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Over the past decade, healthcare systems have started to establish control centres to manage patient flow, with a view to removing delays and increasing the quality of care. Such centres—here dubbed Healthcare Capacity Command/Coordination Centres (HCCCs)—are a challenge to design and operate. Broad-ranging surveys of HCCCs have been lacking, and design for their human users is only starting to be addressed. In this review we identified 73 papers describing different kinds of HCCCs, classifying them according to whether they describe virtual or physical control centres, the kinds of situations they handle, and the different levels of Rasmussen's [1] risk management framework that they integrate. Most of the papers (71%) describe physical HCCCs established as control centres, whereas 29% of the papers describe virtual HCCCs staffed by stakeholders in separate locations. Principal functions of the HCCCs described are categorised as business as usual (BAU) (48%), surge management (15%), emergency response (18%), and mass casualty management (19%). The organisation layers that the HCCCs incorporate are classified according to the risk management framework; HCCCs managing BAU involve lower levels of the framework, whereas HCCCs handling the more emergent functions involve all levels. Major challenges confronting HCCCs include the dissemination of information about healthcare system status, and the management of perspectives and goals from different parts of the healthcare system. HCCCs that take the form of physical control centres are just starting to be analysed using human factors principles that will make staff more effective and productive at managing patient flow. [ABSTRACT FROM AUTHOR]

The aim of the study is to assess the feasibility of Drugs Rational Usage Guideline System (DRUGS)-supported clinical pathway (CP) for breast carcinoma, cataract, inguinal hernia and 2-diabetes mellitus whether the application of such a system could improve work efficiency, medical safety, and decrease hospital cost. Four kinds of diseases which included 1773 cases (where 901 cases using paper-based clinical pathways and 872 cases using DRUGS-supported clinical pathways) were selected and their demographic and clinical data were collected. The evaluation criteria were length of stay, preoperative length of stay, hospital cost, antibiotics prescribed during hospitalization, unscheduled surgery, complications and prognosis. The median total LOS was 1 to 3 days shorter in the DRUGS-supported CP group as compared to the Paper-based CP group for all types ( p < 0.05). Totel hospital cost decreased significantly in the DRUGS-supported CP group than that in Paper-based CP group. About antibiotics prescribed during hospitalization, there were no statistically differences in the time of initial dose of antibiotic and the duration of administration except the choice of antibiotic categories. The proportion of DRUGS-supported clinical pathway conditions where a broad-spectrum antibiotic was prescribed decreased from 63.6 to 34.5 % ( p < 0.01) in the Paper-based group. While after the intervention, the differences were statistically not significant in unscheduled surgery, complications and prognosis. In this study, DRUGS-supported clinical pathway for breast carcinoma, cataract, inguinal hernia, 2-diabetes mellitus was smoothly shifted from a paper-based to an electronic system, and confer benefits at the hospital level. [ABSTRACT FROM AUTHOR]

The main goal of this article is to identify the main dimensions of a model proposal for increasing the potential of big data research in Healthcare for medical doctors' (MDs') learning, which appears as a major issue in continuous medical education and learning. The paper employs a systematic literature review of main scientific databases (PubMed and Google Scholar), using the VOSviewer software tool, which enables the visualization of scientific landscapes. The analysis includes a co-authorship data analysis as well as the co-occurrence of terms and keywords. The results lead to the construction of the learning model proposed, which includes four health big data key areas for MDs' learning: 1) data transformation is related to the learning that occurs through medical systems; 2) health intelligence includes the learning regarding health innovation based on predictions and forecasting processes; 3) data leveraging regards the learning about patient information; and 4) the learning process is related to clinical decision-making, focused on disease diagnosis and methods to improve treatments. Practical models gathered from the scientific databases can boost the learning process and revolutionise the medical industry, as they store the most recent knowledge and innovative research. [ABSTRACT FROM AUTHOR]

This paper addresses the challenges and solutions in developing a holistic prevention mobile health application (mHealth app) for Germany's healthcare sector. Despite Germany's lag in healthcare digitalization, the app aims to enhance primary prevention in physical activity, nutrition, and stress management. A significant focus is on user participation and usability to counter the prevalent issue of user attrition in mHealth applications, as described by Eysenbach's 'law of attrition'. The development process, conducted in a scientific and university context, faces constraints like limited budgets and external service providers. The study firstly presents the structure and functionality of the app for people with statutory health insurance in Germany and secondly the implementation of user participation through a usability study. User participation is executed via usability tests, particularly the think-aloud method, where users verbalize their thoughts while using the app. This approach has proven effective in identifying and resolving usability issues, although some user feedback could not be implemented due to cost-benefit considerations. The implementation of this study into the development process was able to show that user participation, facilitated by methods like think-aloud, is vital for developing mHealth apps. Especially in health prevention, where long-term engagement is a challenge. The findings highlight the importance of allocating time and resources for user participation in the development of mHealth applications. [ABSTRACT FROM AUTHOR]

