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The Internet of Medical Things (IoMT) has been applied to provide health care facilities for elders and parents. Remote health care is essential for providing scarce resources and facilities to coronavirus patients. Ongoing IoMT communication is susceptible to potential security attacks. In this research, an artificial intelligence-driven security model of the IoMT is also proposed to simulate and analyses the results. Under the proposed plan, only authorized users will be able to access private and sensitive patient information, and unauthorized users will be unable to access a secure healthcare network. The various phases for implementing artificial intelligence (AI) techniques in the IoMT system have been discussed. AI-driven IoMT is implemented using decision trees, logistic regression, support vector machines (SVM), and k-nearest neighbours (KNN) techniques. The KNN learning models are recommended for IoMT applications due to their low consumption time with high accuracy and effective prediction. © 2023 IEEE.
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During the COVID-19 pandemic the healthcare facilities all over world collapsed due to shortage of essential biomedical devices. ECG devices are one of those crucial instruments required for tracing electrical activities of heart. Due to the high cost of gold standard ECG devices used in the medical industries, the availability of on-demand ECG devices was not accessible to everyone. Thus, the need of portable, low cost, on-demand ECG device was needful at the earliest. In this paper we propose a novel, versatile, 3-lead, IoT enabled, LM324/LM741 operational amplifiers in instrumentation amplifier configuration Electrocardiogram machine that is aimed towards providing accurate information about the electrical activity of our heart in real time. In this attempt, we have come up with an analogue circuit design consisting of multiple operational amplifier IC based fundamental circuit blocks. The prototype is designed in such a way that the output of ECG can be visualised worldwide using IoT. © 2023 IEEE.
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Whether at home, work, school, or traveling abroad, digital healthcare is in demand. Rapidly changing delivery models are shaping the new healthcare landscape far beyond a COVID-19 world. The papers in this minitrack present innovative digital health applications that can be administered or used in a digital health setting outside the walls of traditional healthcare facilities. These papers present apps for parolee reentry into the community, training for audiology screening, and infectious disease risk assessments. Another paper addresses optimization of at-home triage, while the final manuscript focuses on empowering patients in health consultations using an online platform. Taken together, these papers highlight the growing importance of enabling new delivery models for ubiquitous and comprehensive healthcare. © 2022 IEEE Computer Society. All rights reserved.
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During a large-scale epidemic, a local healthcare system can be overwhelmed by a large number of infected and non-infected patients. To serve the infected and non-infected patients well with limited medical resources, effective emergency medical service planning should be conducted before the epidemic. In this study, we propose a two-stage stochastic programming model, which integrally deploys various types of emergency healthcare facilities before an epidemic and serves infected and non-infected patients dynamically at the deployed healthcare facilities during the epidemic. With the service equity of infected patients and various practical requirements of emergency medical services being explicitly considered, our model minimizes a weighted sum of the expected operation cost and the equity cost. We develop two comparison models and conduct a case study on Chengdu, a Chinese city influenced by the COVID-19 epidemic, to show the effectiveness and benefits of our proposed model. Sensitivity analyses are conducted to generate managerial insights and suggestions. Our study not only extends the existing emergency supply planning models but also can facilitate better practices of emergency medical service planning for large-scale epidemics. © Operational Research Society 2023.
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A painful lesson got from pandemic COVID-19 is that preventive healthcare service is of utmost importance to governments since it can make massive savings on healthcare expenditure and promote the welfare of the society. Recognizing the importance of preventive healthcare, this research aims to present a methodology for designing a network of preventive healthcare facilities in order to prevent diseases early. The problem is formulated as a bilevel non-linear integer programming model. The upper level is a facility location and capacity planning problem under a limited budget, while the lower level is a user choice problem that determines the allocation of clients to facilities. A genetic algorithm (GA) is developed to solve the upper level problem and a method of successive averages (MSA) is adopted to solve the lower level problem. The model and algorithm is applied to analyze an illustrative case in the Sioux Falls transport network and a number of interesting results and managerial insights are provided. It shows that solutions to medium-scale instances can be obtained in a reasonable time and the marginal benefit of investment is decreasing. © 2023, Journal of Industrial and Management Optimization. All Rights Reserved.
