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.

Identification and Genome Characterization of a Novel Nege-like Virus Isolated from Aphids (Aphis gossypii) in Yunnan Province.

Negeviruses are insect-specific enveloped RNA viruses that exhibit a wide geographic distribution. A novel nege-like virus, tentatively named Aphis gossypii nege-like virus (AGNLV, GenBank: OR880429.1), was isolated from aphids (Aphis gossypii) in Lijiang City, Yunnan, China. AGNLV has a genome sequence of 9258 nt (excluding the polyA tail) encoding three open reading frames (ORFs). ORF1 (7149 nt) encodes a viral methyltransferase, a viral RNA helicase, and an RNA-dependent RNA polymerase. ORF2 (1422 nt) encodes a DiSB-ORF2_chro domain and ORF3 encodes an SP24 domain. The genome sequence of AGNLV shares the highest nucleotide identity of 60.0% and 59.5% with Wuhan house centipede virus 1 (WHCV1) and Astegopteryx formosana nege-like virus (AFNLV), respectively. Phylogenetic analysis based on the RNA-dependent RNA polymerase shows that AGNLV is clustered with other negeviruses and nege-like viruses discovered in aphids, forming a distinct "unclassified clade". Interestingly, AGNLV only encodes three ORFs, whereas AFNLV and WHCV1 have four ORFs. Structure and transmembrane domain predictions show the presence of eight alpha helices and five transmembrane helices in the AGNLV ORF3. Translational enhancement of the AGNLV 5' UTR was similar to that of the 5' UTR of plant viruses. Our findings provide evidence of the diversity and structure of nege-like viruses and are the first record of such a virus from a member of the genus Aphis.

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.

New strategy for surface functionalization of periodic mesoporous silica based on meso-HSiO1.5.

Organic functionalization of periodic mesoporous silicas (PMSs) offers a way to improve their excellent properties and wide applications owing to their structural superiority. In this study, a new strategy for organic functionalization of PMSs is demonstrated by hydrosilylation of the recently discovered "impossible" periodic mesoporous hydridosilica, meso-HSiO1.5. This method overcomes the disadvantages of present pathways for organic functionalization of PMSs with organosilica. Moreover, compared to the traditional functionalization on the surface of porous silicon by hydrosilylation, the template-synthesized meso-HSiO1.5 is more flexible to access functional-groups-loaded PMSs with adjustable microstructures. The new method and materials will have wider applications based on both the structure and surface superiorities.

Systemic Inflammatory Response Index, a Potential Inflammatory Biomarker in Disease Severity of Myasthenia Gravis: A Pilot Retrospective Study.

Purpose: Myasthenia gravis (MG) is a chronic autoimmune disease caused by neuromuscular junction (NMJ) dysfunction. Our current understanding of MG's inflammatory component remains poor. The systemic inflammatory response index (SIRI) presents a promising yet unexplored biomarker for assessing MG severity. This study aimed to investigate the potential relationship between SIRI and MG disease severity. Patients and Methods: We conducted a retrospective analysis of clinical data from 171 MG patients admitted between January 2016 and June 2021. Patients with incomplete data, other autoimmune diseases, or comorbidities were excluded. Disease severity was evaluated using the Myasthenia Gravis Foundation of America (MGFA) classification and Myasthenia Gravis Activities of Daily Living (MG-ADL) on admission. The association between SIRI and disease severity was assessed through logistic regression analysis, along with receiver operating characteristic (ROC) curve and decision curve analysis (DCA) comparisons with established inflammation indicators. Results: After exclusion, 143 patients were analyzed in our study. SIRI levels significantly differed between patients with higher and lower disease severity (p < 0.001). Univariate logistic regression showed that SIRI had a significant effect on high disease severity (OR = 1.376, 95% CI 1.138-1.664, p = 0.001). This association remained significant even after adjusting for age, sex, disease duration, history of MG medication and thymoma (OR = 1.308, 95% CI 1.072-1.597, p = 0.008). Additionally, a positive correlation between SIRI and MG-ADL was observed (r = 0.232, p = 0.008). Significant interactions were observed between SIRI and immunosuppressor (p interaction = 0.001) and intravenous immunoglobulin (p interaction = 0.005). DCA demonstrated the superior net clinical benefit of SIRI compared to other markers when the threshold probability was around 0.2. Conclusion: Our findings indicate a strong independent association between SIRI and disease severity in MG, suggesting SIRI's potential as a valuable biomarker for MG with superior clinical benefit to currently utilized markers.