In healthcare organizations, clinical workflows are executed by interdisciplinary healthcare teams (IHTs) that operate in ways that are difficult to manage. Responding to a need to support such teams, we designed and developed the MET4 multi-agent system that allows IHTs to manage patients according to presentation-specific clinical workflows. In this paper, we describe a significant extension of the MET4 system that allows for supporting rich team dynamics (understood as team formation, management and task-practitioner allocation), including selection and maintenance of the most responsible physician and more complex rules of selecting practitioners for the workflow tasks. In order to develop this extension, we introduced three semantic components: (1) a revised ontology describing concepts and relations pertinent to IHTs, workflows, and managed patients, (2) a set of behavioral rules describing the team dynamics, and (3) an instance base that stores facts corresponding to instances of concepts from the ontology and to relations between these instances. The semantic components are represented in first-order logic and they can be automatically processed using theorem proving and model finding techniques. We employ these techniques to find models that correspond to specific decisions controlling the dynamics of IHT. In the paper, we present the design of extended MET4 with a special focus on the new semantic components. We then describe its proof-of-concept implementation using the WADE multi-agent platform and the Z3 solver (theorem prover/model finder). We illustrate the main ideas discussed in the paper with a clinical scenario of an IHT managing a patient with chronic kidney disease. [ABSTRACT FROM AUTHOR]

Ongoing research efforts have been examining how to utilize artificial intelligence technology to help healthcare consumers make sense of their clinical data, such as diagnostic radiology reports. How to promote the acceptance of such novel technology is a heated research topic. Recent studies highlight the importance of providing local explanations about AI prediction and model performance to help users determine whether to trust AI's predictions. Despite some efforts, limited empirical research has been conducted to quantitatively measure how AI explanations impact healthcare consumers' perceptions of using patient-facing, AI-powered healthcare systems. The aim of this study is to evaluate the effects of different AI explanations on people's perceptions of AI-powered healthcare system. In this work, we designed and deployed a large-scale experiment (N = 3,423) on Amazon Mechanical Turk (MTurk) to evaluate the effects of AI explanations on people's perceptions in the context of comprehending radiology reports. We created four groups based on two factors—the extent of explanations for the prediction (High vs. Low Transparency) and the model performance (Good vs. Weak AI Model)—and randomly assigned participants to one of the four conditions. Participants were instructed to classify a radiology report as describing a normal or abnormal finding, followed by completing a post-study survey to indicate their perceptions of the AI tool. We found that revealing model performance information can promote people's trust and perceived usefulness of system outputs, while providing local explanations for the rationale of a prediction can promote understandability but not necessarily trust. We also found that when model performance is low, the more information the AI system discloses, the less people would trust the system. Lastly, whether human agrees with AI predictions or not and whether the AI prediction is correct or not could also influence the effect of AI explanations. We conclude this paper by discussing implications for designing AI systems for healthcare consumers to interpret diagnostic report. [ABSTRACT FROM AUTHOR]

Electronic health record (EHR) systems improve health care services by allowing the combination of health data with clinical decision support features and clinical image analyses. This study presents a modular and distributed platform that is able to integrate and accommodate heterogeneous, multidimensional (omics, histological images and clinical) data for the multi-angled portrayal and management of skin cancer patients. The proposed design offers a layered analytical framework as an expansion of current EHR systems, which can integrate high-volume molecular -omics data, imaging data, as well as relevant clinical observations. We present a case study in the field of dermatology, where we attempt to combine the multilayered information for the early detection and characterization of melanoma. The specific architecture aspires to lower the barrier for the introduction of personalized therapeutic approaches, towards precision medicine. The paper describes the technical issues of implementation, along with an initial evaluation of the system and discussion. [ABSTRACT FROM AUTHOR]

Intelligent agents and healthcare have been intimately linked in the last years. The intrinsic complexity and diversity of care can be tackled with the flexibility, dynamics and reliability of multi-agent systems. The purpose of this review is to show the feasibility of applying intelligent agents in the healthcare domain and use the findings to provide a discussion of current trends and devise future research directions. A review of the most recent literature (2009-2014) of applications of agents in healthcare is discussed, and two classifications considering the main goal of the health systems as well as the main actors involved have been investigated. This review shows that the number of published works exhibits a growing interest of researchers in this field in a wide range of applications. [ABSTRACT FROM AUTHOR]

