Objectives Our objectives were (1) to use in situ simulation to assess the clinical environment and identify latent safety threats (LSTs) related to the management of pediatric tracheostomy patients and (2) to analyze the effects of systems interventions and team factors on LSTs and simulation performance. Methods A multicenter, prospective study to assess LSTs related to pediatric tracheostomy care management was conducted in emergency departments (EDs) and intensive care units (ICUs). LSTs were identified through equipment checklists and in situ simulations via structured debriefs and blinded ratings of team performance. The research team and unit champions developed action plans with interventions to address each LST. Reassessment by equipment checklists and in situ simulations was repeated after 6 to 9 months. Results Forty-one LSTs were identified over 21 simulations, 24 in the preintervention group and 17 in the postintervention group. These included LSTs in access to equipment (ie, availability of suction catheters, lack of awareness of the location of tracheostomy tubes) and clinical knowledge gaps. Mean equipment checklist scores improved from 76% to 87%. Twenty-one unique teams (65 participants) participated in the simulations. The average simulation score was 6.19 out of 16 points. Discussion In situ simulation is feasible and effective as an assessment tool to identify latent safety threats and thus measure the system-level performance of a clinical care environment. Implications for Practice In situ simulation can be used to identify and reassess latent safety threats related to pediatric tracheostomy management and thereby support quality improvement and educational initiatives.

Given a database with numeric attributes, it is often of interest to rank the tuples according to linear scoring functions. For a scoring function and a subset of tuples, the regret of the subset is defined as the (relative) difference in scores between the top-1 tuple of the subset and the top-1 tuple of the entire database. Finding the regret-ratio minimizing set (RRMS), i.e., the subset of a required size k that minimizes the maximum regret-ratio across all possible ranking functions, has been a well-studied problem in recent years. This problem is known to be NP-complete and there are several approximation algorithms for it. Other NP-complete variants have also been investigated, e.g., finding the set of size k that minimizes the average regret ratio over all linear functions. Prior work have designed customized algorithms for different variants of the problem, and are unlikely to easily generalize to other variants. In this paper we take a different path towards tackling these problems. In contrast to the prior, we propose a unified algorithm for solving different problem variants. Unification is done by localizing the customization to the design of variant-specific subroutines or "oracles" that are called by our algorithm. Our unified algorithm takes inspiration from the seemingly unrelated problem of clustering from data mining, and the corresponding k-medoid algorithm. We make several innovative contributions in designing our algorithm, including various techniques such as linear programming, edge sampling in graphs, volume estimation of multi-dimensional convex polytopes, and several others. We provide rigorous theoretical analysis, as well as substantial experimental evaluations over real and synthetic data sets to demonstrate the practical feasibility of our approach.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic placed a tremendous strain on the healthcare system, which led to the deployment of new personnel into acute care settings, early graduation of medical students, and development of new treatment spaces. Education teams at the Montefiore Health System and New York Health and Hospitals/Jacobi Medical Center found simulation, both laboratory-based and in situ, critical to the training of medical staff and investigation of latent safety threats. Through our experience, we encountered unique infection control concerns based on in situ sessions, which prompted us to redesign our programs for the treatment of SARS-CoV-2. Using this experience, we outline our rationale for the use of in situ simulation for newly developed SARS-CoV-2 spaces along with recommendations on safety checks to consider before starting.