Enhancing Hospital Efficiency and Patient Care: Real-Time Tracking and Data-Driven Dispatch in Patient Transport.

Inefficient patient transport in hospitals often leads to delays, overworked staff, and suboptimal resource utilization, ultimately impacting patient care. Existing dispatch management algorithms are often evaluated in simulation environments, raising concerns about their real-world applicability. This study presents a real-world experiment that bridges the gap between theoretical dispatch algorithms and real-world implementation. It applies process capability analysis at Taichung Veterans General Hospital in Taichung, Taiwan, and utilizes IoT for real-time tracking of staff and medical devices to address challenges associated with manual dispatch processes. Experimental data collected from the hospital underwent statistical evaluation between January 2021 and December 2021. The results of our experiment, which compared the use of traditional dispatch methods with the Beacon dispatch method, found that traditional dispatch had an overtime delay of 41.0%; in comparison, the Beacon dispatch method had an overtime delay of 26.5%. These findings demonstrate the transformative potential of this solution for not only hospital operations but also for improving service quality across the healthcare industry in the context of smart hospitals.

A Study on Decision-Making for Improving Service Efficiency in Hospitals.

The provision of efficient healthcare services is essential, driven by the increasing demand for healthcare resources and the need to optimize hospital operations. In this context, the motivation to innovate and improve services while addressing urgent concerns is critical. Hospitals face challenges in managing internal dispatch services efficiently. Outsourcing such services can alleviate the burden on hospital staff, reduce costs, and introduce professional expertise. However, the pressing motivation lies in enhancing service quality, minimizing costs, and exploring innovative approaches. With the rising demand for healthcare services, there is an immediate need to streamline hospital operations. Delays in internal transportation services can have far-reaching implications for patient care, necessitating a prompt and effective solution. Drawing upon dispatch data from a healthcare center in Taiwan, this study constructed a decision-making model to optimize the allocation of hospital service resources. Employing simulation techniques, we closely examine how hospital services are currently organized and how they work. In our research, we utilized dispatch data gathered from a healthcare center in Taichung, Taiwan, spanning from January 2020 to December 2020. Our findings underscore the potential of an intelligent dispatch strategy combined with deployment restricted to the nearest available workers. Our study demonstrates that for cases requiring urgent attention, delay rates that previously ranged from 5% to 34% can be notably reduced to a much-improved 3% to 18%. However, it is important to recognize that the realm of worker dispatch remains subject to a multifaceted array of influencing factors. It becomes evident that a comprehensive dispatching mechanism must be established as part of a broader drive to enhance the efficiency of hospital service operations.

Big data ordination towards intensive care event count cases using fast computing GLLVMS.

BACKGROUND: In heart data mining and machine learning, dimension reduction is needed to remove multicollinearity. Meanwhile, it has been proven to improve the interpretation of the parameter model. In addition, dimension reduction can also increase the time of computing in high dimensional data. METHODS: In this paper, we perform high dimensional ordination towards event counts in intensive care hospital for Emergency Department (ED 1), First Intensive Care Unit (ICU1), Second Intensive Care Unit (ICU2), Respiratory Care Intensive Care Unit (RICU), Surgical Intensive Care Unit (SICU), Subacute Respiratory Care Unit (RCC), Trauma and Neurosurgery Intensive Care Unit (TNCU), Neonatal Intensive Care Unit (NICU) which use the Generalized Linear Latent Variable Models (GLLVM's). RESULTS: During the analysis, we measure the performance and calculate the time computing of GLLVM by employing variational approximation and Laplace approximation, and compare the different distributions, including Negative Binomial, Poisson, Gaussian, ZIP, and Tweedie, respectively. GLLVMs (Generalized Linear Latent Variable Models), an extended version of GLMs (Generalized Linear Models) with latent variables, have fast computing time. The major challenge in latent variable modelling is that the function [Formula: see text] is not trivial to solve since the marginal likelihood involves integration over the latent variable u. CONCLUSIONS: In a nutshell, GLLVMs lead as the best performance reaching the variance of 98% comparing other methods. We get the best model negative binomial and Variational approximation, which provides the best accuracy by accuracy value of AIC, AICc, and BIC. In a nutshell, our best model is GLLVM-VA Negative Binomial with AIC 7144.07 and GLLVM-LA Negative Binomial with AIC 6955.922.