present our experience in designing and testing a CDSS interface embedded within a nurse care planning and documentation tool. We developed four prototypes based on different CDSS feature designs, and tested them in simulated end-of-life patient handoff sessions with a group of 40 nurse clinicians. We show how our prototypes directed nurses towards an optimal care decision that was rarely performed in unassisted practice. We also discuss the effect of CDSS layout and interface navigation in a nurse's acceptance of suggested actions. These findings provide insights into effective nursing CDSS design that are generalizable to care scenarios different than end-of-life.

Robots have potential to provide assistance to healthcare providers in daily caregiving tasks. The healthcare providers' acceptance of assistive robots will mediate the success or failure of implementation of robotic systems in care settings. It is essential to understand why and how providers would accept implementation of a robot in their daily work routines. We identified caregiving tasks with which healthcare providers would or would not accept assistance from a personal robot (Willow Garage's PR2). We also explored preferences for human or robot assistance. The healthcare providers we interviewed were quite open to the idea of receiving robot assistance for certain tasks.

We present the design, development and an initial study changes and adaptations related to navigation that take place in the brain, by incorporating an Audio-Based Environments Simulator (AbES) within a neuroimaging environment. This virtual environment enables a blind user to navigate through a virtual representation of a real space in order to train his/her orientation and mobility skills. Our initial results suggest that this kind of virtual environment could be highly efficient as a testing, training and rehabilitation platform for learning and navigation.

We present the design, development and initial cognitive evaluation of an Audio-based Environment Simulator (AbES). This software allows a blind user to navigate through a virtual representation of a real space for the purposes of training orientation and mobility skills. Our findings indicate that users feel satisfied and self-confident when interacting with the audio-based interface, and the embedded sounds allow them to correctly orient themselves and navigate within the virtual world. Furthermore, users are able to transfer spatial information acquired through virtual interactions into real world navigation and problem solving tasks.