Smart Healthcare Services Research Articles

A Case Study on Implementation of Health Data Standards for Smart Healthcare

Electronic Health Records (EHR) and other health standards are in use for quite some time now in some of the developed countries. Simultaneously, with the advancement of ICT-based infrastructure for providing smart healthcare services, volume of health data has increased vastly and storage and management of this huge volume ofdata, which also have the other properties of big data, has evolved as a major challenge. The objective of this paper is to investigate whether the presently available EHR standards and other related standards can adequately handle the data, which are generated through a smart healthcare system. Here, we consider some of the well-known EHR standards and related standards, which have been proposed and are commonly used in various countries. These standards are studied in the context of data storage, data representation and data handling. Suitability of these standards is analyzed in terms of different evaluation factors, such as portability, scalability, and interoperability. Moreover, the implementation experiences of these standards are also considered. For this analysis, the authors consulted the survey papers and research papers describing the experiences of the researchers, as well as the users. The paper concludes that in a smart healthcare system various types of data are generated, that include structured data like EHRs, as well as unstructured clinical data of patients, some of which need to be accessed quickly and frequently. Thus, an EHR system should be supplemented with models for representing unstructured data. A suitable ontology is required for designing a storage structure for storing healthcare data.

SMAD: SMart assistant during and after a medical emergency case based on deep learning sentiment analysis: The pandemic COVID-19 case.

The huge cost of emergency situations could have fatal effects on humanity and society, and it could present a genuine threat to both of them. In fact, most people confronted with an emergency could feel psychological trauma, which will, for the most part, change over time as they can exhibit chaotic or even turbulent behaviours. The situation could worsen in the case of a pandemic as fear and anxiety invade and spread in addition to isolation and quarantine. In this paper, we propose to build a smart assistant, called SMAD, that could detect the symptoms of an emergency case as well as symptoms of a mental disorder while analysing the natural language speech of an ordinary citizen, during and after an emergency situation using natural language processing and deep learning sentiment analysis model to track the patient’s mental state during an ongoing conversation. Our proposed smart assistant is an online human-bot interaction that could handle a variety of physical and mental circumstances of any emergency situation. The proposed approach is a smart healthcare service that consists of four interconnected modules: The information understanding module, the data collector module, the action generator module, and the mental analysis module, which is based on the sentiment analysis model performed on a social media dataset using a pre-trained word-embedding model.

Mobile Application Characteristics and User Perspective in Smart Healthcare Service Applications

The current COVID-19 pandemic has an impact on accelerating the digital transformation of various sectors in Indonesia, one of which is the health sector. Hence, many mobile applications of smart healthcare service have been developed, which can be a solution for people to get better health services. The aim of the research is to evaluate the mobile application (SpeedId and SpeedQ) for smart healthcare service based on the user perspective and the characteristics of the mobile application. The research is an analytic observational study that contains five stages of research. There are 64 respondents in the research using a questionnaire as data collection tool. Then, through a literature review, seven variables are determined, and a research model is proposed. The evaluation of the mobile application characteristics includes the operating system and network availability as independent variables and response time, responsiveness, and interface accessibility as dependent variables. Meanwhile, evaluating user perspective consists of respondents’ gender and education as independent variables and duration and ease of use as dependent variables. From the mobile application characteristics, the results show that operating system and network availability do not significantly affect all dependent variables. From the user perspective, gender only significantly affects the duration variable. Meanwhile, education significantly impacts the duration and the ease of use.

Energy-Efficient and Secure Opportunistic Routing Protocol for WSN: Performance Analysis with Nature-Inspired Algorithms and Its Application in Biomedical Applications.