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.

A Role for Systemic Inflammation in Stroke-Associated Infection and the Long-Term Prognosis of Acute Ischemic Stroke: A Mediation Analysis.

Background and Purpose: The dynamic systemic inflammation level and stroke-associated infection (SAI) are related to the prognosis of acute ischemic stroke (AIS). We aimed to explore whether the systemic inflammatory response index (SIRI), systemic immune inflammation index (SII), and their dynamic changes possess predictability for SAI and long-term prognosis. Methods: A total of 1804 AIS patients without intravenous thrombolysis in two hospitals were included. We explored the relationship between SIRI, SII, and their dynamic changes and outcomes by constructing clusters. The mediating effects of SAI between prognosis and systemic inflammation were further evaluated. Results: Each SD increase in the concentration of SIRI exhibited a significant correlation with the risk of poor functional outcome, mortality, and functional dependency. Through K-means clustering analysis, patients with dramatically elevated or decreased systemic inflammation levels of SIRI (OR: 2.293, 95% CI: 1.279-4.109) and SII (OR: 3.165, 95% CI: 1.627-6.156) within 7 days had a higher risk of functional outcome. Through mediation analysis, SAI mediated the association between systemic inflammation and poor prognosis (SIRI: 33.73%, SII: 16.01%). Conclusion: Dramatically changing dynamics of SIRI and SII were significantly associated with a higher risk of poor prognosis in AIS patients. SAI mediated the association between systemic inflammation and prognosis at 1 year.

Use of the Smart Lean Method to Conduct High-Quality Integrated Perioperative Management Prior to Hospitalization.

BACKGROUND: competition in the healthcare market is becoming increasingly intense. Health technology continues to evolve, so hospitals and clinics need to strengthen hospital management techniques and also adopt a more patient-centered approach in order to provide high-quality healthcare services, including a more simplified process and shorter waiting times for examinations. The Lean and Six Sigma methodologies and smart technology were introduced and implemented into the integrated perioperative management (PERIO) processes for the purpose of decreasing pre-admission management waiting time, as well as increasing the completion rate and quality of pre-admission management for surgical patients in a 1576-bed medical center in central Taiwan. METHODS: in order to improve hospital admission procedures for surgical patients by shortening process waiting times, simplifying admission processes, emphasizing a patient-centered approach, and providing the most efficient service process, the present study applied the DMAIC architecture of the Lean Six Sigma. This approach allowed the patients to save time on the hospital admission process. The current workflow used value flow mapping to identify wasted time caused by unnecessary walking and waiting during the hospital admission process. Therefore, we improved the process cycle for each patient by simultaneously selecting and controlling the process for the purpose of saving time. RESULTS: the experimental results show that the percentage of Process Cycle Efficiency (PCE) increased from 35.42% to 42.47%, Value Added was reduced from 34 to 31 min, and Non-Value Added was reduced from 62 to 42 min. The satisfaction score of the 97 pre-implementation patients was 4.29 compared with 4.40 among the 328 post-implementation patients (p < 0.05). The LOS (Length of Stay) of 2660 pre-implementation patients was 2.49~3.31 days and for 304 after-implementation patients it was 1.16~1.57 days. CONCLUSIONS: by integrating different units and establishing standard perioperative management (PERIO) procedures, together with the support of the information systems, the time spent by patients on hospital admission procedures was shortened. These changes also improved the comprehensiveness of the preoperative preparations and the surgical safety of patients, thereby facilitating the provisions necessary for high-quality healthcare services. This in turn reduced the average length of hospital stays and increased the turnover of patients, benefiting the overall operations of the hospital.