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The Institute of Medicine proposed six aims for healthcare quality improvement. Nevertheless, trauma care quality research still focuses on one aim at a time. This research investigates how to incorporate all aims into trauma care quality assessments using data from the Michigan Trauma Quality Improvement Program. Through a literature review, we identified quantifiable metrics for most aims, except for equity and patient-centeredness. We proposed two approaches to build composite scores accounting for equity via an adjustment procedure based on observed disparities. The single- and multi-aim approaches were compared through correlation, concordance of trauma centre categorisations, and hypothetical incentives. The differences in the approaches stemmed mainly from the weights allocated to the different aims. Results indicated the potential value of multi-aim quality assessment and provided insights about implementation challenges and opportunities. The methods are applicable to the preferred metrics; nevertheless, further research is needed in measuring patient-centeredness.

Childhood and adolescent obesity is a serious health problem that is on the rise at the global level. Earlier, certain diseases such as Type 2 diabetes, high blood pressure, and heart disease affected only adults, but now they are being detected in young children as well. Several studies based on machine learning have been proposed to develop obesity prediction models or to determine key determinants of obesity for designing intervention tools. Despite having a rich and diverse set of literature on obesity prediction models, obesity rates are at an all-time high for both children and adolescents. There is a need of proper understanding and critical analysis of existing machine learning models in order to design effective strategies for curbing obesity at childhood and adolescent level. This paper surveys the growing body of recent literature on machine and deep learning models for obesity prediction by providing a coherent view (critical analysis) of the limitations of the existing systems. The taxonomy of the existing literature on obesity prediction into methods used, predicted outcome, factors used, type of datasets, and the associated purpose, is discussed for analysis of the state-of-the-art. This analysis revealed that a) prediction-focused models do not use variables from as many domains as predictor-focused models do, b) very few studies proposed gender-specific and race-specific obesity prediction models, c) lack of large-scale multimodal datasets and d) existing predictor-focused models obtain an accuracy range of [53.7%, 96%] with an optimum set of predictors. Further, computer vision-based methods for obesity prediction and interpretable techniques for understanding the outcome of the models are discussed as well. In addition, we have also identified novel research directions. The overall aim is to advance the state-of-the-art and improve the quality of discourse in this field.

This study presents robot-based rehabilitation and its assessment. Robotic devices have significantly been useful to help therapists do the training procedure consistently. However, as robotic devices interface with humans, quantifying the interaction and its intended outcomes is still a research challenge. In this study, human–robot interaction during rehabilitation is assessed based on measurable interaction forces and human physiological response data, and correlations are established to plan the intervention and effective limb trajectories within the intended rehabilitation and interaction forces. In this study, the Universal Robot 5 (UR5) is used to guide and support the arm of a subject over a predefined trajectory while recording muscle activities through surface electromyography (sEMG) signals using the Trigno wireless DELSYS devices. The interaction force is measured through the force sensor mounted on the robot end-effector. The force signals and the human physiological data are analyzed and classified to infer the related progress. Feature reduction and selection techniques are used to identify redundant inputs and outputs.

Near-miss reports are qualitative descriptions of events that could have harmed patients but did not due to a timely intervention or a convenient evolution of the circumstances. Near-miss reporting has increasingly become a relevant tool to support patient safety efforts since they provide some evidence of risk in the system before patients suffer adverse consequences. Near-misses are usually classified into pre-specified categories that correspond to sources of risk in the system or its processes. Their analysis often consists of tallying classified near-misses to determine risk priorities based on frequency within each pre-specified risk category. Our research aims to use different combinations of near-miss reports to find potential sources of risk. We propose an unsupervised bisecting k-prototypes algorithm for clustering coded near-miss reports to identify relationships between events that would not otherwise have been easily identified. Subsequent study of resulting clusters will lead to the identification of potentially dangerous, but unsuspected system interactions. We illustrate or methodology with preliminary results of its implementation at the University of South Florida Health clinics.

El desarrollo de la logística en la empresa durante las últimas décadas ha sido significativo debido a la posibilidad de lograr ventajas competitivas a través de ella. Hoy día existe la posibilidad de recuperar y aprovechar económicamente aquellos productos que dejan de satisfacer las necesidades del consumidor; esto genera un flujo de materiales y productos hacia el productor denominada Logística Inversa. El objetivo de este artículo es relacionar algunas formas de aplicación de Logística Inversa de algunas organizaciones con sus estrategias competitivas, lo que se realizará mediante una revisión bibliográfica sobre los aspectos relevantes del tema y de los resultados de una encuesta realizada en una muestra de empresas barranquilleras pertenecientes a cuatro sectores: periódicos, panaderías, químicos y distribuidores de drogas. Los resultados de dicha encuesta no arrojaron evidencia de uso significativo de la Logística Inversa como motor para la estrategia que les permite permanecer en el mercado, lo que sugiere que las condiciones del mercado local no alcanzan aún un nivel de competencia en el que surjan estas competencias como aspectos diferenciadores, o más probablemente, el desconocimiento de sus potencialidades por parte de los empresarios locales trae consigo este aparente abandono.

El desempeño de una línea de producción siempre será un foco importante de análisis. En este artículo se realiza un análisis progresivo, en su grado de complejidad, un análisis experimental con base en la simulación de una variedad de escenarios compuestos por la combinación de distintas variables en niveles determinados, con el fin de observar la interacción entre éstas e incluyendo el término de estocasticidad en tiempos de procesamiento para la definición de dichos escenarios, con el objetivo de indagar y observar la relación la variable del coeficiente de variación de los tiempos de procesamiento en la obtenida previamente./The performance of a production line will always be an important subject of analysis. In this document, a progressive analysis is experimentally made, based on the simulation of several scenarios That consist of a combination of some variables in different levels. In order to appreciate their interaction, stochasticity in processing times are taken into account for the definition of each scenario, with the objective of observing and inquiring into the relation of the variation coefficient of processing times with the one previously obtained in prior research.