Wireless sensor network (WSN) is made up of tiny sensor nodes. The application of WSN in diverse fields has seen a tremendous escalation in recent years. WSN applications are constrained by the limited set of computing resources possessed by the sensor nodes and the security aspects of data communication in the WSN. Many algorithms based on nature-inspired optimization (NIO) have been proposed in the past to optimize the issue of energy efficiency and security in WSN. In the proposed work, two opportunistic routing algorithms, i.e., intelligent opportunistic routing protocol (IOP) and trust-based secure intelligent opportunistic routing protocol (TBSIOP), are compared against two NIO algorithms developed for achieving energy efficiency and security in WSN for performance analysis. The performance is evaluated by simulating the algorithms on MATLAB and comparing the obtained results with existing ACO-based and PSO-based routing algorithms. It is observed that the TBSIOP outperforms the NIO-based algorithms in terms of energy efficiency, network lifetime, packet delivery ratio, end-to-end delay, and average risk level. All the parameters under consideration are recorded in the presence of a maximum of 50% malicious nodes for 25, 50, and 100 nodes' test cases. The increasing size of the network has a significant effect on the performance of TBSIOP, as the packet delivery ratio is close to 100%. Also, TBSIOP can easily avoid malicious nodes during the routing process as reflected from the results. This will improve the network lifetime of TBSIOP compared to other protocols. As far as the application of the work is concerned, it would be beneficial for smart healthcare services. It can also help in better communication during the sharing of data by providing energy-efficient services and keeping the network alive for a longer period.

Optimal Edge Nodes Deployment With Multi Association for Smart Health

The further evolution and integration of internet of things (IoT) and artificial intelligence (AI) technologies would transform traditional health care processes into smart health care services, which leverages various wireless sensors, cameras, and controllers to collect multi-dimensional medical information and analyze them intelligently. In case of this, the massive amount of data generated by smart health devices need to be transferred, processed and stored quickly, which poses great challenges to traditional cloud-based communications with single association mechanism. To address this problem, this paper proposes an innovative multi-access edge computing based access network with multi-association mechanism, where a single smart device could associate and communicate with multiple edge nodes. The deployment of edge nodes for proposed access network is formulated as an extended knapsack problem, which considers not only signal to interference plus noise rate (SINR) requirement of devices, but also power constraint and load balance of edge nodes and it is solved by the simulated annealing (SA) algorithm. Numerical results via SA algorithm show that the proposed multi-association mechanism can improve the SINR of devices significantly, while achieving better load balancing among edge nodes. All these together provides good reference to build an efficient access network for future smart healthcare system.

Slight: A lightweight authentication scheme for smart healthcare services

Critical infrastructures (CIs) include the vital resources for the country’s economic and health systems and should be kept secure. We face improvements in the Internet of Things which brings benefits and, at the same time, dependency for CIs. Internet of Medical Things (IoMT) is among the CI sectors that gather health-related information from patients via sensors and provide healthcare services accordingly. However, research has highlighted that this large-scale system opens the door to the patients’ private data disclosure. Recent work has concentrated on proposing authentication schemes to address this challenge. Motivated by this, in this paper, we introduce a secure and lightweight authentication and key agreement model named Slight. We informally prove Slight’s security and robustness against attacks and formally by using the Scyther tool. We analyze Slight’s performance to show it causes minimal computational overhead (0.0076 ms) and comparable communication overhead (1632 bits), making it suitable for IoMT.

How Does Smart Healthcare Service Affect Resident Health in the Digital Age? Empirical Evidence From 105 Cities of China.

With the emergence of the digital age, smart healthcare services based on the new generation of information technologies play an increasingly important role in improving the quality of resident health. This study empirically examined the impact of regional smart healthcare services on resident health as well as the underlying mechanism by employing a two-way fixed effects model. We constructed a Regional Smart Healthcare Service Development Index and matched it with survey data from the China Health and Retirement Longitudinal Study to validate the model. The results showed that (1) smart healthcare services have a significant positive impact on resident health. (2) The availability of outpatient services and inpatient services plays a mediating role in the relationship between regional smart healthcare services and resident health. (3) The influence of regional smart healthcare services on resident health is heterogeneous among different regions. Specifically, the effect of smart healthcare services on resident health is significant in the eastern regions, while it is not significant in the central, western, and northeastern regions. The effect of smart healthcare services on resident health is significant in rural regions but not in urban regions. This study enriches the nascent research stream of smart healthcare services. This study offers useful insights for practitioners and the government to guide them in formulating smart healthcare strategies.

