Smart Healthcare Services Research Articles

Service quality evaluation of county-level public hospitals in Chongqing under smart healthcare

BackgroundThis study explores patients' perceptions and expectations of smart healthcare services from the perspective of patient experience.The aim is to provide insights for the development of smart healthcare services in hospitals.MethodsA cluster sampling method was used to select 10 public county-level hospitals from October to November 2021 in a municipality in southwestern China. Patient expectations and perceptions about smart healthcare services were assessed using SERVQUAL scale, which included 24 items across four dimensions: ability, application, platform, and effectiveness. The perception-expectation gap was calculated, and factors influencing this gap were analyzed. The Importance-Performance Analysis (IPA) model was then applied to evaluate the results.ResultsA total of 915 patients from outpatient and inpatient departments participated in the study. The average perception score was 3.86, while the average expectation score was 4.44, resulting in a gap of -0.58. Paired sample t-tests revealed significant differences between patients' perceptions and expectations across the 24 items (P < 0.05). IPA quadrant analysis identified 5 items in quadrant IV. A generalized linear model indicated that patients with a college degree, income between 2001 ~ 3500RMB, and income between 3501 ~ 5000RMB were associated with differences in service quality evaluation. Patients covered by urban and rural residents' medical insurance also showed variations in their evaluations.ConclusionsThe study highlights that smart healthcare services in public county-level hospitals fall short of meeting patients' expectations. It emphasizes the importance of addressing individualized medical needs and improving aspects such as cost control, system design, technology-human care balance, and operational efficiency.

Regulating learning module for patient monitoring interactive event detecting robots

Process automation robots in smart healthcare services are reliable in providing interactive patient services. The patient requirements are identified and approached through automated learning and responses. A Regulating Learning Interaction Module (RLIM) using Sigmoid Optimized Neural Networks is designed for robots in such sensitive environments. This learning module trains the robots for various services/ user interactions over patient demands. In particular, the patient interacting features on monitoring and responses/ event triggering are induced through the learning. The miscommunicated interactions are streamlined using sigmoid optimization from the conditional neural layers for assessment. This assessment delivers the possible choices to improve event detection/ response delivery. The neural network regulates the training patterns from the user inputs, histories, and patient diagnosis data for which the events are detected. This event relates the patient with the diagnosis, assistance, and monitoring for better assistance. The performance of this proposed module is validated using interaction detection, response precision, event generation rate, and miscommunication.

Artificial intelligence probabilities scheme for disease prevention data set construction in intelligent smart healthcare scenario

In the face of an aging population, smart healthcare services are now within reach, thanks to the proliferation of high-speed internet and other forms of digital technology. Data problems in smart healthcare, unfortunately, put artificial intelligence in this area to serious limitations. There are several issues, including a lack of standard samples, noisy data interference, and actual data that is missing. A three-stage AI-based data generating strategy is suggested to handle missing datasets, using a small sample dataset obtained from a smart healthcare program community in a specific city: Step one involves generating the dataset's basic attributes using a tree-based generation strategy that takes the original data distribution into account. Step two involves using the Naive Bayes algorithm to create basic indicators of behavioural capability assessment for the samples. Step three builds on stage two and uses a multivariate linear regression method to create evaluation criteria and indicators of high-level behavioural capability. Six problems involving multiple classifications and two tasks using multiple labels are implemented using various neural network-based training strategies on the obtained data to assess the usefulness of the dataset for downstream tasks. To ensure that the data collected is genuine and useful, the experimental data must be analysed and expert knowledge must be included.

A Study of the Construction of Smart Healthy Elderly Care City: An Aspect of Foshan City, Guangdong Province

The smart healthy elderly care city takes the integration of healthy elderly care resources with information technology as its basic feature, and innovates the healthy elderly care service model to accurately serve the elderly. In recent years, all parts of the country have gradually promoted the construction of smart, healthy and elderly care cities, which have achieved good results, but also face many problems. In order to evaluate the current situation of the construction of smart healthy elderly care cities, the author has built a smart healthy elderly care city evaluation index system based on the research results and standards of smart city and healthy elderly care city evaluation index system. The evaluation index system of smart health care city involves five fields, including smart health care infrastructure, smart health care environment, smart health care economy and smart health care services, which can comprehensively and systematically reflect the construction of smart health care city. Taking Foshan City, Guangdong Province as an example, this paper empirically analyzes the current situation and problems of the construction of a smart healthy elderly care city using the data from 2020-2021, and then puts forward high-quality development strategies for the construction of a smart healthy elderly care city in Foshan.

