Search for: All records

Background:Technology has the potential to prevent patient falls in healthcare settings and to reduce work-related injuries among healthcare providers. However, the usefulness and acceptability of each technology requires careful evaluation. Framed by the Technology Acceptance Model (TAM) and using the Adaptive Robotic Nursing Assistant (ARNA) to assist with patient ambulation, the present study examined the perceived usefulness of robots in patients’ fall prevention with implications for preventing associated work-related injuries among healthcare providers. Methods:Employing an experimental design, subjects were undergraduate nursing students ( N = 38) and one external subject (not a nursing student) who played the role of the patient. Procedures included subjects ambulating a simulated patient in three ways: (a) following the practice of a nurse assisting a patient to walk with the patient wearing a gait belt; (b) an ARNA-assisted process with the gait belt attached to ARNA; (c) an ARNA-assisted process with a subject walking a patient wearing a harness that is attached to ARNA. Block randomization was used with the following experimental scenarios: Gait Belt (human with a gait belt), “ARNA + Gait Belt” (a robot with a gait belt), and “ARNA + Harness” (a robot with a harness). Descriptive statistics and a multiple regression model were used to analyze the data and compare the outcome described as the Perceived Usefulness (PU) of a robot for patient walking versus a human “nurse assistant” without a robot. The independent variables included the experimental conditions of “Gait Belt,” “ARNA + Gait Belt,” and “ARNA + Harness,” the subject’s age, race, and previous videogame playing experience. Findings:Results indicated that PU was significantly higher in the Gait Belt + ARNA and Harness + ARNA conditions than in the Gait Belt condition ( p-value <.01 for both variables). In examining potential influencing factors, the effects of race (White, African American, and Asian), age, and previous video-playing experience were not statistically significant ( p-value >.05). Discussion:Results demonstrated that using robot technology to assist in walking patients was perceived by subjects as more useful in preventing falls than the gait belt. Patient fall prevention also has implications for preventing associated work-related injuries among healthcare providers. Implications:Understanding the effects of a subject’s perceptions can guide further development of assistive robots in patient care. Robotic engineers and interdisciplinary teams can design robots to accommodate worker characteristics and individual differences to improve worker safety and reduce work injuries. 

Problem: Given the United States’ urgency for systemic-level improvements to care, advancing systems-based practice (SBP) competency among future physicians is crucial. However, SBP education is inadequate, lacks a unifying framework and faculty confidence in its teaching, and is taught late in the medical education journey. Approach: The Oklahoma State University Center for Health Systems Innovation (CHSI) created an SBP program relying on Lean Health Care for a framework and targeted medical students before their second year began. Lean curricula were developed (lecture and simulation) and a partnership with a hospital was secured for work-based practice. The CHSI developed a skills assessment tool for preliminary evaluation of the program. In June 2022, 9 undergraduate medical students responded to a Lean Health Care Internship (LHCI) presentation. Outcomes: Student SBP skills increased after training and again after work-based practice. All 9 students reported that their conceptualization of problems in health care changed “extraordinarily,” and they were “extraordinarily” confident in their ability to approach another health care problem by applying the Lean method. The LHCI fostered an awareness of physicians as interdependent systems citizens, a key goal of SBP competency. After the internship concluded, the Lean team recommendations generated a resident-led quality assurance performance improvement initiative for bed throughput. Next Steps: The LHCI was effective in engaging students and building SBP skills among undergraduate medical education students. The levels of student enthusiasm and skill acquisition exceeded the Lean trainers’ expectations. The researchers will continue to measure LHCI’s effect on students’ rotation experiences to better evaluate the long-term benefit of introducing SBP concepts earlier in medical education. The program’s success has spurred enthusiasm for continued collaboration with hospital and residency programs. Program administrators are exploring how to broaden access. 

This paper presents an attention-based, deep learning framework that converts robot camera frames with dynamic content into static frames to more easily apply simultaneous localization and mapping (SLAM) algorithms. The vast majority of SLAM methods have difficulty in the presence of dynamic objects appearing in the environment and occluding the area being captured by the camera. Despite past attempts to deal with dynamic objects, challenges remain to reconstruct large, occluded areas with complex backgrounds. Our proposed Dynamic-GAN framework employs a generative adversarial network to remove dynamic objects from a scene and inpaint a static image free of dynamic objects. The Dynamic-GAN framework utilizes spatial-temporal transformers, and a novel spatial-temporal loss function. The evaluation of Dynamic-GAN was comprehensively conducted both quantitatively and qualitatively by testing it on benchmark datasets, and on a mobile robot in indoor navigation environments. As people appeared dynamically in close proximity to the robot, results showed that large, feature-rich occluded areas can be accurately reconstructed with our attention-based deep learning framework for dynamic object removal. Through experiments we demonstrate that our proposed algorithm has up to 25% better performance on average as compared to the standard benchmark algorithms.