The provision of Quality of Service (QoS) and Quality of Experience (QoE) is a mandatory requirement when transmitting telemedicine traffic, due to information relevance to maintain the patient’s health. The main objective of this paper is to present a review of existing research works in the literature, referring to QoS and QoE in telemedicine and eHealth applications. The academic databases that were used to perform the searches are Google Scholar, IEEE Xplore, PubMed, Science Direct and Web of Science, taking into account the date of publication from 2008 to the present. These databases cover the most information of scientific texts in multidisciplinary fields, engineering and medicine. Several search criteria were established such as ‘QoS’ AND ‘eHealth’ OR ‘Telemedicine’, ‘QoE’ AND ‘eHealth’ AND ‘Telemedicine’ etc. selecting the items of greatest interest. A total of 248 papers related to QoS and QoE in telemedicine and eHealth have been found, of which 39 papers have been identified as relevant works. The results show that the percentage of studies related to QoS in literature is higher with 74.36% to QoE with 25.64%. From the review of the research articles analyzed, it can be said that QoS and QoE in telemedicine and eHealth are important and necessary factors to guarantee the privacy, reliability, quality and security of data in health care systems. [ABSTRACT FROM AUTHOR]

This paper describes the development of statistical classifiers to help diagnose meningococcal meningitis, i.e. the most sever, infectious and deadliest type of this disease. The goal is to find a mechanism able to determine whether a patient has this type of meningitis from a set of symptoms that can be directly observed in the earliest stages of this pathology. Currently, in Brazil, a country that is heavily affected by meningitis, all suspected cases require immediate hospitalization and the beginning of a treatment with invasive tests and medicines. This procedure, therefore, entails expensive treatments unaffordable in less developed regions. For this purpose, we have gathered together a dataset of 22,602 records of suspected meningitis cases from the Brazilian state of Bahia. Seven classification techniques have been applied from input data of nine symptoms and other information about the patient such as age, sex and the area they live in, and a 10 cross-fold validation has been performed. Results show that the techniques applied are suitable for diagnosing the meningococcal meningitis. Several indexes, such as precision, recall or ROC area, have been computed to show the accuracy of the models. All of them provide good results, but the best corresponds to the J48 classifier with a precision of 0.942 and a ROC area over 0.95. These results indicate that our model can indeed help lead to a non-invasive and early diagnosis of this pathology. This is especially useful in less developed areas, where the epidemiologic risk is usually high and medical expenses, sometimes, unaffordable. [ABSTRACT FROM AUTHOR]

This paper presents an ensemble based classification proposal for predicting neurological outcome of severely traumatized patients. The study comprises both the whole group of patients and a subgroup containing those patients suffering traumatic brain injury (TBI). Data was gathered from patients hospitalized in the Intensive Care Unit (ICU) of the University Hospital in Salamanca. Predictive models were induced from both epidemiologic and clinical variables taken at the emergency room and along the stay in the ICU. The large number of variables leads to a low accuracy in the classifiers even when feature selection methods are used. In addition, the presence of a much larger number of instances of one of the classes in the subgroup of TBI patients produces a significantly lesser precision for the minority class. Usual ways of dealing with the last problem is to use undersampling and oversampling strategies, which can lead to the loss of valuable data and overfitting problems respectively. Our proposal for dealing with these problems is based in the use of ensemble multiclassifiers as well as in the use of an ensemble playing the role of base classifier in multiclassifiers. The proposed strategy gave the best values of the selected quality measures (accuracy, precision, sensitivity, specificity, F-measure and area under the Receiver Operator Characteristic curve) as well as the closest values of precision for the two classes under study in the case of the classification from imbalanced data. [ABSTRACT FROM AUTHOR]

Recently, Artificial Intelligence (AI) has been used widely in medicine and health care sector. In machine learning, the classification or prediction is a major field of AI. Today, the study of existing predictive models based on machine learning methods is extremely active. Doctors need accurate predictions for the outcomes of their patients' diseases. In addition, for accurate predictions, timing is another significant factor that influences treatment decisions. In this paper, existing predictive models in medicine and health care have critically reviewed. Furthermore, the most famous machine learning methods have explained, and the confusion between a statistical approach and machine learning has clarified. A review of related literature reveals that the predictions of existing predictive models differ even when the same dataset is used. Therefore, existing predictive models are essential, and current methods must be improved. [ABSTRACT FROM AUTHOR]

Advances supported by emerging wearable technologies in healthcare promise patients a provision of high quality of care. Wearable computing systems represent one of the most thrust areas used to transform traditional healthcare systems into active systems able to continuously monitor and control the patients' health in order to manage their care at an early stage. However, their proliferation creates challenges related to data management and integration. The diversity and variety of wearable data related to healthcare, their huge volume and their distribution make data processing and analytics more difficult. In this paper, we propose a generic semantic big data architecture based on the 'Knowledge as a Service' approach to cope with heterogeneity and scalability challenges. Our main contribution focuses on enriching the NIST Big Data model with semantics in order to smartly understand the collected data, and generate more accurate and valuable information by correlating scattered medical data stemming from multiple wearable devices or/and from other distributed data sources. We have implemented and evaluated a Wearable KaaS platform to smartly manage heterogeneous data coming from wearable devices in order to assist the physicians in supervising the patient health evolution and keep the patient up-to-date about his/her status. [ABSTRACT FROM AUTHOR]