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CoViD-19 pandemic caused a severe changing of healthcare facilities activities. Specifically, one of the most affected areas are the Department of Emergency Surgery that have been reorganized to face the emergency giving priority to urgent procedures at cost of those which could be deferred. This study evaluates the impact of the pandemic on the departments of two different Italian Hospitals: "San Giovanni di Dio and Ruggi d'Aragona” University Hospital in Salerno and the AORN "A. Cardarelli” of Napoli. Two different analyses (statistical and machine learning) have been provided for investigating patients in 2019, as an example of the normal activity before the pandemic, and those recorded in 2020, in which the pandemic reached its peak. The evaluation performed showed an increase in the urgent hospitalization and Diagnostic Related Group while transfers to Social Care Residences (RSA) decreased in both the Hospitals, even if the steepness of these changes are consistent with the starting values. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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Healthcare sectors such as hospitals, nursing homes, medical offices, and hospice homes encountered several obstacles due to the outbreak of Covid-19. Wearing a mask, social distancing and sanitization are some of the most effective methods that have been proven to be essential to minimize the virus spread. Lately, medical executives have been appointed to monitor the virus spread and encourage the individuals to follow cautious instructions that have been provided to them. To solve the aforementioned challenges, this research study proposes an autonomous medical assistance robot. The proposed autonomous robot is completely service-based, which helps to monitor whether or not people are wearing a mask while entering any health care facility and sanitizes the people after sending a warning to wear a mask by using the image processing and computer vision technique. The robot not only monitors but also promotes social distancing by giving precautionary warnings to the people in healthcare facilities. The robot can assist the health care officials carrying the necessities of the patent while following them for maintaining a touchless environment. With thorough simulative testing and experiments, results have been finally validated. © 2022 IEEE.
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Due to sudden evolution and spread of COVID-19, the entire community in the globe is at risk. The covid has affected the health and economy and caused loss of life. In India, due to social economic factors, several thousands of people are infected, and India is seen as one of the top countries seriously impacted by the pandemic. Despite of having a modern medical instruments, drugs, and technical technology, it is very difficult to contain the spread of virus and save people from risk. Healthcare system and government personnel need to get an insight of covid outbreaks in the near future to decide on stepping up the healthcare facilities, to take necessary actions and to implement prevention policies to minimize the spread. In order to help the government, this study aims to build model a forecast COVID-19 model to foretell growth curve by predicting number of confirmed cases. Three variant models based on long short-term memory (LSTM) were built on the Indian COVID-19 dataset and are compared using the root mean squared error (RMSE) and mean absolute percentage error (MAPE). The findings have revealed that the proposed stacked LSTM model outperforms the other proposed LSTM variants and is suitable for forecasting COVID-19 progress in India. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.
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The Wells-Riley model invokes human physiological and engineering parameters to successfully treat airborne transmission of infectious diseases. Applications of this model would have high potentiality on evaluating policy actions and interventions intended to improve public safety efforts on preventing the spread of COVID-19 in an enclosed space. Here, we constructed the interaction relationships among basic reproduction number (R0) - exposure time - indoor population number by using the Wells-Riley model to provide a robust means to assist in planning containment efforts. We quantified SARS-CoV-2 changes in a case study of two Wuhan (Fangcang and Renmin) hospitals. We conducted similar approach to develop control measures in various hospital functional units by taking all accountable factors. We showed that inhalation rates of individuals proved crucial for influencing the transmissibility of SARS-CoV-2, followed by air supply rate and exposure time. We suggest a minimum air change per hour (ACH) of 7 h-1 would be at least appropriate with current room volume requirements in healthcare buildings when indoor population number is < 10 and exposure time is < 1 h with one infector and low activity levels being considered. However, higher ACH (> 16 h-1) with optimal arranged-exposure time/people and high-efficiency air filters would be suggested if more infectors or higher activity levels are presented. Our models lay out a practical metric for evaluating the efficacy of control measures on COVID-19 infection in built environments. Our case studies further indicate that the Wells-Riley model provides a predictive and mechanistic basis for empirical COVID-19 impact reduction planning and gives a framework to treat highly transmissible but mechanically heterogeneous airborne SARS-CoV-2.
