Lindroth, Heidi

The early detection of the acute deterioration of escalating illness severity is crucial for effective patient management and can significantly impact patient outcomes. Ambient sensing technology, such as computer vision, may provide real-time information that could impact early recognition and response. This study aimed to develop a computer vision model to quantify the number and type (clinician vs. visitor) of people in an intensive care unit (ICU) room, study the trajectory of their movement, and preliminarily explore its relationship with delirium as a marker of illness severity. To quantify the number of people present, we implemented a counting-by-detection supervised strategy using images from ICU rooms. This was accomplished through developing three methods: single-frame, multi-frame, and tracking-to-count. We then explored how the type of person and distribution in the room corresponded to the presence of delirium. Our designed pipeline was tested with a different set of detection models. We report model performance statistics and preliminary insights into the relationship between the number and type of persons in the ICU room and delirium. We evaluated our method and compared it with other approaches, including density estimation, counting by detection, regression methods, and their adaptability to ICU environments.

Computer vision (CV), a type of artificial intelligence (AI) that uses digital videos or a sequence of images to recognize content, has been used extensively across industries in recent years. However, in the healthcare industry, its applications are limited by factors like privacy, safety, and ethical concerns. Despite this, CV has the potential to improve patient monitoring, and system efficiencies, while reducing workload. In contrast to previous reviews, we focus on the end-user applications of CV. First, we briefly review and categorize CV applications in other industries (job enhancement, surveillance and monitoring, automation, and augmented reality). We then review the developments of CV in the hospital setting, outpatient, and community settings. The recent advances in monitoring delirium, pain and sedation, patient deterioration, mechanical ventilation, mobility, patient safety, surgical applications, quantification of workload in the hospital, and monitoring for patient events outside the hospital are highlighted. To identify opportunities for future applications, we also completed journey mapping at different system levels. Lastly, we discuss the privacy, safety, and ethical considerations associated with CV and outline processes in algorithm development and testing that limit CV expansion in healthcare. This comprehensive review highlights CV applications and ideas for its expanded use in healthcare.

Abstract Effective mentorship relationships increase mentee academic success and satisfaction. However, existing mentorship models are limited by miscommunication, undefined roles, and mismatched goals. The agile mentorship process aims to address these limitations by leveraging insights from agile science and the existing evidence on effective mentorship models to support effective mentoring relationships in healthcare environments. To illustrate the agile mentorship process and the growth of a mentored clinician–scientist (H.L., first author), we describe the model and share qualitative findings generated from the independent analysis of 18 months of mentee reflections. In two iterative cycles, reflections (n = 56) were analyzed using exploratory content and relational analysis. Coauthors C.S. and B.T. employed inductive and deductive coding approaches to explore the data using an ontological lens. We discuss and share quotes representing the identified four main themes. Identification of shortcomings, adaptive perspective, managing relationships, and personal growth. In addition, personal growth had three subthemes: Awareness, continual reflection, and toolkit development. In summary, the reflections of one mentee within the agile mentorship process illustrated the growth process which occurred within an effective mentorship relationship. The agile mentorship process is a scalable and sustainable framework that is adaptable to various career development processes. Further evaluation is needed to understand the longitudinal impact of the model on mentee performance and satisfaction.

Objective: The application of computer models in continuous patient activity monitoring using video cameras is complicated by the capture of images of varying qualities due to poor lighting conditions and lower image resolutions. Insufficient literature has assessed the effects of image resolution, color depth, noise level, and low light on the inference of eye opening and closing and body landmarks from digital images. Method: This study systematically assessed the effects of varying image resolutions (from 100 × 100 pixels to 20 × 20 pixels at an interval of 10 pixels), lighting conditions (from 42 to 2 lux with an interval of 2 lux), color-depths (from 16.7 M colors to 8 M, 1 M, 512 K, 216 K, 64 K, 8 K, 1 K, 729, 512, 343, 216, 125, 64, 27, and 8 colors), and noise levels on the accuracy and model performance in eye dimension estimation and body keypoint localization using the Dlib library and OpenPose with images from the Closed Eyes in the Wild and the COCO datasets, as well as photographs of the face captured at different light intensities. Results: The model accuracy and rate of model failure remained acceptable at an image resolution of 60 × 60 pixels, a color depth of 343 colors, a light intensity of 14 lux, and a Gaussian noise level of 4% (i.e., 4% of pixels replaced by Gaussian noise). Conclusions: The Dlib and OpenPose models failed to detect eye dimensions and body keypoints only at low image resolutions, lighting conditions, and color depths. Clinical Impact: Our established baseline threshold values will be useful for future work in the application of computer vision in continuous patient monitoring.

Background Management of delirium in intensive care units is challenging because effective therapies are lacking. Music is a promising nonpharmacological intervention. Objectives To determine the feasibility and acceptability of personalized music (PM), slow-tempo music (STM), and attention control (AC) in patients receiving mechanical ventilation in an intensive care unit, and to estimate the effect of music on delirium. Methods A randomized controlled trial was performed in an academic medical-surgical intensive care unit. After particular inclusion and exclusion criteria were applied, patients were randomized to groups listening to PM, relaxing STM, or an audiobook (AC group). Sessions lasted 1 hour and were given twice daily for up to 7 days. Patients wore noise-canceling headphones and used mp3 players to listen to their music/audiobook. Delirium and delirium severity were assessed twice daily by using the Confusion Assessment Method for the Intensive Care Unit (CAM-ICU) and the CAM-ICU-7, respectively. Results Of the 1589 patients screened, 117 (7.4%) were eligible. Of those, 52 (44.4%) were randomized, with a recruitment rate of 5 patients per month. Adherence was higher in the groups listening to music (80% in the PM and STM groups vs 30% in the AC group; P = .01), and 80% of patients surveyed rated the music as enjoyable. The median number (interquartile range) of delirium/coma-free days by day 7 was 2 (1-6) for PM, 3 (1-6) for STM, and 2 (0-3) for AC (P = .32). Median delirium severity was 5.5 (1-7) for PM, 3.5 (0-7) for STM, and 4 (1-6.5) for AC (P = .78). Conclusions Music delivery is acceptable to patients and is feasible in intensive care units. Further research testing use of this promising intervention to reduce delirium is warranted.