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Call for action:Increased Healthcare utilization with growing use of percutaneous cholecystectomy tube over initial cholecystectomy in cirrhotics

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Hepatobiliary & Pancreatic Diseases International 2022年第1期21卷 56-62页

作者： Srikanth Vedachalam Sajid Jalil Somashaker G Krishna Kyle Porter Na Li Sean G Kelly Lanla Conteh Khalid Mumtaz Department of Internal Medicine The Ohio State University Wexner Medical CenterColumbusOH 43210USA Division of Gastroenterology Hepatology and NutritionThe Ohio State University Wexner Medical CenterColumbusOH 43210USA Center for Biostatistics Department of Biomedical InformaticsThe Ohio State University Wexner Medical CenterColumbusOH 43210USA

Background:Acute calculous cholecystitis(ACC)is frequently seen in cirrhotics,with some being poor candidates for initial cholecystectomy.Instead,these patients may undergo percutaneous cholecystostomy tube(PCT)placement.We studied the Healthcare utilization and predictors of cholecystectomy and PCT in patients with ACC.Methods:The National Database was queried to study all cirrhotics and non-cirrhotics with ACC between 2010-2014 who underwent initial PCT(with or without follow-up cholecystectomy)or cholecystectomy.Cirrhotic patients were divided into compensated and decompensated cirrhosis.Independent predictors and outcomes of initial PCT and failure to undergo subsequent cholecystectomy were studied.Results:Out of 919189 patients with ACC,13283(1.4%)had cirrhosis.Among cirrhotics,cholecystec-tomy was performed in 12790(96.3%)and PCT in the remaining 493(3.7%).PCT was more frequent in cirrhotics(3.7%)than in non-cirrhotics(1.4%).Multivariate analyses showed increased early readmis-sions[odds ratio(OR)=2.12,95%confidence interval(CI):1.43-3.13,PHealthcare utilization is seen with initial PCT amongst cirrhotic patients.This situation reflects suboptimal management of ACC in cirrhotics and a call for action.

关键词： National Healthcare Cholecystectomy Percutaneous cholecystostomy Cirrhosis Decompensated cirrhosis

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Time to forge ahead:The Internet of Things for Healthcare

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Digital Communications and Networks 2023年第1期9卷 223-235页

作者： Denzil Furtado Andre F.Gygax Chien Aun Chan Ashley I.Bush Melbourne Dementia Research Centre&Department of Chemical Engineering University of MelbourneVIC3010Australia Faculty of Business and Economics University of MelbourneVIC3010Australia Department of Electrical and Electronic Engineering University of MelbourneVIC3010Australia Florey Institute of Neuroscience and Mental Health University of MelbourneVIC3010Australia

Situated at the intersection of technology and medicine,the Internet of Things(IoT)holds the promise of addressing some of Healthcare's most pressing challenges,from medical error,to chronic drug shortages,to overburdened hospital systems,to dealing with the COVID-19 pandemic.However,despite considerable recent technological advances,the pace of successful implementation of promising IoT Healthcare initiatives has been slow.To inspire more productive collaboration,we present here a simple—but surprisingly underrated—problemoriented approach to developing Healthcare technologies.To further assist in this effort,we reviewed the various commercial,regulatory,social/cultural,and technological factors in the development of the IoT.We propose that fog computing—a technological paradigm wherein the burden of computing is shifted from a centralized cloud server closer to the data source—offers the greatest promise for building a robust and scalable Healthcare IoT ecosystem.To this end,we explore the key enabling technologies that underpin the fog architecture,from the sensing layer all the way up to the cloud.It is our hope that ongoing advances in sensing,communications,cryptography,storage,machine learning,and artificial intelligence will be leveraged in meaningful ways to generate unprecedented medical intelligence and thus drive improvements in the health of many people.

关键词： Internet of Things Healthcare Information Fog computing Artificial intelligence Machine learning Big data COVID-19 pandemic

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Decreased influenza vaccination coverage among Chinese Healthcare workers during the COVID-19 pandemic

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Infectious Diseases of Poverty 2022年第5期11卷 63-73页

作者： Libing Ma Xuan Han Yuan Ma Yuan Yang Yunshao Xu Di Liu Weizhong Yang Luzhao Feng School of Population Medicine and Public Health Chinese Academy of Medical Sciences&Peking Union Medical CollegeBeijing 100730China Department of Respiratory and Critical Care Medicine The Afliated Hospital of Guilin Medical UniversityGuilinGuangxi 541001China “Breath Circles”Network Platform BeijingChina.

