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.

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.