Blockchain and trusted reputation assessment-based incentive mechanism for healthcare services

Blockchain-based healthcare IoT technology research enhances security for smart healthcare services such as real-time monitoring and remote disease diagnosis. To incentivize positive behavior among participants within a blockchain-based smart healthcare system, existing efforts employ benefit distribution and reputation assessment methods to enhance performance. Yet, there remains a significant gap in multidimensional assessment strategies and consensus improvements in addressing complex healthcare scenarios. In this paper, we propose a blockchain and trusted reputation assessment-based incentive mechanism for healthcare services (BtRaI). BtRaI provides a realistic and comprehensive reputation assessment with feedback to motivate blockchain consensus node participation, thus effectively defending against malicious behavior in the healthcare service system. Specifically, BtRaI first introduces multiple moderation factors for comprehensive multidimensional reputation assessment and credibly records the assessment results on the blockchain. Then, we propose an improved PBFT algorithm, grounded in the reputation assessment, to augment blockchain consensus efficiency. Finally, BtRaI designs a token-based reward and punishment mechanism to motivate honest participation in the blockchain, inhibit potential misbehavior, and promote enhanced service quality in the healthcare system. Theoretical analysis and simulation experiments conducted across various scenarios demonstrate that BtRaI effectively suppresses malicious attacks in healthcare services, improves blockchain node fault tolerance rates, and achieves blockchain transaction processing efficiency within 0.5 s in a 100-node consortium chain. BtRaI’s reputation assessment and token incentive mechanism, characterized by realistic differentiation granularity and change curves, are well-suited for dynamic and complex healthcare service environments.

Lightweight Multi-Class Support Vector Machine-Based Medical Diagnosis System with Privacy Preservation.

Machine learning, powered by cloud servers, has found application in medical diagnosis, enhancing the capabilities of smart healthcare services. Research literature demonstrates that the support vector machine (SVM) consistently demonstrates remarkable accuracy in medical diagnosis. Nonetheless, safeguarding patients' health data privacy and preserving the intellectual property of diagnosis models is of paramount importance. This concern arises from the common practice of outsourcing these models to third-party cloud servers that may not be entirely trustworthy. Few studies in the literature have delved into addressing these issues within SVM-based diagnosis systems. These studies, however, typically demand substantial communication and computational resources and may fail to conceal classification results and protect model intellectual property. This paper aims to tackle these limitations within a multi-class SVM medical diagnosis system. To achieve this, we have introduced modifications to an inner product encryption cryptosystem and incorporated it into our medical diagnosis framework. Notably, our cryptosystem proves to be more efficient than the Paillier and multi-party computation cryptography methods employed in previous research. Although we focus on a medical application in this paper, our approach can also be used for other applications that need the evaluation of machine learning models in a privacy-preserving way such as electricity theft detection in the smart grid, electric vehicle charging coordination, and vehicular social networks. To assess the performance and security of our approach, we conducted comprehensive analyses and experiments. Our findings demonstrate that our proposed method successfully fulfills our security and privacy objectives while maintaining high classification accuracy and minimizing communication and computational overhead.

Empowering Smart Cities with Fog Computing: A Versatile Framework for Enhanced Healthcare Services and Beyond

Fog Computing represents a distributed computing infrastructure strategically positioned at the network's edge, acting as an intermediate layer between remote cloud services and the data-generating smart devices on the ground. Leveraging this concept, a flexible and efficient smart city design emerges, offering a diverse range of applications, including smart healthcare, car parking, power management, water management, and waste management. The implementation of Fog computing enables reduced data processing latency and equitable workload distribution across fog nodes. The smart city system comprises several layers, namely connection, real-time processing, neighborhood linking, main processing, and data server layers. The flexibility of this framework allows for the scaling up or down of layers depending on specific smart city applications. In a case study focused on Smart healthcare services, the iFogSim platform was utilized to evaluate the system's performance. Notably, the results demonstrated a significant reduction in network usage, data processing latency, and processing costs when compared to traditional cloud computing solutions. Consequently, this improvement in efficiency translated into an enhanced user experience, offering superior scalability and reliability to users utilizing smart city services, including healthcare facilities.