Analysis and Improvement of Blockchain-Based Multilevel Privacy-Preserving Location Sharing Scheme for Telecare Medical Information Systems

Patient location sharing is an important part of modern smart healthcare and mobile medical services. Blockchain has many attractive properties and is suitable for managing patient locations in telecare medical information systems (TMIS). Recently, Ji et al. proposed a blockchain-based multilevel privacy-preserving location sharing (BMPLS) scheme for TMIS. In this paper, we show that Ji et al.’s BMPLS scheme does not achieve confidentiality and multilevel privacy-preserving. An adversary outside the system can use an ordinary personal computer to completely break the system within a dozen hours and obtain the location of any patient at any time. The adversary inside the system can use an ordinary personal computer to obtain the location of the designated patient within tens of seconds. Using salting technology, we propose an improved BMPLS scheme to fix our attacks. We also optimized the BMLS scheme to make it correct and executable. The security analysis shows that the improved BMPLS scheme achieves decentralization, untamperability, confidentiality, multilevel privacy-preserving, retrievability, and verifiability. The simulation shows that the improved BMPLS scheme is practical, the computational overhead of the location record phase is within 10 ms, and the computational overheads of the location sharing and location extraction phases are both within 30 ms.

Artificial intelligence healthcare service resources adoption by medical institutions based on TOE framework.

ObjectivesThis study used the Technology-Organization-Environment (TOE) framework to identify the factors involved in the decisions made by integrated medical and healthcare organizations to adopt artificial intelligence (AI) elderly care service resources.MethodThis study identified the Decision-making Trial and Evaluation Laboratory-Interpretive Structural Modeling (DEMATEL-ISM) method was used to construct a multilayer recursive structural model and to analyze the interrelationships between the levels. A MICMAC quadrant diagram was used for a cluster analysis.ResultsThe ISM recursive structural model was divided into a total of seven layers. The bottom layer contained the four factors of High risk of data leakage (T1), Lack of awareness of the value and benefits of AI healthcare technology (T5), Lack of management leadership support (O1), and Government policies (E1). Having a low dependency but high driving force, these factors are the root causes of adoption by healthcare organizations. The topmost layer contained the most direct factors, which had a high dependency but the low driving force, influencing adoption: Competitive pressures (E2), Lack of patient trust (E5), and Lack of excellent partnerships (E7). Healthcare organizations are more concerned with technology and their environments when deciding to adopt intelligent healthcare resources.ConclusionThe combination of the three methods of DEMATEL-ISM-MICMAC construction models provides new ideas for smart healthcare services for hospitals. The DEMATEL method favors the construction dimension of the micro-model, while the ISM method favors the construction dimension of the macro-model. Combining these two methods may reduce the loss of information within the system, simplify the matrix calculation workload, and improve the efficiency of operations while decomposing the complex problems into several sub-problems in a more comprehensive and detailed way. Conducting cluster analysis of the adoption determinants utilizing MICMAC quadrant diagrams may provide strong methodological guidance and decision-making recommendations for government departments, senior decision-makers in healthcare organizations, and policy-makers in associations in the senior care industry.

A design method for an intelligent manufacturing and service system for rehabilitation assistive devices and special groups

The maturity of Industrial 4.0 technologies (smart wearable sensors, Internet of things [IoT], cloud computing, etc.) has facilitated the iteration and digitization of rehabilitation assistive devices (RADs) and the innovative development of intelligent manufacturing systems of RADs, expanding the value-added component of smart healthcare services. The intelligent manufacturing service mode, based on the concept of the product life cycle, completes the multi-source data production process analysis and the optimization of manufacturing, operation, and maintenance through intelligent industrial Internet of things and other means and improves the product life cycle management and operation mechanism. The smart product-service system (PSS) realizes the value-added of products by providing users with personalized products and value-added services, service efficiency, and sustainable development and gradually forms an Internet-product-service ecosystem. However, research on the PSS of RADs for special populations is relatively limited. Thus, this paper provides an overview of an IoT-based production model for RADs and a smart PSS-based development method of multimodal healthcare value-added services for special people. Taking the hand rehabilitation training devices for autistic children as a case, this paper verifies the effectiveness and availability of the proposed method. Compared with the traditional framework, the method used in this paper primarily helps evaluate rehabilitation efficacy, personalizes schemes for patients, provides auxiliary intelligent manufacturing service data and digital rehabilitation data for RAD manufacturers, and optimizes the product iteration development procedures by combining user-centered product interaction, multimodal evaluation, and value-added design. This study incorporates the iterative design of RADs into the process of smart PSS to provide some guidance to the RADs design manufacturers.