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COVID-19 , Humans , SARS-CoV-2 , HospitalsABSTRACT
Corona Virus (COVID-19) is a virus that is endemic almost all over the world, including Indonesia. COVID-19 was first confirmed by the World Health Organization (WHO) on December 31, 2019, in Wuhan City, Hubei Province, China, and then rapidly expanded outside of China. To suppress the Covid-19 case, medical volunteers are needed as the main actors in efforts to handle Covid-19 patients. This makes health care facilities also need to focus on the principles of health worker safety, not only focus on the principles of patient safety. This also makes health care facilities also need to focus on the principles of health worker safety, not only focus on the principles of patient safety. The use of hazmat clothes is one of the efforts to protect health workers when in contact with Covid-19 patients. Hazmat clothes are technically referred to as "encapsulated waterproof protective clothing” which is PPE that must be used for officers from the risk of contracting the Covid-19 virus through airborne droplets and contact with patients and patient body fluids. Although hazmat clothing is an important PPE for health workers to stay protected, the use of hazmat clothing for a long time often makes medical personnel feel uncomfortable when providing services. Based on the problems above, the researchers conducted a study on the heat pipe - thermoelectric hazmat suit cooling vest. This technology can absorb more heat than other methods by simply applying the principle of capillarity to the wicks on the pipe walls. schematic of testing a cooling vest on a hazmat suit. The loading on the thermoelectric is given through the DC - Power supply. The temperature data read by the sensor will be detected by the computer system using the NI 9123 and C-DAQ 9174 modules. The test results can be viewed using the NI LabView 2017 software. The temperature used in this experiment is the result of tests carried out for 30 min. Based on the tests that have been carried out, the heat pipe-based thermoelectric hazmat suit cooling vest has been able to reach the lowest thermoelectric temperature of 24,42 ∘C, which is distributed through heat pipes to body parts. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Healthcare spatial accessibility requires a better understanding and evaluation, especially during pan-demic outbreaks like the recent COVID-19 pandemic. The main goal of this study is to measure and assess community-level spatial accessibility in Amman city to various COVID-19 related healthcare resources that could provide any urgent medical care for suspected or confirmed COVID-19 cases. To address this aim, the Enhanced 2-step floating catchment area (E2SFCA) method combined with several geospatial techniques were performed. The main E2SFCA results show the differences in the capacities and spatial accessibility of health facilities within Amman city, as well as how the variations are captured at different regions. The resulted spatial accessibility scores were presented in interactive Geo-spatial maps, ana-lyzed, and compared for several health resources in public, private, and educational hospitals. The current research findings stated that although there are enough healthcare facilities to service almost the entire city, inappropriate health facility distribution, rather than a lack of resources, has resulted in coverage gaps in some areas. The center zones had been fully serviced, or perhaps over-served, by a large number of facilities. The other zones, on the contrary, were partially served or were even underserved by a certain number of resources.(c) 2022 National Authority of Remote Sensing & Space Science. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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The COVID-19 pandemic has disrupted daily lives globally, causing social isolation that impacted the mental health and well-being of the population, particularly the students. With the shortage of accessible healthcare facilities and resources, the community is turning to technology-based mental healthcare interventions such as telemental health systems, online support groups, self-service web and mobile applications, and chatbots. In this study, we assessed the extent in which the daily interaction with the chatbot Wysa can influence the well-being of students during the COVID-19 pandemic. Students evaluated the usability and effectiveness of Wysa's clinical interventions which include the talk therapy, gratitude journal, self-care practices and mindfulness exercise throughout the duration of the week-long experiment. They provided their perception on the quality of the chatbot's response, affect and human-likeness, and shared attributes that would motivate self-disclosure and openness to communicate with the chatbot. Our findings can shed insights on the effectiveness of mental health apps as a coping mechanism in a time of social isolation and provide suggestions on how such technologies can be improved in order to maximize well-being benefits as well as user satisfaction. © 2022 ACM.
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This study aims to identify the impact of adherence to Non-Pharmaceutical Interventions (NPI) such as facemask type Cotton Fabric Mask and social distancing on the rate of COVID-19 exposure in waiting areas inside an emergency department. As a methodology, a Multi-Agent Simulation approach was used to model and capture the flow of patients inside the emergency department in this research. Each agent represents a physical entity, including its attributes defined. These agents will collaborate based on the defined rules to achieve the best mimic of the system being modeled. This methodology aims to quantitatively evaluate the performance of preventive measures based on the agent's proximity and exposure time. The number of infections was affected by the application of the facemask. Infections were reduced when facemask adherence and social distancing were applied. The study showed that the application of social distancing has a similar effect to a 20% adherence of agents wearing a facemask. The model also reveals that more agents adhere to the facemask, and the time required to get an agent to the state exposed increases. Waiting areas are a potentially significant contributor to transmission. © 2022 IEEE.