Extension of physical activity recognition with 3D CNN using encrypted multiple sensory data to federated learning based on multi-key homomorphic encryption

Background and Objective: The Internet of medical things is enhancing smart healthcare services using physical wearable sensor-based devices connected to the Internet. Machine learning techniques play an important role in the core of these services for remotely consulting patients thanks to the pattern recognition from on-device data, which is transferred to the central servers from local devices. However, transferring personally identifiable information data to servers could become a source for hackers to steal from, manipulate and perform illegal activities. Federated learning is a new branch of machine learning that creates directly training models from on-device data and aggregates these learned models on the servers without centralized data. Another way to protect data confidentiality on computer systems is data encryption. Data encryption transforms data into another form that only users with authority to a decryption key can read. In this work, we propose a novel method enabling preservation of client privacy and protection of client biomedical data from illegal hackers while transmitting through the Internet.Methods: We propose a method applying 3-dimensional convolutional neural networks for human activity recognition using multiple sensory data. In order to protect the data, we apply the bitwise XOR operator encryption technique. Then, we extend our 3-dimensional convolutional neural network methods to both traditional federated learning and the federated learning based on multi-key homomorphic encryption using the proposed encrypting data.Results: Based on leave-one-out-cross-validation, the 3-dimensional method obtains an accuracy of 94.6% and of 94.9% (without data encrypting and without federated learning) tested on two different benchmarked datasets, Sport and DaLiAC respectively. Accuracy is decreased slightly to 89.5% (from 94.6% of the baseline) when we use the proposed encrypting data method. However, the encryption-data-based method still has a potential result compared to the state-of-the-art which only uses raw data. In addition, the proposed full federated learning scheme of this work shows that illegal persons who somehow can get the trained model transmitted via networks cannot infer the private result.Conclusions: This novel method for sensory data representation which translates temporal and frequency bio-signal values to voxel intensities that can encode 3-dimensional activity images. Secondly, the proposed 3-dimensional convolutional neural network methods outperform other deep-learning-based human activity recognition approaches. Finally, extensive experiments show the proposed data-encrypted federated learning approach can achieve feasibility in terms of efficiency in privacy preservation.

Deep reinforcement learning‐based precise prediction model for Smart M‐Health system

AbstractThe use of Android mobile phones and other wireless technology in the field of healthcare infers to mHealth (mobile health). Modern Science and technological developments have paved way for better and more sophisticated solutions towards Smart mHealth systems and preventive Smart Healthcare services for disease—treatment, surveillance, management of chronic disease and tracking epidemic outbreaks. Thus, the mHealth data can be collected from various end users, deposited in repository, perform analysis and suitable Smart Healthcare services can be made accessible to end users, anytime, anywhere, over the internet. This can be achieved by incorporating the methodologies of advanced Soft Computing methodologies, data communication, cloud storage and cloud computing, big data analysis, artificial intelligence, computer communication/networking and other engineering techniques. However, the analysis of such huge volumes of data and to provide precise Smart Healthcare services is a million‐dollar question. This research article exposes a Deep Reinforcement Learning model for precisely predicting the disease and offer precise Smart m‐Healthcare services to the end users. This research provides an intensive experimental analysis and investigation using synthetic health parameters that were simulated using various mHealth sensors. The dataset includes 15 varieties of mHealth metrics with a total dataset size of 285.

Performance analysis of video data transmission for telemedicine applications with 5G enabled Internet of Things

Fifth Generation (5G) communication access technology has been implemented to provide highly reliable and efficient video data streaming in telemedicine applications. The Internet of Things (IoT) advancement enhances the 5G network for smart healthcare services and applications. The existing research work focused only on Lagrangian Encoder (LE) based video compression technique with H.265 Protocol for video data transmission in 5G networks for telemedicine applications. This paper proposes a novel KNN classifier-based H.265 protocol with a single buffer model incorporated with multiple sensors for telemedicine applications. The proposed multiple sensors are placed at the transmitter and receiver base stations to exchange data efficiently and accurately between transmitter and receiver devices. The data transmission performance is measured using collision error, propagation error, sensing error, and visual security with encryption for the proposed and existing methods. The performance of the proposed model is compared with the existing LE-based single buffer and identifies the proposed KNN classifier-based single buffer with a multi-sensor technique that performs better.

Machine Learning Based Healthcare Service Dissemination Using Social Internet of Things and Cloud Architecture in Smart Cities

Smart healthcare using the cloud and the Internet of Things (IoT) allows for remote patient monitoring, real-time data collection, improved data security, and cost-effective storage and analysis of healthcare data. This paper proposes an information-centric dissemination scheme (ICDS) for smart healthcare services in smart cities. The proposed scheme addresses the time sensitiveness of healthcare data and aims to ensure consistent dissemination. The ICDS uses decision-tree learning to classify requests based on time-sensitive features, allowing prioritization of access. The scheme also involves segregating sensitive information and distributing digital health data within the classified time to retain time sensitiveness and prioritize access. The learning is then modified for the leaves based on data significance and minimum resources to reduce waiting times and improve availability.

