This study focuses on feedback from domain experts to assess usability and acceptance of the E-Consent electronic consent platform. Quantitative and qualitative data were captured throughout the usability inspection, which was structured around a cognitive walkthrough with heuristics evaluation. Additional surveys measured biobanking knowledge and attitudes and familiarity with informed consent. A semi-structured qualitative interview captured open-ended feedback. 23 researchers of various ages and job titles were included for analysis. The System Usability Scale (SUS) provided a standardized reference for usability and satisfaction, and the mean result of 86.7 corresponds with an 'above average' usability rating in the >90th percentile. Overall, participants believe that electronic consenting using this platform will be faster than previous workflows while enhancing patient understanding, and human rapport is still a key component of the consent process. Expert review has provided valuable insight and actionable information that will be used to further enhance this maturing platform.

This project aims to assess usability and acceptance of a customized Epic-based flowsheet designed to streamline the complex workflows associated with care of patients with implanted Deep Brain Stimulators (DBS). DBS patient care workflows are markedly fragmented, requiring providers to switch between multiple disparate systems. This is the first attempt to systematically evaluate usability of a unified solution built as a flowsheet in Epic. Iterative development processes were applied, collecting formal feedback throughout. Evaluation consisted of cognitive walkthroughs, heuristic analysis, and 'think-aloud' technique. Participants completed 3 tasks and multiple questionnaires with Likert-like questions and long-form written feedback. Results demonstrate that the strengths of the flowsheet are its consistency, mapping, and affordance. System Usability Scale scores place this first version of the flowsheet above the 70th percentile with an 'above average' usability rating. Most importantly, a copious amount of actionable feedback was captured to inform the next iteration of this build.

This study seeks to assess usability and acceptance of E-Consent on mobile devices such as tablet computers for collecting universal biobank consents. Usability inspection occurred via cognitive walkthroughs and heuristics evaluations, supplemented by surveys to capture health literacy, patient engagement, and other metrics. 17 patients of varied ages, backgrounds, and occupations participated in the study. The System Usability Scale (SUS) provided a standardized reference for usability and satisfaction, and the mean result of 84.4 placed this mobile iteration in the top 10th percentile. A semi-structured qualitative interview provided copious actionable feedback, which will inform the next iteration of this project. Overall, this implementation of the E-Consent framework on mobile devices was considered easy-to-use, satisfying, and engaging, allowing users to progress through the consent materials at their own pace. The platform has once again demonstrated high usability and high levels of user acceptance, this time in a novel setting.

This study represents a post-implementation qualitative inquiry for a maturing flowsheet design that aims to replace multiple disparate devices used for data entry. The flowsheet has already experienced multiple iterative development cycles based on formal feedback from formative and summative usability studies. This next phase focused on a semi-structured qualitative interview to provide new feedback that will be used to further refine the product. Results of the 9-item interview were both actionable and provocative, revealing multiple avenues of improvement and a new usability map that can inform future studies and design plans.

The goal of this paper was to apply unsupervised machine learning techniques towards the discovery of latent clusters in COVID-19 patients. Over 6,000 adult patients tested positive for the SARS-CoV-2 infection at the Mount Sinai Health System in New York, USA met the inclusion criteria for analysis. Patients' diagnoses were mapped onto chronicity and one of the 18 body systems, and the optimal number of clusters was determined using K-means algorithm and the elbow method. 4 clusters were identified; the most frequently associated comorbidities involved infectious, respiratory, cardiovascular, endocrine, and genitourinary disorders, as well as socioeconomic factors that influence health status and contact with health services. These results offer a strong direction for future research and more granular analysis.

This study is designed to measure the concordance of step counts recorded by Fitbit activity trackers when the devices are placed on multiple locations of the body and while subjects climb stairs at fast, slow, and medium paces. Nine participants wore 5 Fitbit trackers concurrently while performing the stair-climbing activity. The level of concordance was characterized by variability metrics derived from five step counts obtained for each study participant at each climbing pace. Results of one-way ANOVA analysis revealed statistically significant difference between mean variance, standard deviation (SD) and range of step count measurements depending on location of tracker and pace of movement. Stair climbing at a 'medium pace' produced the least variance (25.9±24.5) with smallest SD (4.0±2.3), whereas the 'slow pace' trial produced the greatest variance (1770.9±3307.5) and SD (27.6±27.1). Discordance between Fitbit step count measurements obtained at different activity levels may affect overall accuracy of step count reporting.

We developed a multipurpose scalable electronic informed consent platform (E-Consent) which is reusable for any informed consent in a multitude of settings. The platform allows research staff to easily upload multimedia information about a research protocol with an approved informed consent into the system, which delivers this content interactively for prospective study candidates in a user-friendly way. Consistent with user-centered design, E-Consent underwent usability inspection via cognitive walkthroughs accompanied by surveys that captured task complexity on a 5-point Likert-type scale. The System Usability Scale (SUS) provided a standardized reference for usability and satisfaction. Overall, the E-Consent framework was considered by participants to be easy-to-use, satisfying, and timely, while delivering complex information such as that on a consent form. E-Consent ranked in the top 10th percentile for usability as measured by SUS. This extensible framework successfully delivered complex information and recorded user consents, all in an easy-to-understand and highly usable fashion.

The goal of this project was to assess usability and acceptance of a web-based tool after iterative development based on cognitive walkthroughs. The website is a "Research Roadmap", modeled after the NYC Subway map, and designed to help the user navigate the complex structure of research at a large multi-institution organization. A mixed process of evaluation and design was applied; after an initial survey phase, the website was revised, then another cycle of feedback was implemented. Surveys consisted of standardized questions with answers arranged as Likert-type scales and additional written responses. The first phase of survey feedback shaped overall design of the tool. The second phase measured task performance (time-to-completion), perceived ease-of-use, and satisfaction. These ongoing cycles of cognitive walkthroughs provided actionable data that led to redesign of the tool, an improved interface, improved user satisfaction, and 'above average' usability (top 10th percentile) as measured by the System Usability Scale.