Patient Flow Optimization in an Emergency Department Using SimPy-Based Simulation Modeling and Analysis: A Case Study

Long waiting times and patient congestion are common problems faced by emergency departments (EDs) worldwide. During pandemics like COVID-19, EDs worldwide start to be flooded with patients and hospitals find it very challenging to provide good treatment to the large number of patients visiting the EDs with their current allocation of resources. Hospitals are in need of a decision support system (DSS) which can predict the excess demand and suggest the appropriate quantity of resources to be allocated at each point of care. The present research focuses on an ED of a large public hospital in India and explores in finding a solution for the long patient waiting time problem experienced by the hospital. This study extends the application domain of SimPy-based simulation modeling with integrated metamodeling and optimization to optimally allocate the resources in the ED. This can be used as a novel DSS which is relatively faster and needs less human interaction by the hospital management compared to the existing methods. The proposed resource allocation by this model reduced the patient waiting time by 44% in the case hospital being studied. Hospitals may use the proposed methodology to appropriately allocate their resources in times of excess demand. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

FAST: FPGA-based Acceleration of Genomic Sequence Trimming

The sheer amount of genomic sequencing data generated daily that requires time-sensitive processing for downstream analysis calls for accelerating the bioinformatics pipelines. Previous studies mainly have attempted accelerating the alignment stage, leaving the other pipeline stages as performance bottlenecks. In this work, we propose the first FPGA-based framework dubbed FAST to accelerate the stages that deal with sequence trimming, in particular adapter and primer removal. FAST supports a comprehensive set of functionalities and is convenient to use by operating on standard genomics data formats. The proposed framework is fully configurable and supports variety of runtime settings. It surpasses the state-of-the-art widely-used adapter trimmer (fastp) by 4.7×-29.4× speed-up, with 10.1×-54.9 less energy, respectively. For clipping primers, which with current existing tool (iVar) accounts for ∼50% of SARS-CoV-2 analysis pipeline, FAST achieves up to 62× speed-up in trimming the virus sequences with a low FPGA resource utilization of 12%. © 2022 IEEE.

COVID-19 Crisis and Supply Side Bottlenecks in the EU. Shorter and Longer Term Prospects

The COVID-19 shock affected the global economy mainly through the col-lapse of demand, labour supply and industrial output, supply chains, commodity prices, international trade and capital flows. From the second half of 2020, various bottlenecks (logistical disruptions;shortages of raw materials and parts) in the EU economy have had a growing impact on growth. Following the recurrent pandemic shocks, the war shock has caused further severe supply-side disruptions from February 2022 onwards, in particular in raw materials markets (including energy sources and agricultural raw materials) and international logistics. Supply shortages in the EU have not only a one-off but also a ripple effect throughout the economy. Spill-over effects across Member States are of great importance. Together, the shortages of production inputs can have a significant negative impact on output and the recovery of the economy. At the same time, supply-side shocks can have a significant impact on the restructuring of supply chains, the deepening of green and digital transformation, and productivity growth. © 2022, Economic Laboratory for Transition Research. All rights reserved.

Real-Time Face Mask Detection and Analysis System

Due to COVID-19 situation, we need to wear face masks in public places. Reports say that wearing face mask at public places and at workspace reduces the transmission of virus as the SARS-CoV-2 spreads through atmosphere among people, at gathering in any environment. In this paper, a real-time face mask detection system is presented which will detect mask presence on the face using TensorFlow. We are using MobileNetV2 model to provide a greater accuracy in determining the mask presence. Accuracy obtained is 99%. Older systems do not provide a proper working system. A face mask detector has been designed with computer vision using Python, OpenCV, Keras, and TensorFlow. Video surveillance input can be given directly, and our primary purpose is to identify to check people are wearing masks on daily basis or not wearing masks and prepare a weekly and monthly report based on this observation and display the data on an interactive web application. System provides option to see the historical records, thereby reducing transmission. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Nanodiagnostics to Face SARS-CoV-2 and Future Pandemics: From an Idea to the Market and Beyond.

The COVID-19 pandemic made clear how our society requires quickly available tools to address emerging healthcare issues. Diagnostic assays and devices are used every day to screen for COVID-19 positive patients, with the aim to decide the appropriate treatment and containment measures. In this context, we would have expected to see the use of the most recent diagnostic technologies worldwide, including the advanced ones such as nano-biosensors capable to provide faster, more sensitive, cheaper, and high-throughput results than the standard polymerase chain reaction and lateral flow assays. Here we discuss why that has not been the case and why all the exciting diagnostic strategies published on a daily basis in peer-reviewed journals are not yet successful in reaching the market and being implemented in the clinical practice.

Optimization of medical waste recycling network considering disposal capacity bottlenecks under a novel coronavirus pneumonia outbreak.