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OBJECTIVE: To describe maternal perception of the quality of maternal and newborn care (QMNC) in facilities in Norway during the first year of COVID-19 pandemic. METHODS: Women who gave birth in a Norwegian facility from March 1, 2020, to October 28, 2021, filled out a structured online questionnaire based on 40 WHO standards-based quality measures. Quantile regression analysis was performed to assess changes in QMNC index over time. RESULTS: Among 3326 women included, 3085 experienced labor. Of those, 1799 (58.3%) reported that their partner could not be present as much as needed, 918 (29.8%) noted inadequate staff numbers, 183 (43.6%) lacked a consent request for instrumental vaginal birth (IVB), 1067 (34.6%) reported inadequate communication from staff, 78 (18.6%) reported fundal pressure during IVB, 670 (21.7%) reported that they were not treated with dignity, and 249 (8.1%) reported experiencing abuse. The QMNC index increased gradually over time (3.68 points per month, 95% CI, 2.83-4.53 for the median), with the domains of COVID-19 reorganizational changes and experience of care displaying the greatest increases, while provision of care was stable over time. CONCLUSION: Although several measures showed high QMNC in Norway during the first year of the COVID-19 pandemic, and a gradual improvement over time, several findings suggest that gaps in QMNC exist. These gaps should be addressed and monitored.
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COVID-19 , Maternal Health Services , Pregnancy , Infant, Newborn , Female , Humans , Pandemics , COVID-19/epidemiology , Delivery, Obstetric , Parturition , Quality of Health CareABSTRACT
Background: Molnupiravir (MOL) is an oral antiviral medication that has recently been treated for COVID-19. Objectively: We perform a prospective and observational study to elucidate the efficacy and safety of MOL in healthcare patients with COVID-19. Materials and Methods: A observational, non-randomized study of patients diagnosed with COVID-19 in 46 healthcare facilities and treated with MOL started within 5 days after the onset of signs or symptoms. We recorded data for all patients, including demographic data, clinical features, and symptoms. Treatment response was classified into cure, stable, hospitalization and death. Multivariate analysis was performed with stepwise logistic regression for hospitalization and death risk factors. Results: In total, 856 patients were diagnosed as having COVID-19 and treated with MOL during the study period. Of those, 496 patients (57.9%) were cured, 256 patients (29.9%) in stable condition, 104 patients (12.2%) hospitalized, and 22 patients (2.6%) died, respectively. There was significant effectiveness (87.8%) in COVID-19 patients using MOL. Multivariate analysis was performed to confirm the risk factors for hospitalization and death and included elder age (>80 years old) (odds ratio (OR) 2.2, 95% confidence interval (CI): 1.1-6.9), old cerebrovascular accident (CVA) (OR=4.1, 95% CI: 1.3-9.9), the presence of diabetes mellitus (DM) (OR=2.6, 95% CI: 1.2-9.1) and chronic respiratory diseases (OR=2.4, 95% (CI): 1.3-8.1). Limitations: This is an observational study, neither randomized study nor control group study. Conclusion: Initial treatment with MOL has the treatment benefits and is well tolerated for patients with COVID-19 in healthcare facilities. Older age, old CVA, DM, and chronic respiratory diseases were independent risk factors for hospitalization and mortality. The results demonstrate there are important clinical benefits of MOL beyond the reduction in hospitalization or death for these patients with more comorbidities in Taiwan.
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COVID-19 , Diabetes Mellitus , Humans , Aged , Aged, 80 and over , COVID-19/diagnosis , SARS-CoV-2 , Prospective Studies , Comorbidity , Diabetes Mellitus/epidemiology , Hospitalization , Delivery of Health Care , Retrospective StudiesABSTRACT
OBJECTIVES: Healthcare facility-onset Clostridioides difficile infection (HO-CDI) is a major nosocomial infection associated with high mortality and healthcare costs. We aimed to determine if HO-CDI incidence decreased due to the COVID-19 pandemic. We hypothesized that the pandemic decreased HO-CDI as healthcare workers became more diligent in handwashing and sanitization. METHODS: In this retrospective cohort study, adult patients with sepsis hospitalized in general wards from January 2018 to February 2021 were identified using a nationwide Japanese administrative database. Patients were divided into two groups according to the hospitalization date (before and after the first declaration of a state of emergency). The primary outcome was a change in the level of the HO-CDI monthly incidence ratio (per 10000 patient-days). RESULTS: Of the 49,156 eligible hospitalizations for sepsis, 41,870 were before and 7,283 were after the first state of emergency declaration. Interrupted time-series (ITS) analysis showed no significant difference in the HO-CDI incidence ratio after Japan's first state of emergency declaration (level change -1.0, 95% confidence interval (CI) -8.6 to 6.6, p = 0.8, slope change 0.06, 95% CI -0.17 to 0.3, p = 0.6). The overall HO-CDI incidence ratio was 3.86/10000 patient-days (interquartile range 2.97-4.53); higher incidence existed in subgroups with older adults or a lower Barthel index at admission. CONCLUSIONS: No significant change in HO-CDI incidence was observed in patients with sepsis hospitalized in general wards before and after Japan's first state of emergency declaration. Our study revealed that HO-CDI in general wards in Japan had been consistently decreasing since before the COVID-19 pandemic.