With the advances in wearable computing and various wireless technologies, there is an increasing trend to outsource body signals from wireless body area network (WBAN) to outside world including cyber space, healthcare big data clouds, etc. Since the environmental and physiological data collected by multimodal sensors have different importance, the provisioning of quality of service (QoS) for the sensory data in WBAN is a critical issue. This paper proposes multiple level-based QoS design at WBAN media access control layer in terms of user level, data level and time level. In the proposed QoS provisioning scheme, different users have different priorities, various sensory data collected by different sensor nodes have different importance, while data priority for the same sensor node varies over time. The experimental results show that the proposed multi-level based QoS provisioning solution in WBAN yields better performance for meeting QoS requirements of personalized healthcare applications while achieving energy saving. [ABSTRACT FROM AUTHOR]

Wearable computing is becoming a more and more attracting field in the last years thanks to the miniaturisation of electronic devices. Wearable healthcare monitoring systems (WHMS) as an important client of wearable computing technology has gained a lot. Indeed, the wearable sensors and their surrounding healthcare applications bring a lot of benefits to patients, elderly people and medical staff, so facilitating their daily life quality. But from a research point of view, there is still work to accomplish in order to overcome the gap between hardware and software parts. In this paper, we target the problem of congestion control when all these healthcare sensed data have to reach the destination in a reliable manner that avoids repetitive transmission which wastes precious energy or leads to loss of important information in emergency cases, too. We propose a congestion control scheme CCS_WHMS that ensures efficient and fair data delivery while used in the body wearable system part or in the multi-hop inter bodies wearable ones to get the destination. As the congestion detection paradigm is very important in the control process, we do experimental tests to compare between state of the art congestion detection methods, using MICAz motes, in order to choose the appropriate one for our scheme. [ABSTRACT FROM AUTHOR]

To protect the transmission of the sensitive medical data, a secure and efficient authenticated key agreement scheme should be deployed when the healthcare delivery session is established via Telecare Medicine Information Systems (TMIS) over the unsecure public network. Recently, Islam and Khan proposed an authenticated key agreement scheme using elliptic curve cryptography for TMIS. They claimed that their proposed scheme is provably secure against various attacks in random oracle model and enjoys some good properties such as user anonymity. In this paper, however, we point out that any legal but malicious patient can reveal other user's identity. Consequently, their scheme suffers from server spoofing attack and off-line password guessing attack. Moreover, if the malicious patient performs the same time of the registration as other users, she can further launch the impersonation attack, man-in-the-middle attack, modification attack, replay attack, and strong replay attack successfully. To eliminate these weaknesses, we propose an improved ECC-based authenticated key agreement scheme. Security analysis demonstrates that the proposed scheme can resist various attacks and enables the patient to enjoy the remote healthcare services with privacy protection. Through the performance evaluation, we show that the proposed scheme achieves a desired balance between security and performance in comparisons with other related schemes. [ABSTRACT FROM AUTHOR]

The current coronavirus disease 2019 (COVID-19) pandemic has caused significant strain on medical centers resources. Thus, concerns about the reducing and management of COVID-19 are on the rise, as there is need to provide diagnosis, treatment, monitoring, and follow-ups during the pandemic. Therefore, the COVID-19 pandemic has radically and quickly altered how medical practitioners provide care to patients. Medical centers are now responding to COVID-19 through rapid adoption of digital tools and technologies such as telemedicine and virtual care which refer to the delivery of healthcare services digital or at a distance using Information and Communications Technology (ICT) for treatment of patients. Telemedicine is expected to deliver timely care while minimizing exposure to protect medical practitioners and patients. Accordingly, a rapid literature review was conducted, and 35 research studies published from 2019 to May 2020 were employed to provide theoretical and practical evidence on the significance of using telemedicine and virtual care for remote treatment of patients during the COVID-19 pandemic. This article provides practical guide based on how to use telemedicine and virtual care during the COVID-19 pandemic. This study provides implication on the potentials of consolidating virtual care solutions in the near future towards contributing to integrate digital technologies into healthcare. [ABSTRACT FROM AUTHOR]

The use of embodied conversational agents in mental health has increased in the last years. Several studies exist describing the benefits and advantages of this technology as a complement to psychotherapeutic interventions for the prevention and treatment of depression, anxiety, or post-traumatic stress disorder, to name a few. A small number of these works implement capabilities in the virtual agent focused on the detection and prevention of suicidality risks. The work presented in this paper describes the development of an embodied conversational agent used as the main interface in HelPath, a mobile-based application addressed to individuals detected with any of the suicidal behaviours: ideation, planning or attempt. The main objective of HelPath is to continuously collect user's information that, complemented with data from the electronic health record, supports the identification of risks associated with suicidality. Through the virtual agent, the users also receive information and suggestions based on cognitive behaviour therapy that would help them to maintain a healthy condition. The paper also presents the execution of an exploratory pilot to assess the acceptability, perception and adherence of users towards the virtual agent. The obtained results are presented and discussed, and some actions for further improvement of the embodied conversational agent are also identified. [ABSTRACT FROM AUTHOR]