Ontological Model for Cohesive Smart Health Services Management

Health care has become an essential social-economic concern for all stakeholders (e.g., patients, doctors, hospitals etc.), health needs, private care and the elderly class of society. The massive increase in the usage of health care Internet of things (IoT) applications has great technological evolvement in human life. There are various smart health care services like remote patient monitoring, diagnostic, disease-specific remote treatments and telemedicine. These applications are available in a split fashion and provide solutions for variant diseases, medical resources and remote service management. The main objective of this research is to provide a management platform where all these services work as a single unit to facilitate the users. The ontological model of integrated healthcare services is proposed by getting requirements from various existing healthcare services. There were 26 smart health care services and 26 smart health care services to classify the knowledge-based ontological model. The proposed ontological model is derived from different classes, relationships, and constraints to integrate health care services. This model is developed using Protégé based on each interrelated/correlated health care service having different values. Semantic querying SPARQL protocol and RDF query language (SPARQL) were used for knowledge acquisition. The Pellet Reasoner is used to check the validity and relations coherency of the proposed ontology model. Comparative to other smart health care services integration systems, the proposed ontological model provides more cohesiveness.

Retracted: Research on Smart Healthcare Services: Based on the Design of APP Health Service Platform.

[This retracts the article DOI: 10.1155/2021/9922389.].

Patient-centered Smart Healthcare and Medical Service Systems, Augmented and Virtual Reality-based Remote Patient Monitoring and Individualized Treatments, and Medical and Metaverse Operating System Technologies in 3D Internet of Things Healthcare and Virtual Clinical Settings

Patient-centered Smart Healthcare and Medical Service Systems, Augmented and Virtual Reality-based Remote Patient Monitoring and Individualized Treatments, and Medical and Metaverse Operating System Technologies in 3D Internet of Things Healthcare and Virtual Clinical Settings

Telemedicine and Smart Healthcare—The Role of Artificial Intelligence, 5G, Cloud Services, and Other Enabling Technologies

This paper discusses telemedicine and the employment of advanced mobile technologies in smart healthcare delivery. It covers the technological advances in connected smart healthcare, including the roles of artificial intelligence, machine learning, 5G and IoT platforms, and other enabling technologies. It also presents the challenges and potential risks that could arise from delivering connected smart healthcare services. Healthcare delivery is witnessing revolutions engineered by the developments in mobile connectivity and the plethora of platforms, applications, sensors, devices, and equipment that go along with it. Human society is evolving fast in response to these technological developments, which are also pushing the connectivity-providing sector to create and adopt new waves of network technologies. Consequently, new communications technologies have been introduced into the healthcare system and many novel applications have been developed to make it easier for sharing data in various forms and volumes within health-related services. These applications have also made it possible for telemedicine to be effectively adopted. This paper provides an overview of some of the recent developments within the space of mobile connectivity and telemedicine.

Prediction of health monitoring with deep learning using edge computing

Today's modern computing environment provides a smart healthcare monitoring system for early prediction of fall detection. The Internet of Things-based health model plays a significant role in the health care service area and helps to improve the processing of data and its prediction. Transferring reports or data from one place to another takes too much time and energy, and it will cause high latency and energy issues. To handle these kinds of hazards, edge computing provides solutions. In this paper w presents smart healthcare system issues, services, and applications. Furthermore, propose a CNN-based prediction model with the use of edge computing and IoT paradigms. Edge computing is a distributed environment framework that enables rapid resource availability and response time through local edge servers computed at the end of IoT devices. The CNN model is used to analyse the health data collected by IoT devices. Furthermore, the role of edge devices is to provide doctors and patients with timely health-prediction reports via edge servers. The proposed mechanism can be analysed using accuracy and error rate performance parameters. In the proposed mechanism, the accuracy is 99.23% in comparison with other techniques.

Digital Twin-assisted Blockchain-inspired irregular event analysis for eldercare

Since the development of smart healthcare services, different solutions have been developed in the field of healthcare to increase the life expectancy of the patient by reducing the cost of healthcare. Digital Twin (DT) is considered one of the most promising technologies and a game changer in the field of healthcare. DT is generating a virtual imitation of a physical object that mimics the status of an event by changing the information in real time. In this article, a smart context-aware physical activity monitoring framework is developed by combining different advanced techniques such as IoT, DT, FoT, CoT, and Blockchain to maintain the sensitiveness of the healthcare domain. In the proposed study, the physical movements of an elder are analyzed by utilizing the sequential data processing capability of deep learning to detect irregular physical events. In addition, the proposed framework can keep the data of an individual secured by applying progressed security highlights of blockchain. The proposed solution effectively analyzed an irregular event of an individual with considerable accuracy in real time. The calculated outcomes have shown the effectiveness of DT with smart healthcare solutions that would help to develop effective medical services by bringing patients and medical care experts together. Furthermore, the performance of the proposed solution is measured with respect to irregular event recognition, model training and testing, rate of latency, and data processing cost. In this manner, a case study defines the effectiveness of the proposed methodology in the smart healthcare industry.