Background:Healthcare workers(HCWs)were the priority group for influenza vaccination,in China during the 2020/2021 and 2021/2022 influenza seasons.However,vaccination rates in HCWs have always been low.This study investigated influenza vaccination status among Chinese HCWs and analyzed the factors driving vaccination.Methods:We provided electronic questionnaires to HCWs from January 27,2022 to February 21,2022,using the WeChat platform"Breath Circles".HCWs who received the link could also forward it to their colleagues.Binary logistic regression models were used to analyze vaccination-associated factors among HCWs.Results:Among the 1697 HCWs surveyed,vaccination coverage was 43.7%(741/1697)during the 2020/2021 influenza season,and 35.4%(600/1697)during the 2021/2022 influenza season,as of February 21,2022.Additionally,22.7%(385/1697)and 22.1%(358/1697)of HCWs reported that their workplaces implemented a free vaccination policy for all employees during the 2020/2021 and 2021/2022 influenza seasons.HCWs who were required to be vaccinated according to hospital regulations,and whose hospitals implemented the free influenza vaccine policy were more likely to be vaccinated(2020/2021 and 2021/2022;P<0.05).In addition,the economic level of the HCWs'province(2021/2022,P<0.05)and the HCWs’knowledge about vaccination and willingness to get vaccinated,such as active learning about vaccines(2020/2021,P<0.05),supportive attitude toward vaccination for all HCWs(2020/2021 and 2021/2022;P<0.05),also had an impact on vaccine coverage.Conclusions:A free influenza vaccination policy and workplace required vaccination are effective in improving influenza vaccination coverage among HCWs.Influenza vaccination coverage of Chinese HCWs remained low and showed a downward trend after the COVID-19 outbreak.Further effective measures,such as advocacy campaigns,free vaccine policies,and on-site vaccination could be implemented to improve influenza vaccination coverage.

关键词： Influenza Healthcare worker Vaccination Coverage Internet-based survey China

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Optimizing Response Strategies of Healthcare System in a Large-scale Disaster

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Journal of Safety Science and Resilience 2022年第4期3卷 288-301页

作者： Fredy Tantri Sulfikar Amir Interdisciplinary Graduate Programme Nanyang Technological University50 Nanyang AvenueSingapore 639798 Sociology Programme School of Social SciencesNanyang Technological University48 Nanyang Avenue SHHK-05-31Singapore 639818

Urban infrastructures are invariably constituted by social and technical components whose capacity to withstand crisis is determined by the resilience of their sociotechnical structures.This study aims to apply the principles of sociotechnical resilience in modeling and simulating disaster response in urban areas.Drawing on a case study of Jakarta,Indonesia,our study focuses on the role of hospitals as part of Healthcare infrastructure in response to a large-scale disaster.Each hospital is modeled as a coordinated location with a certain amount of resources,primarily in terms of medical staff.We perform sensitivity analysis through Monte Carlo simulations to observe the impacts of various response strategies,disaster severity,and communication duration on system resilience.The results show that centralized systems are generally more suitable for dealing with low disaster severity,while the decentralized strategy performs better during a disaster with worse impacts.Additionally,the time taken for communication and coordination can significantly affect the performance of centralized systems.By simulating various scenarios,parameters,and recovery protocols,the model we developed can help policymakers,city planners,and other stakeholders design proper response strategies suitable to their structural conditions and available resources during a large-scale disaster in urban cities.

关键词： Healthcare Disaster response Modeling Simulation Sociotechnical resilience Urban infrastructure

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RFID Adaption in Healthcare Organizations:An Integrative Framework

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Computers, Materials & Continua 2022年第1期70卷 1335-1348页

作者： Ahed Abugabah Louis Sanzogni Luke Houghton Ahmad Ali AlZubi Alaa Abuqabbeh College of Technical Innovation Zayed UniversityAbu DhabiUAE Nathan Campus Griffith UniversityAustralia Department of Computer Science King Saud UniversityRiyadhSaudi Arabia Golden Gate University San FranciscoCaliforniaUSA

Radio frequency identification(RFID),also known as electronic label technology,is a non-contact automated identification technology that recognizes the target object and extracts relevant data and critical characteristics using radio frequency signals.Medical equipment information management is an important part of the construction of a modern hospital,as it is linked to the degree of diagnosis and care,as well as the hospital’s benefits and growth.The aim of this study is to create an integrated view of a theoretical framework to identify factors that influence RFID adoption in Healthcare,as well as to conduct an empirical review of the impact of organizational,environmental,and individual factors on RFID adoption in the Healthcare industry.In contrast to previous research,the current study focuses on individual factors as well as organizational and technological factors in order to better understand the phenomenon of RFID adoption in Healthcare,which is characterized as a dynamic and challenging work environment.This research fills a gap in the current literature by describing how user factors can influence RFID adoption in Healthcare and how such factors can lead to a deeper understanding of the advantages,uses,and impacts of RFID in Healthcare.The proposed study has superior performance and effective results.