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

With the development of information technology, big data, and cloud computing, the concept of smart healthcare has gradually become more and more important. Compared with the traditional healthcare service, the new model, health service platform, is becoming increasingly popular and convenient. The use of wearable monitoring devices and some APPs is improving the health monitoring efficacy and effectiveness. To improve and facilitate the smart healthcare services, an effective and convenient app health service platform is needed urgently to serve the older and younger. Based on the above, this paper elaborates the principles of health service system and health information perception terminal design of the APP health service platform.

The roles of trust, personalization, loss of privacy, and anthropomorphism in public acceptance of smart healthcare services

AI-based smart healthcare services are emerging as promising tools to improve efficiency and effectiveness of healthcare service delivery. This study aimed to examine the roles of trust and three AI-specific characteristics (i.e., personalization, loss of privacy, anthropomorphism) in public acceptance of smart healthcare services based on an extended Technology Acceptance Model. The model's validity was confirmed using a partial least squares structural equation modeling technique based on data collected from 769 survey samples. Multigroup analyses were conducted to determine whether the path coefficients differed by gender, age, and usage experience. The results showed that perceived usefulness, perceived ease of use, and the three AI-specific characteristics were important determinants of public acceptance of smart healthcare services, whose roles were fully or partially mediated by trust. Trust, perceived usefulness, and personalization directly determined behavioral intention to use smart healthcare services. The relationships among antecedent factors and behavioral intention to use smart healthcare services were also moderated by gender, age, and usage experience. The study demonstrated the critical roles of personalization, loss of privacy, and anthropomorphism in shaping public trust and acceptance of smart healthcare services. The results offer important theoretical and practical implications for the design and implementation of such services.

Deep Learning-Based Stroke Disease Prediction System Using Real-Time Bio Signals.

The emergence of an aging society is inevitable due to the continued increases in life expectancy and decreases in birth rate. These social changes require new smart healthcare services for use in daily life, and COVID-19 has also led to a contactless trend necessitating more non-face-to-face health services. Due to the improvements that have been achieved in healthcare technologies, an increasing number of studies have attempted to predict and analyze certain diseases in advance. Research on stroke diseases is actively underway, particularly with the aging population. Stroke, which is fatal to the elderly, is a disease that requires continuous medical observation and monitoring, as its recurrence rate and mortality rate are very high. Most studies examining stroke disease to date have used MRI or CT images for simple classification. This clinical approach (imaging) is expensive and time-consuming while requiring bulky equipment. Recently, there has been increasing interest in using non-invasive measurable EEGs to compensate for these shortcomings. However, the prediction algorithms and processing procedures are both time-consuming because the raw data needs to be separated before the specific attributes can be obtained. Therefore, in this paper, we propose a new methodology that allows for the immediate application of deep learning models on raw EEG data without using the frequency properties of EEG. This proposed deep learning-based stroke disease prediction model was developed and trained with data collected from real-time EEG sensors. We implemented and compared different deep-learning models (LSTM, Bidirectional LSTM, CNN-LSTM, and CNN-Bidirectional LSTM) that are specialized in time series data classification and prediction. The experimental results confirmed that the raw EEG data, when wielded by the CNN-bidirectional LSTM model, can predict stroke with 94.0% accuracy with low FPR (6.0%) and FNR (5.7%), thus showing high confidence in our system. These experimental results demonstrate the feasibility of non-invasive methods that can easily measure brain waves alone to predict and monitor stroke diseases in real time during daily life. These findings are expected to lead to significant improvements for early stroke detection with reduced cost and discomfort compared to other measuring techniques.

Intelligent Control and Security of Fog Resources in Healthcare Systems via a Cognitive Fog Model

There have been significant advances in the field of Internet of Things (IoT) recently, which have not always considered security or data security concerns: A high degree of security is required when considering the sharing of medical data over networks. In most IoT-based systems, especially those within smart-homes and smart-cities, there is a bridging point (fog computing) between a sensor network and the Internet which often just performs basic functions such as translating between the protocols used in the Internet and sensor networks, as well as small amounts of data processing. The fog nodes can have useful knowledge and potential for constructive security and control over both the sensor network and the data transmitted over the Internet. Smart healthcare services utilise such networks of IoT systems. It is therefore vital that medical data emanating from IoT systems is highly secure, to prevent fraudulent use, whilst maintaining quality of service providing assured, verified and complete data. In this article, we examine the development of a Cognitive Fog (CF) model, for secure, smart healthcare services, that is able to make decisions such as opting-in and opting-out from running processes and invoking new processes when required, and providing security for the operational processes within the fog system. Overall, the proposed ensemble security model performed better in terms of Accuracy Rate, Detection Rate, and a lower False Positive Rate (standard intrusion detection measurements) than three base classifiers (K-NN, DBSCAN, and DT) using a standard security dataset (NSL-KDD).