The sudden outbreak and prolonged impact of the global novel coronavirus disease (COVID-19) epidemic has caused an increase in demand for medical products, such as masks and protective clothing, leading to an exponential increase in the generation of medical waste. As medical waste under the epidemic is highly infectious, it poses a great danger to human health. Therefore, with the proliferation of medical waste, it has become crucial to construct a reverse logistics recycling network that can handle medical waste quickly and efficiently. In this study, we construct a multi-period medical waste emergency reverse logistics network siting model with the objectives of minimum cost, minimum safety risk, and minimum time for the safe and quick disposal of medical waste. The model considers disposal capacity bottlenecks of existing facilities. Based on an empirical analysis using the COVID-19 epidemic in New York City, USA, as a case study, we find that the use of a suitable number of synergistic facilities and the establishment of temporary medical waste disposal centers are viable options for handling the dramatic increase in medical waste during the peak of the COVID-19 epidemic.

Paradoxes and mysteries in virus-infected supply chains: Hidden bottlenecks, changing consumer behaviors, and other non-usual suspects.

In the early onset of the COVID-19 pandemic in the U.S., consumers experienced surprising shortages of essential goods that appeared to be unrelated to the pandemic: toilet paper, yeast and flour, and meat cuts. The usual explanations-attributing these shortages to demand spikes-often failed to provide an adequate explanation or predicted only temporary shortages. But these shortages ended up being real supply-chain struggles for which the true causes revealed a deeper set of unusual causes. Our detailed analysis of these supply chains identifies overlooked failure factors and hidden causes. We conclude with the profound lessons learned from the pandemic crisis on supply chains and the implied challenges of building resilient supply chains for the future, which require rethinking the relevant systems we plan and optimize. The level of investment required for building firm-specific redundancy of assets and operational flexibility might be prohibitive for any one firm, or their financial stakeholders, to pursue and accept.

Pro-equity immunization and health systems strengthening strategies in select Gavi-supported countries.

BACKGROUND: Achieving universal immunization coverage and reaching every child with life-saving vaccines will require the implementation of pro-equity immunization strategies, especially in poorer countries. Gavi-supported countries continue to implement and report strategies that aim to address implementation challenges and improve equity. This paper summarizes the first mapping of these strategies from country reports. METHODS: Thirteen Gavi-supported countries were purposively selected with emphasis on Gavi's priority countries. Following a scoping of different documents submitted to Gavi by countries, 47 Gavi Joint Appraisals (JAs) for the period 2016-2019 from the 13 selected countries were included in the mapping. We used a consolidated framework synthesized from 16 different equity and health systems frameworks, which incorporated UNICEF's coverage and equity assessment approach - an adaptation of the Tanahashi model. Using search terms, the mapping was conducted using a combination of manual search and the MAXQDA qualitative analysis tool. Pro-equity strategies meeting the inclusion criteria were identified and compiled in an Excel database, and then populated on a tableau visualization dashboard. RESULTS: In total, 258 pro-equity strategies were implemented by the 13 sampled Gavi-supported countries between 2016 and 2019. The framework determinants of social norms, utilization, and management and coordination accounted for more than three-quarters of all pro-equity strategies implemented in these countries. The median number of strategies reported per country was 17. Afghanistan, Nigeria, and Uganda reported the highest number of strategies that we considered as pro-equity. CONCLUSION: Findings from this mapping can be useful in addressing equity gaps, reaching partially immunized, and 'zero-dose' vaccinated children, and valuable resource for countries planning to implement pro-equity strategies, especially as immunization stakeholders reimagine immunization delivery in light of COVID-19, and as Gavi finalizes its fifth organizational strategy. Future efforts should seek to identify pro-equity strategies being implemented across additional countries, and to assess the extent to which these strategies have improved immunization coverage and equity.

Healthcare Digitalization and Pay-For-Performance Incentives in Smart Hospital Project Financing.

This study aims to explore the impact of healthcare digitalization on smart hospital project financing (PF) fostered by pay-for-performance (P4P) incentives. Digital platforms are a technology-enabled business model that facilitates exchanges between interacting agents. They represent a bridging link among disconnected nodes, improving the scalable value of networks. Application to healthcare public-private partnerships (PPPs) is significant due to the consistency of digital platforms with health issues and the complexity of the stakeholder's interaction. In infrastructural PPPs, public and private players cooperate, usually following PF patterns. This relationship is complemented by digitized supply chains and is increasingly patient-centric. This paper reviews the literature, analyzes some supply chain bottlenecks, addresses solutions concerning the networking effects of platforms to improve PPP interactions, and investigates the cost-benefit analysis of digital health with an empirical case. Whereas diagnostic or infrastructural technology is an expensive investment with long-term payback, leapfrogging digital applications reduce contingent costs. "Digital" savings can be shared by key stakeholders with P4P schemes, incentivizing value co-creation patterns. Efficient sharing may apply network theory to a comprehensive PPP ecosystem where stakeholding nodes are digitally connected. This innovative approach improves stakeholder relationships, which are re-engineered around digital platforms that enhance patient-centered satisfaction and sustainability. Digital technologies are useful even for infectious disease surveillance, like that of the coronavirus pandemic, for supporting massive healthcare intervention, decongesting hospitals, and providing timely big data.