New sources of operational data are leading to novel healthcare delivery system design and opportunities to support operational planning and decision-making. Technologies such as real time locating systems (RTLS) provide a unique view and understanding of how healthcare delivery settings behave and respond to operational design changes. In this paper RTLS data from an outpatient clinical setting is leveraged to identify the appropriate number of scheduled providers in order to improve the utilization of the clinical space while balancing the negative effects of clinic congestion. The approaches presented pair historical utilization rates for the clinical space with scheduled provider and patient volumes to support scheduling decisions in an operationally flexible clinic design. These historical data are augmented with clinic staff observation logs to identify target utilization rates as well as high congestion levels. Results are presented for two approaches: one where utilization of clinical space is a key performance metric and another where the decision-maker may be risk averse toward the use of provider time and use a probabilistic approach to determine provider staffing levels. [ABSTRACT FROM AUTHOR]

The demand of healthcare systems for chronically ill patients and elderly has increased in the last few years. This demand is derived by the necessity to allow patients and elderly to be independent in their homes without the help of their relatives or caregivers. The prosperity of the information technology plays an essential role in healthcare by providing continuous monitoring and alerting mechanisms. In this paper, we survey the most recent applications in healthcare monitoring. We organize the applications into categories and present their common architecture. Moreover, we explain the standards used and challenges faced in this field. Finally, we make a comparison between the presented applications and discuss the possible future research paths. [ABSTRACT FROM AUTHOR]

We investigated the role of both individual-level social needs and community-level social determinants of health (SDOH) in explaining emergency department (ED) utilization rates. We also assessed the potential synergies between the two levels of analysis and their combined effect on patterns of ED visits. We extracted electronic health record (EHR) data between July 2016 and June 2020 for 1,308,598 unique Maryland residents who received care at Johns Hopkins Health System, of which 28,937 (2.2%) patients had at least one documented social need. There was a negative correlation between median household income in a neighborhood with having a social need such as financial resource strain, food insecurity, and residential instability (correlation coefficient: -0.05, -0.01, and − 0.06, p = 0, respectively). In a multilevel model with random effects after adjusting for other factors, living in a more disadvantaged neighborhood was found to be significantly associated with ED utilization statewide and within Baltimore City (OR: 1.005, 95% CI: 1.003–1.007 and 1.020, 95% CI: 1.017–1.022, respectively). However, individual-level social needs appeared to enhance the statewide effect of living in a more disadvantaged neighborhood with the OR for the interaction term between social needs and SDOH being larger, and more positive, than SDOH alone (OR: 1.012, 95% CI: 1.011–1.014). No such moderation was found in Baltimore City. To our knowledge, this study is one of the first attempts by a major academic healthcare system to assess the combined impact of patient-level social needs in association with community-level SDOH on healthcare utilization and can serve as a baseline for future studies using EHR data linked to population-level data to assess such synergistic association. [ABSTRACT FROM AUTHOR]

Medical care has become an indispensable part of people’s lives, with a dramatic increase in the volume of medical data (e.g., diagnosis certificates and medical records). Medical data, however, is easily stolen, tampered with, or even completely deleted. If the above occurs, medical data cannot be recorded or retrieved in a reliable manner, resulting in delay treatment progress, even endanger the patient’s life. In this paper, we propose a novel blockchain-based data preservation system (DPS) for medical data. To provide a reliable storage solution to ensure the primitiveness and verifiability of stored data while preserving privacy for users, we leverage the blockchain framework. With the proposed DPS, users can preserve important data in perpetuity, and the originality of the data can be verified if tampering is suspected. In addition, we use prudent data storage strategies and a variety of cryptographic algorithms to guarantee user privacy; e.g., an adversary is unable to read the plain text even if the data are stolen. We implement a prototype of the DPS based on the real world blockchain-based platform Ethereum. Performance evaluation results demonstrate the effectiveness and efficiency of the proposed system. [ABSTRACT FROM AUTHOR]

The widely use of IoT technologies in healthcare services has pushed forward medical intelligence level of services. However, it also brings potential privacy threat to the data collection. In healthcare services system, health and medical data that contains privacy information are often transmitted among networks, and such privacy information should be protected. Therefore, there is a need for privacy-preserving data collection (PPDC) scheme to protect clients (patients) data. We adopt (a,k)-anonymity model as privacy pretection scheme for data collection, and propose a novel anonymity-based PPDC method for healthcare services in this paper. The threat model is analyzed in the client-server-to-user (CS2U) model. On client-side, we utilize (a,k)-anonymity notion to generate anonymous tuples which can resist possible attack, and adopt a bottom-up clustering method to create clusters that satisfy a base privacy level of (a1,k1)-anonymity. On server-side, we reduce the communication cost through generalization technology, and compress (a1,k1)-anonymous data through an UPGMA-based cluster combination method to make the data meet the deeper level of privacy (a2,k2)-anonymity (a1 ≥ a2, k2 ≥ k1). Theoretical analysis and experimental results prove that our scheme is effective in privacy-preserving and data quality. [ABSTRACT FROM AUTHOR]