Edge Intelligence for Empowering IoT-Based Healthcare Systems

The demand for real-time, affordable, and efficient smart healthcare services is increasing exponentially due to the technological revolution and burst of population. To meet the increasing demands on this critical infrastructure, there is a need for intelligent methods to cope with the existing related challenges. In this regard, edge computing technology can reduce latency and energy consumption by moving processes closer to the data sources in comparison to the traditional centralized cloud and IoT-based healthcare systems. In addition, by bringing automated insights into the smart healthcare systems, artificial intelligence (AI) provides the possibility of detecting and predicting high-risk diseases in advance, decreasing medical costs for patients, and offering efficient treatments. The objective of this article is to highlight the benefits of the adoption of the edge intelligent technology, along with the use of AI in smart healthcare systems. Moreover, a novel smart healthcare model is proposed to boost the utilization of AI and edge technology in smart healthcare systems. Additionally, we discuss potential challenges and future research directions arising when integrating these different technologies.

Ontology Driven Smart Health Service Integration

Background and objectiveThe massive increase, in the Internet of Things applications, has greatly evolved technological aspects of human life. The drastic development of IoT based smart healthcare services have layout the smart process models to facilitate all stakeholders (e.g. patients, doctors, hospitals etc.) and made it an important social-economic concern. There are variety of smart healthcare services like remote patient monitoring, diagnostic, disease specific remote treatments and telemedicine. Many trending Internet of Health Things research and development are done in a very disjoint and independent fashion providing solutions and guidelines for variant diseases, medical resources and remote services management. These expositions work over many shared resources such as health facilities for patient and human in healthcare system. MethodsThis research discusses the ontology for merging methods to form an integrated platform with shared knowledge of smart healthcare services. The proposed process model creates an ontological framework of integrated healthcare services, which are firstly defined using ontologies and lately integrated over similarities, differences, dependencies and other semantic relations. The data and process requirements for service integration facility is derived from various smart healthcare services. ResultsThe proposed model is evaluated using two-step ontological modeling testing method, applied at the ontological framework of integrated smart health services. First evaluation step has targeted the model consistency validation using reasoning tool while querying tools are used to validate the retrieved data entities and relations among them for predefined use-cases. ConclusionsThe research concluded with a novel approach for smart health service integration using ontological modeling and merging techniques. The model efficiency enhancement and query optimization methods are listed in future tasks of the research.

Socioeconomic Effects on Household Access to Primary Healthcare Facilities in Selected Riverine Communities, Rivers State, Nigeria

Access to healthcare is complex and varies across countries, regions, communities and individuals. The study aimed to assess the socioeconomic effects on household access to Primary Healthcare (PHC) facilities in selected riverine communities in Rivers State, Nigeria. Objectives of the study are to characterise the socioeconomic condition of households in the study area; determine the spatial distribution of PHC facilities in the study area, identify the effects of the socioeconomic conditions of households to access to PHC facilities in the selected riverine communities in the study area, and suggest appropriate policy measures to improve households access to PHC facilities and services in the study area. The study employed Mixed Methods Research (MMR) approach and sequential explanatory research design for data collection and analyses. The study employed simple random and purposive sampling techniques. A total of 400 respondents (households) were interviewed including key informants (government agencies and professionals) and Geographic Information System (GIS) was used for map analysis to show spatial distribution of PHC facilities in the study area. However, 328 questionnaires were considered valid for analysis of data. The study revealed, most of the households’ occupations are fishing, artisan and petty trading characterising low socioeconomic status. The study found PHC facilities are not evenly spatially distributed in the study area which has made households to use various available transport modes to access the facilities since the area is separated by creeks, rivers and sea. This has made travel time, distance and cost economically unfavourable to households in term of accessibility since they are relatively poor economically. The study has suggested the following recommendations including improvement of the socioeconomic status of households through education and vocational training programmes that will provide better employment opportunities, government should provide Mobile Healthcare programmes and services through water vessels and ambulances to reach remoted communities, proper study should be carried out to ascertain the population and their characteristics before locating PHC facilities for optimum utilisation, Smart Healthcare Services (SHCS) should be provided through Tele-Healthcare facilities to overcome distance and time, and government should collaborate with non-governmental organisations (NGOs) and multinational to provide Mobile Healthcare services in riverine communities to reduce challenges of accessibility and cost on households.