关键词： RFID Healthcare information technology user factors

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Privacy Preserving Image Encryption with Deep Learning Based IoT Healthcare Applications

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Computers, Materials & Continua 2022年第10期73卷 1159-1175页

作者： Mohammad Alamgeer Saud S.Alotaibi Shaha Al-Otaibi Nazik Alturki Anwer Mustafa Hilal Abdelwahed Motwakel Ishfaq Yaseen Mohamed I.Eldesouki Department of Information Systems College of Science&Art at MahayilKing Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of Information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia

Latest developments in computing and communication technologies are enabled the design of connected Healthcare system which are mainly based on IoT and Edge technologies.Blockchain,data encryption,and deep learning(DL)models can be utilized to design efficient security solutions for IoT Healthcare applications.In this aspect,this article introduces a Blockchain with privacy preserving image encryption and optimal deep learning(BPPIEODL)technique for IoT Healthcare applications.The proposed BPPIE-ODL technique intends to securely transmit the encrypted medical images captured by IoT devices and performs classification process at the cloud server.The proposed BPPIE-ODL technique encompasses the design of dragonfly algorithm(DFA)with signcryption technique to encrypt the medical images captured by the IoT devices.Besides,blockchain(BC)can be utilized as a distributed data saving approach for generating a ledger,which permits access to the users and prevents third party’s access to encrypted data.In addition,the classification process includes SqueezeNet based feature extraction,softmax classifier(SMC),and Nadam based hyperparameter optimizer.The usage of Nadam model helps to optimally regulate the hyperparameters of the SqueezeNet architecture.For examining the enhanced encryption as well as classification performance of the BPPIE-ODL technique,a comprehensive experimental analysis is carried out.The simulation outcomes demonstrate the significant performance of the BPPIE-ODL technique on the other techniques with increased precision and accuracy of 0.9551 and 0.9813 respectively.

关键词： Internet of things Healthcare decision making privacy preserving blockchain deep learning

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Impact of hospital size on Healthcare information system effectiveness: evidence from Healthcare data analytics

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Journal of Management Analytics 2022年第2期9卷 211-231页

作者： Liuliu Fu Ling Li Lusi Li Wenlu Zhang Zihao Luo CIS Department California State UniversityLos AngelesCAUSA IS Department Old Dominion UniversityNorfolkVAUSA IS Department California State UniversityLong BeachCAUSA Accounting Department Zhongnan University of Economics and LawWuhanPeople’s Republic of China

With the rapid development of information technology, the increasing use ofmobile digital devices and efforts from the whole society, the Healthcareinformation systems (HISs) are moving towards a new era. However, there isstill a lack of clear understanding of the benefits of HIS at the hospital leveland the influential factors for HIS effectiveness. In this study, we propose aresearch framework to explain how HIS implementation improves hospitalperformance. Our results reinforce the positive effect of HIS on hospitalperformance. In particular, we found that HIS implementation increases boththe cost and revenue of the hospitals, but the increasing effect in revenue ismuch bigger than the increasing effect in cost. We also found that althoughboth small and big hospitals benefit from the implementation of HIS, the effectof size is different. Size has a positive effect on hospital performance for smallhospitals but has a negative effect on big hospitals. This indicates that thecompetitive advantage of economies of scale disappears for big hospitalsbecause the level of information transparency becomes lower and transactioncosts become higher as size increases.

关键词： Healthcare Healthcare information systems HIS hospital performance size

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An Analysis of Integrating Machine Learning in Healthcare for Ensuring Confidentiality of the Electronic Records

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Computer Modeling in Engineering & Sciences 2022年第3期130卷 1387-1422页

作者： Adil Hussain Seh Jehad F.Al-Amri Ahmad F.Subahi Alka Agrawal Nitish Pathak Rajeev Kumar Raees Ahmad Khan Department of Information Technology Babasaheb Bhimrao Ambedkar UniversityLucknow226025India Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Science Umm Al-Qura UniversityMakkah21421Saudi Arabia Department of Information Technology Guru Gobind Singh Indraprastha UniversityDelhi110078India Department of Computer Applications Shri Ramswaroop Memorial UniversityBarabanki225003India Department of Computer Science and Engineering Babu Banarasi Das UniversityLucknow226028India