This article presents a comprehensive review of the state-of-the-art applications and methodologies related to the use of unmanned aerial vehicles (UAVs) in the healthcare sector, with a particular focus on path planning. UAVs have gained remarkable attention in healthcare during the outbreak of COVID-19, and this study explores their potential as a viable option for medical transportation. The survey categorizes existing studies by mission type, challenges addressed, and performance metrics to provide a clearer picture of the path planning problems and potential directions for future research. It highlights the importance of addressing the path planning problem and the challenges that UAVs may face during their missions, including the UAV delivery range limitation, and discusses recent solutions in this field. The study concludes by encouraging researchers to conduct their studies in a realistic environment to reveal UAVs' real potential, usability, and feasibility in the healthcare domain. [ABSTRACT FROM AUTHOR]

Embodied conversational agents (ECAs) are advanced computational interactive interfaces designed with the aim to engage users in the continuous and long-term use of a background application. The advantages and benefits of these agents have been exploited in several e-health systems. One of the medical domains where ECAs are recently applied is to support the detection of symptoms, prevention and treatment of mental health disorders. As ECAs based applications are increasingly used in clinical psychology, and due that one fatal consequence of mental health problems is the commitment of suicide, it is necessary to analyse how current ECAs in this clinical domain support the early detection and prevention of risk situations associated with suicidality. The present work provides and overview of the main features implemented in the ECAs to detect and prevent suicidal behaviours through two scenarios: ECAs acting as virtual counsellors to offer immediate help to individuals in risk; and ECAs acting as virtual patients for learning/training in the identification of suicide behaviours. A literature review was performed to identify relevant studies in this domain during the last decade, describing the main characteristics of the implemented ECAs and how they have been evaluated. A total of six studies were included in the review fulfilling the defined search criteria. Most of the experimental studies indicate promising results, though these types of ECAs are not yet commonly used in routine practice. The identification of some open challenges for the further development of ECAs within this domain is also discussed. [ABSTRACT FROM AUTHOR]

Introduction: European Union intends to enable cross-border health services through a program referred to as "MyHealth@EU". The first main service is the dispensation of medicine by interlinking national electronic prescription systems. The second one is the Patient Summary, which enables providing the basic set of patients' medical data. Methods: The contemporary technical documentation of the project was studied and selected published Key Performance Indicators of the project were analyzed. Where necessary, data were acquired directly from the European Commission. Results: Data from the start of the project (fourth quarter of 2019) until the second quarter of 2022 were analyzed. During this time both the overall number of EU countries with operational cross-border healthcare and their particular abilities in both services have risen. At present, there are eleven countries with capabilities in at least one of the services, of which nine have reported transactions. More countries are in the test phase now and will join the operational phase of the project shortly. Discussion and Conclusion: Nevertheless, the program is still used mostly for testing purposes. It seems that only electronic prescription and dispensation are commonly and widely used so far and only Estonian and Finnish patients usually get their medication dispensed abroad. The rest of the operational countries is still at present missing country pairs with a strong cross-border use case. [ABSTRACT FROM AUTHOR]

New, emerging technologies, transform every day our life and have direct consequence on our health and well-being. More and more wearable medical devices (MD) with wireless communication technologies embedded are being developed by innovative academic community and companies. Optical wireless communication (OWC) consisting of Visible Light Communication (VLC), infrared (IR), Optical Camera Communication (OCC) and Light Fidelity (LiFi) along with the conventional Radio Frequency (RF) wireless communication are suitable technologies to be used for hybrid Wireless Integrated Medical Assistance Systems (WIMAS). The WIMAS addressed in this paper consists of two Wireless Medical Body Area Networks (WMBAN) (an insulin wearable kit and an ECG test device with VLC/OCC are considered) and an Emergency Remote Medical Assistance (ERMA) with LiFi technology embedded. Using RF in medical facilities is subject of strict regulations due to interferences with other RF medical devices, negative effects on human health and lack of security. VLC and OCC are suitable to be embedded in MDs in order to be used by the patients with wearable WMBAN. Research on IR transdermal communication for implantable MDs has also been demonstrated as feasible and both VLC and OCC have promising future, as well. On the other hand, LiFi technology, recently deployed on the market, is mature enough to be integrated in the ERMA system addressed here. [ABSTRACT FROM AUTHOR]

