Patient Experience Diagnosis: Using Telemed Simulation to Assess Health Care Provider Verbal and Nonverbal Communication Issues to Prescribe Potential Interventions

Patient experience contributes to health outcomes, and a host of healthcare organization success factors, including profitability. Often applied and academic analysis of patient experience applies macro-level approaches to defining issues and suggesting improvements. Guided by the theoretical framework of provider-patient communication during telemedicine, this study used a simulation to measure impacts of provider behaviors that might be improved through communication training to positively impact outcomes on both sides of patient care. The study employed between-subject experimental design to investigate impacts of provider verbal and nonverbal communication on patient satisfaction during telemedicine consultations. Participants, randomly assigned to one of eight experimental conditions, watched a recorded telemedicine “consultation” with either a male or female provider that displayed either high- or low-immediacy nonverbal cues. Participants imagined being the patient and completed a survey regarding perceptions of provider communication and evaluation of the experience. Results suggest a healthcare provider’s verbal and nonverbal communication represents a significant predictor of patient satisfaction, even during telemedicine. The findings provide empirical evidence for Miller’s model and point to the importance and potential of improving providers’ verbal and nonverbal communication skills through communication training on specific interpersonal skills

A centrality measure for quantifying spread on weighted, directed networks

While many centrality measures for complex networks have been proposed, relatively few have been developed specifically for weighted, directed (WD) networks. Here we propose a centrality measure for spread (of information, pathogens, etc.) through WD networks based on the independent cascade model (ICM). While deriving exact results for the ICM requires Monte Carlo simulations, we show that our centrality measure (Viral Centrality) provides excellent approximation to ICM results for networks in which the weighted strength of cycles is not too large. We show this can be quantified with the leading eigenvalue of the weighted adjacency matrix, and we show that Viral Centrality outperforms other common centrality measures in both simulated and empirical WD networks.Comment: 3 figure

A Congressional Twitter network dataset quantifying pairwise probability of influence

We present a social network dataset based on interactions between members of the 117th United States Congress between Feb. 9, 2022, and June 9, 2022. The dataset takes the form of a directed, weighted network in which the edge weights are empirically obtained “probabilities of influence” between all pairs of Congresspeople. Twitter's application programming interface (API) V2 was used to determine the number of times each member of Congress retweeted, quote tweeted, replied to, or mentioned other Congressional members, and the probability of influence was found by normalizing the summed influence by the number of tweets issued by each Congressperson. This network may be of particular interest to the study of information diffusion within social networks