The adoption of sustainable electronic Healthcare infrastructure has revolutionized Healthcare services and ensured that E-health technology caters efficiently and promptly to the needs of the stakeholders associated with Healthcare.Despite the phenomenal advancement in the present Healthcare services,the major obstacle that mars the success of E-health is the issue of ensuring the confidentiality and privacy of the patients’data.A thorough scan of several research studies reveals that Healthcare data continues to be the most sought after entity by cyber invaders.Various approaches and methods have been practiced by researchers to secure Healthcare digital services.However,there are very few from the Machine learning(ML)domain even though the technique has the proactive ability to detect suspicious accesses against Electronic Health Records(EHRs).The main aim of this work is to conduct a systematic analysis of the existing research studies that address Healthcare data confidentiality issues through ML approaches.B.A.Kitchenham guidelines have been practiced as a manual to conduct this work.Seven well-known digital libraries namely IEEE Xplore,Science Direct,Springer Link,ACM Digital Library,Willey Online Library,PubMed(Medical and Bio-Science),and MDPI have been included to performan exhaustive search for the existing pertinent studies.Results of this study depict that machine learning provides a more robust security mechanism for sustainable management of the EHR systems in a proactive fashion,yet the specified area has not been fully explored by the researchers.K-nearest neighbor algorithm and KNIEM implementation tools are mostly used to conduct experiments on EHR systems’log data.Accuracy and performance measure of practiced techniques are not sufficiently outlined in the primary studies.This research endeavour depicts that there is a need to analyze the dynamic digital Healthcare environment more comprehensively.Greater accuracy and effective implementation of ML-based models are the need of the day for ensu

关键词： EHRs Healthcare machine learning systematic analysis

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IOT Assisted Biomedical Monitoring Sensors for Healthcare in Human

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Computer Systems Science & Engineering 2023年第6期45卷 2853-2868页

作者： S.Periyanayagi V.Nandini K.Basarikodi V.Sumathy Department of ECE Ramco Institute of TechnologyRajapalayam626117India Department of CSE Sona College of TechnologySalem636005India Department of Mathematics Ramco Institute of TechnologyRajapalayamIndia Government College of Engineering DharmapuriIndia

The Internet of Things(IoT)is a concept that refers to the deployment of Internet Protocol(IP)address sensors in health care systems to monitor patients’health.It has the ability to access the Internet and collect data from sensors.Automated decisions are made after evaluating the information of illness people records.Patients’health and well-being can be monitored through IoT medical devices.It is possible to trace the origins of biological,medical equipment and processes.Human reliability is a major concern in user activity and fitness trackers in day-to-day activities.The fundamental challenge is to measure the efficiency of the human system accurately.Aim to maintain tabs on the well-being of humans;this paper recommends the use of wireless body area networks(WBANs)and artificial neural networks(ANN)to create an IoT-based Healthcare framework for hospital information systems(IoT-HF-HIS).Our evaluation system uses a server to estimate how much computing power is needed for modeling,and simulations of the framework have been done using data rate and latency requirements are implementing the energy-aware technology presented in this paper.The proposed framework implements several hospital information system case studies by building a time-saving simulation environment.As the world’s population ages,more and more people suffer from physical and emotional ailments.Using the recommended strategy regularly has been proven user-friendly,reliable,and cost-effective,with an overall performance of 95.2%.

关键词： IoT WBANs ANN Healthcare biomedical sensors humans

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DeepGan-Privacy Preserving of Healthcare System Using DL

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Intelligent Automation & Soft Computing 2023年第8期37卷 2199-2212页

作者： Sultan Mesfer Aldossary Prince Sattam Bin Abdulaziz University Wadi Ad Dawaser11990Saudi Arabia

The challenge of encrypting sensitive information of a medical image in a Healthcare system is still one that requires a high level of computing complexity,despite the ongoing development of cryptography.After looking through the previous research,it has become clear that the security issues still need to be looked into further because there is room for expansion in the research field.Recently,neural networks have emerged as a cost-effective and effective optimization strategy in terms of providing security for images.This revelation came about as a result of current developments.Nevertheless,such an implementation is a technique that is expensive to compute and does not handle the huge variety of different assaults that may be made on pictures.The primary objective of the system that has been described is to provide evidence of a complex framework in which deep neural networks have been applied to improve the efficiency of basic encryption techniques.Our research has led to the development and proposal of an enhanced version of methods that have previously been used to encrypt pictures.Instead,the generative adversarial network(GAN),commonly known as GAN,will serve as the learning network that generates the private key.The transformation domain,which reflects the one-of-a-kind fashion of the private key that is to be formed,is also meant to lead the learning network in the process of actually accomplishing the private key creation procedure.This scheme may be utilized to train an excellent Deep Neural Networks(DNN)model while instantaneously maintaining the confidentiality of training medical images.It was tested by the proposed approach DeepGAN on open-source medical datasets,and three sets of data:The Ultrasonic Brachial Plexus,the Montgomery County Chest X-ray,and the BraTS18.The findings indicate that it is successful in maintaining both performance and privacy,and the findings of the assessment and the findings of the security investigation suggest that the development of suitable generation technologies is

关键词： Healthcare cryptography deep learning adversarial network privacy

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