In today's world, 46.8 million people suffer from brain related diseases. Dementia is most prevalent of all. In general scenario, a dementia patient lacks proper guidance in searching out the way to return back at his/her home. Thus, increasing the risk of getting damaged at individual-health level. Therefore, it is important to track their movement in more sophisticated manner as possible. With emergence of wearables, GPS sensors and Internet of Things (IoT), such devices have become available in public domain. Smartphone apps support caregiver to locate the dementia patients in real-time. RF, GSM, 3G, Wi-Fi and 4G technology fill the communication gap between patient and caregiver to bring them closer. In this paper, we incorporated 7 most popular wearables for investigation to seek appropriateness for dementia tracking in recent times in systematic manners. We performed an in-depth review of these wearables as per the cost, technology wise and application wise characteristics. A case novel study i.e. IoT-based Force Sensor Resistance enabled System-FSRIoT, has been proposed and implemented to validate the effectiveness of IoT in the domain of smarter dementia patient tracking in wearable form factor. The results show promising aspect of a whole new notion to leverage efficient assistive physio-medical healthcare to the dementia patients and the affected family members to reduce life risks and achieve a better social life. [ABSTRACT FROM AUTHOR]

The use of contemporary technologies in healthcare systems to improve quality of care and to promote behavioral healthcare outcomes are prevalent in high-income countries. However, low and middle-income countries (LMICs) are not receiving the same advantages of technology, which may be due to inadequate technological infrastructure and financial resources, lack of interest among policy makers and healthcare service providers, lack of skills and capacity among healthcare professionals in using technology based interventions, and resistance of the public to the use of technologies for healthcare or health promotion activities. Technology-based interventions offer considerable promise to develop entirely new models of healthcare both within and outside of formal systems of care and offer the opportunity to have a large public health impact. Such technology-based interventions could be used to address targeted global health problems in LMICs, including the chronic non-communicable diseases (NCDs) - a growing health system burden in LMICs. Major preventable behavioral risk factors of chronic NCDs are increasing in LMICs, and innovative interventions are essential to address these risk factors. Computer-based or mobile-based virtual coaches or Relational Agents (RAs) are increasingly being explored for counseling patients to change their health behavior in high-income countries; however, the use of RAs in LMICs has not been studied. In this paper, we summarize the growing application of RA technology in behavior change interventions in high-income countries and describe the potential of its use in LMICs. Finally, we review the potential barriers and challenges in promoting RAs in LMICs. [ABSTRACT FROM AUTHOR]

Now-a-days, the society is witnessing a keen urge to enhance the quality of healthcare services with the intervention of technology in the health sector. The main focus in transforming traditional healthcare to smart healthcare is on facilitating the patients as well as medical professionals. However, this changover is not easy due to various issues of security and integrity associated with it. Security of patients's personal health record and privacy can be handled well by permitting only authorized access to the confidential health-data via suitably designed authentication scheme. In pursuit to contribute in this direction, we came across the role of Universal Serial Bus (USB), the most widely accepted interface, in enabling communication between peripheral devices and a host controller like laptop, personal computer, smart phone, tablet etc. In the process, we analysed a recently proposed a three-factor authentication scheme for consumer USB Mass Storage Devices (MSD) by He et al. In this paper, we demonstrate that He et al.'s scheme is vulnerable to leakage of temporary but session specific information attacks, late detection of message replay, forward secrecy attacks, and backward secrecy attacks. Then motivated with the benefits of USB, we propose a secure three-factor authentication scheme for smart healthcare. [ABSTRACT FROM AUTHOR]

Big data has become one of the most imperative technologies for collecting, handling and analysing enormous volumes of data in a high-performance environment. Enterprise healthcare organizations needs high compute power for the large volume of sensitive data, as well as large storage for storing both data and results, preferably in the cloud. However, security and privacy of patient data have become a critical issue that restricts many healthcare services from using cloud services to their optimal level. Therefore, this issue has limited healthcare organizations from migrating patient data to a cloud storage, because the cloud operators have chance to access sensitive data without the owner's permission. This paper proposes an intelligent security system called Intelligent Framework for Healthcare Data Security (IFHDS). IFHDS enables to secure and process large-scale data using column-based approach with less impact on the performance of data processing. The intelligent framework intends masking personal data and to encrypt sensitive data only. The proposed IFHDS splits sensitive data into multiple parts according to sensitivity level, where each part is stored separately over distributed cloud storage. Splitting data based on sensitivity level prevents cloud provider to break complete record of data if succeeds to decrypt part of data. The experimental results confirm that the proposed system secure the sensitive patient data with an acceptable computation time compared to recent security approaches. [ABSTRACT FROM AUTHOR]