A Systematic Literature Review of Enabling IoT in Healthcare: Motivations, Challenges, and Recommendations

Internet of things (IoT) has revolutionized how we utilize technology over the past decade. IoT’s rapid growth affects several fields, including the healthcare sector. As a result, the concept of smart healthcare or electronic healthcare (e-healthcare) has emerged. Smart healthcare promises to enhance people’s lives and wellbeing by monitoring them, offering an efficient connection, improving mobility, gathering medical data, and decreasing hospital and patient costs. IoT in healthcare is still one of the hot and trendy topics that needs in-depth investigation. No recent review has been conducted to elucidate the extent of research in the area, features of published papers, motives, and challenges in enabling IoT in healthcare systems. This study presents a comprehensive systematic review of the screened articles published between 2015 and 2022 pertaining to enabling IoT in healthcare services and applications. A total of 106 papers fulfilled the final inclusion criteria and were analyzed using systematic literature review (SLR). Two procedures were used to review the final articles: First, publications are examined in terms of study designs, publishing journals, and topics/study objectives. In the second approach, motives, challenges, and recommendations for enabling IoT in healthcare systems are explored. This article summarizes published studies on IoT in healthcare systems and its usage in smart healthcare service delivery. Based on the reviewed studies, recommendations for future research to enable the effective application of IoT in healthcare and service delivery are proposed.

Dual adaption based evolutionary algorithm for optimized the smart healthcare communication service of the Internet of Things in smart city

The Internet of Things (IoT) is a revolutionary technique of sharing data for smart devices that generates huge amounts of data from smart healthcare systems. Therefore, healthcare systems utilize the convergence power and traffic analysis of sensors that cannot be satisfactorily handled by the IoT. In this article, a novel mutation operator is devised and incorporated with the differential evolution (DE) algorithm. Two tests have been conducted in the validation process. Firstly, the newly dual adaption-based operators incorporated with the differential evolution algorithm are being proposed. The proposed approach provides sufficient diversity and enhances the search speed of nature’s local and global search environments in the problem. The proposed method incorporates the application of IoT-based smart healthcare. Second, an application-based test has been conducted, in which the proposed approach is applied to the application in the smart healthcare system. Therefore, IoT sensor deployment is an optimization problem to minimize service time, delay, and energy loss by considering the communication constraint between sensors(objects). The proposed algorithm is applied in this article to solve this optimization problem. Further, in the experimentation and comparative study, the proposed method is superior to the standard evolutionary algorithms in IoT applications concerning the minimum number of function evaluations and minimization of traffic services. The proposed approach also achieves efficiency in the minimum loss of energy in each service and reduces load and delay.

A Cloud-Based Machine Learning Approach to Reduce Noise in ECG Arrhythmias for Smart Healthcare Services.

ECG (electrocardiogram) identifies and traces targets and is commonly employed in cardiac disease detection. It is necessary for monitoring precise target trajectories. Estimations of ECG are nonlinear as the parameters TDEs (time delays) and Doppler shifts are computed on receipt of echoes where EKFs (extended Kalman filters) and electrocardiogram have not been examined for computations. ECG, certain times, results in poor accuracies and low SNRs (signal-to-noise ratios), especially while encountering complicated environments. This work proposes to track online filter performances while using optimization techniques to enhance outcomes with the removal of noise in the signal. The use of cost functions can assist state corrections while lowering costs. A new parameter is optimized using IMCEHOs (Improved Mutation Chaotic Elephant Herding Optimizations) by linearly approximating system nonlinearity where multi-iterative function (Optimized Iterative UKFs) predicts a target's unknown parameters. To obtain optimal solutions theoretically, multi-iterative function takes less iteration, resulting in shorter execution times. The proposed multi-iterative function provides numerical approximations, which are derivative-free implementations. Signals are updated in the cloud environment; the updates are received by the patients from home. The simulation evaluation results with estimators show better performances in terms of reduced NMSEs (normalized mean square errors), RMSEs (root mean squared errors), SNRs, variances, and better accuracies than current approaches. Machine learning algorithms have been used to predict the stages of heart disease, which is updated to the patient in the cloud environment. The proposed work has a 91.0% accuracy rate with an error rate of 0.05% by reducing noise levels.