Enhancing smart healthcare in dentistry: an approach to managing patients’ stress

ABSTRACT This paper presents a model of a smart healthcare service for stress management in dental patients during the interventions. The main goal is to provide dental clinics with a model that enables introducing a stress management service into everyday practice and provides patients with a better experience in a typically stressful situation. The approach is based on employing wearable sensors for monitoring physiological parameters, and a mobile application for progressive muscle relaxation therapy. Dental patients were divided into experimental and control groups. Participants from the experimental group were treated with progressive muscle relaxation through mobile health application with audio content, and patients from the control group were not exposed to any relaxation method. Heart rate was measured in both groups through three test phases: pre-intervention, intervention, and post-intervention. Evaluation of the anxiety level was performed using the STAI test. Results show that the measured heart rate in the post-intervention phase is lower than in the intervention phase in both testing groups, as well as in the pre-intervention phase. STAI scores were significantly higher in the control group through all test phases. The research found that the proposed system applied to dentist patients may relieve their anxiety symptoms and decrease stress level, which improves the patients’ experience and leads to higher patients’ satisfaction.

IoT for a More Reliable and Safer Patient Monitoring Healthcare Service during the Pandemic

The internet of things (IoT) has been widely identified as one of the best solutions to make a smart life for human beings. Moreover, it can also be applied to alleviate the pressures on healthcare systems that improve the access and quality of care and essentially reduce the cost of care. This research presents a method to apply the IoT health monitoring method to hospital wings and make a much-developed smart healthcare service that is more comfortable and safer, especially in a pandemic situation. This paper proposes a real-time monitor system consisting of healthcare wearables such as BPM (beats per minute) meter, body temperature meterand central communication hub. This research mainly focuses on making a more comfortable health monitoring system compared to other modern-day systems. Moreover, the purposed system consisted of several data presentation methods for easier data analyzing on the patient.

Quality of Service‐aware clustered triad layer architecture for critical data transmission in multi‐body area network environment

AbstractWireless Body Area Network (WBAN) is an important element of future smart healthcare services in smart cities. For this reason, many research works have been undergone in WBAN. The significant research issue in the WBAN environment is Quality of Service (QoS) provisioning. In literature, some of the works have been focused on QoS aspect of WBAN. However, they have only marginal impact due to ineffective network design and the usage of a single sink node. Also, most of the research works have been tested within a single BAN and present intra‐BAN communication. In practical, an effective multi‐BAN communication system is emerging due to the need of large‐scale healthcare system. This paper addresses these issues and designs a novel QoS‐aware Clustered Triad Layer (QC‐TriL) architecture for multi‐BAN environment. The novel QC‐TriL uses dual sink nodes in each BAN and both sinks are deployed in the optimal positions. To determine the optimal position, Type‐II Fuzzy Logic (T2FL) is proposed with different criteria. Next, we form clusters based on the positions of dual sink nodes. QC‐TriL adopts criticality‐aware routing by presenting Delay‐aware One‐Hop Transmission and Fused Rank Scheme (FuRank) for critical data and normal data routing respectively. The data transmission is organized by a novel 1D‐Dragonfly topology‐based Priority Dual TDMA (PD‐TDMA) scheduling protocol. Finally, multi‐BAN communication is enabled with an optimal route selected by QoS‐aware Emperor Penguin Colony (QoS‐EPC) algorithm. The QC‐TriL multi‐BAN environment is modeled in OMNeT++ simulator and evaluated in terms of energy consumption, packet delivery ratio, throughput, and delay. The experiments show promising results for the multi‐BAN environment.