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COVID-19 , Clostridium Infections , Cross Infection , Sepsis , Humans , Clostridioides difficile , Clostridium Infections/diagnosis , Clostridium Infections/epidemiology , COVID-19/epidemiology , COVID-19 Testing , Cross Infection/epidemiology , Delivery of Health Care , East Asian People , Incidence , Interrupted Time Series Analysis , Pandemics , Retrospective Studies , Sepsis/epidemiologyABSTRACT
The COVID-19 pandemic has affected the whole world, and we can classify it as a significant disaster in modern history. However, not only a pandemic is the unique disaster that has hit the earth. Due to the increased attention to the pandemic, spent not much attention on the other disasters. There have been forest fires in Australia, floods in Indonesia, volcano eruption in the Philippines, and others in 2020. Several disasters are causing cascading effects that may affect different sectors. Fires or floods are just one of them. The cascade effect can endanger several elements of critical infrastructure, such as the energy sector and healthcare. In healthcare facilities, patients are dependent on electricity supplies. It is therefore essential that the proper functioning of healthcare facilities is maintained. The aim of the paper is to highlight the growing trend in the number of disasters around the world and draw attention to the preparedness of healthcare facilities to solve these disasters. Based on the analysis, we can take the information that there is a big difference in the preparedness of the healthcare facilities to solve a power outage. © 2021 The Authors.
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This research summarizes the strategies employed in reconfiguring healthcare facilities in OECD member countries to care for patients with COVID-19. The findings were organized by highlighting each country’s hospital reconfiguration strategies, strategies targeting medical devices for treating COVID-19 patients, and medical devices classified by patient severity. Specific hospitals or new units were designated to treat patients in 79% of member countries, 47% reported having reoriented hospital areas for patient care, and 57% reported having increased capacity to treat patients in intensive care units. Telematic consultations (57%) and postponement of non-urgent interventions (76%) were reported strategies for reducing contagion. The 38 countries reported increased personal protective equipment, hospital beds, ventilators, and oxygenation supplies. Significantly few countries reported an increase in ECMO machines, negative pressure systems and rooms, and the availability of imaging equipment. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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Since the SARS-CoV-2 transmission can occur by contact with surfaces contaminated with respiratory secretions and other fluids like faeces or saliva, the superficial disinfection has been one of the main problems during the COVID-19 pandemic. Cross-contagion has been observed between health personnel and cleaning staff from hospitals attending COVID-19 patients. The problem was solved through the implementation of a contact-less disinfection system that reduces the COVID-19 exposition of sanitation workers from healthcare facilities. This work presents the results observed from the implementation of an Ultraviolet-C (UV-C) disinfection method controlled and monitored using an Internet of Things (IoT) scheme. Also, implementation experiences obtained from the application of the proposed solution at the Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán (INCMNSZ) are discussed in this article. The main contribution of this work relies in the fulfillment of a disinfection proceeding that helps reducing the cross-contagion between the cleaning staff of hospitals attending the COVID-19 pandemic. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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Covid-19 has shaken the entire globe. In the fight against this pandemic, the doctors and frontline workers are the real heroes who are facing an unseen enemy. The Masks, PPE Kits, and other protective wearables are used by patients, doctors, and other front-line workers for only one time. This leads to increased costs and supply issues, and also leads to huge environmental pollution. That is the problem that the product 'Safe Box' Addresses. The proposed system sterilizes Masks, PPE Kits, and other wearables making them reusable. 'Safe Box' plays a vital role in aiding hospitals, laboratories, clinics, and other healthcare facilities where non-reusable kits like masks, PPE, and other wearables are widely used. © 2022 IEEE.