With increased patient access to data, healthcare services are experiencing change where patients are moving away from being mere passive actors towards becoming more active and involved participants. In this paper, we explore the role of patient accessible electronic health records (PAEHRs) with respect to this increase in patient involvement. The study was performed as a case study and included nine interviews with patients and a survey that was responded to by 56 patients. Our results show that PAEHRs have a role in the enhancement of patient involvement because PAEHRs (i) foster a more balanced relationship between patients and healthcare professionals and (ii) increase access to information. [ABSTRACT FROM AUTHOR]

Background: Electronic Health Records (EHRs) can help clinicians to plan, document and deliver care for patients in healthcare services. When used consistently, EHRs can advance patient safety and quality, and reduce clinician's workload. However, usability problems can make it difficult for clinicians to use EHRs effectively, which can negatively impact both healthcare professionals and patients. Objective: To improve usability of EHRs within a mental health service in the UK. Methods: This was a feasibility study conducted with two mental health teams. A mixed-methods approach was employed. Focus group discussions with clinicians identified existing usability problems in EHRs and changes were made to address these problems. Updated EHR assessment forms were evaluated by comparing the following measures pre and post changes: (1) usability testing to monitor time spent completing and duplicating patient information in EHRs, (2) clinician's experience of using EHRs, and (3) proportion of completed EHR assessment forms. Results: Usability testing with clinicians (n = 3) showed that the time taken to complete EHR assessment forms and time spent duplicating patient information decreased. Clinician's experience of completing EHR assessment forms also significantly improved post changes compared to baseline (n = 71; p < 0.005). There was a significant increase in completion of most EHR forms by both teams after EHR usability improvements (all at p < 0.01). Conclusions: Usability improvements to EHRs can reduce the time taken to complete forms, advance clinician's experience and increase usage of EHRs. It is important to engage healthcare professionals in the usability improvement process of EHRs in mental health services. [ABSTRACT FROM AUTHOR]

With critical importance of medical healthcare, there exist urgent needs for in-depth medical studies that can access and analyze specific physiological signals to provide theoretical support for practical clinical care. As a consequence, obtaining the valuable medical data with minimal cost and impacts on hospital work comes as the first concern of researchers. Anesthesia plays a widely recognized role in surgeries, which attracts people to undertake relevant research. In this paper, a real-time physiological medical signal data acquisition system (PMSDA) for the multi-operating room applications is proposed with high universality of the hospital practical settings and research requirements. By utilizing a wireless communication approach, it provides an easily accessible network platform for collection of physiological medical signals such as photoplethysmogram (PPG), electrocardiograph (ECG) and electroencephalogram (EEG) during the surgery. In addition, the raw data is stored on a server for safe backup and further analysis of depth of anesthesia (DoA). Results show that the PMSDA exhibits robust, high quality performance and efficiently reduces costs compared to previously manual methods and allows seamless integration into hospital environment, independent of its routine work. Overall, it provides a pragmatic and flexible surgery-data acquisition system model with low impact and resource cost applicable to research in critical and practical medical circumstances. [ABSTRACT FROM AUTHOR]

Telecare Medical Information System (TMIS) provides the flexible and convenient e-health care. It helps the patients to gain health monitoring information and provides patients to share their experience wirelessly. Traditional authentication and key agreement (AKA) protocols in TMIS are mostly considered in same-domain environment. However, future generation network may integrate various of wireless mesh networks under various domain. What’s more, patients heterogeneous cross-domain service has become an inevitable trend. However, there is still no heterogeneous cross-domain authenticated protocol between PKI-domain and IBC-domain in TMIS. In this paper, we propose a heterogeneous cross-domain AKA protocol with symptoms-matching in TMIS (short for CDAKA). It not only keeps good security features, but also truly provides patients’ anonymity to protect sensitive information from illegal interception. It still provides patients in two different domains to share their experience, broaden their understanding of illness by using their mobile device freely. Besides, it can realize AKA with extremely low computing cost and communication cost. What’s more, it is proved to be secure against known possible attacks under the Elliptic Curve Computable Diffie-Hellman problem (ECDHP) assumption in the random oracle model. Hence, these features make CDAKA protocol very suitable for mobile application scenarios, where resource is severely constrained and security is particularly concerned. [ABSTRACT FROM AUTHOR]

The burden on healthcare services in the world has increased substantially in the past decades. The quality and quantity of care have to increase to meet surging demands, especially among patients with chronic heart diseases. The expansion of information and communication technologies has led to new models for the delivery healthcare services in telemedicine. Therefore, mHealth plays an imperative role in the sustainable delivery of healthcare services in telemedicine. This paper presents a comprehensive review of healthcare service provision. It highlights the open issues and challenges related to the use of the real-time fault-tolerant mHealth system in telemedicine. The methodological aspects of mHealth are examined, and three distinct and successive phases are presented. The first discusses the identification process for establishing a decision matrix based on a crossover of ‘time of arrival of patient at the hospital/multi-services’ and ‘hospitals’ within mHealth. The second phase discusses the development of a decision matrix for hospital selection based on the MAHP method. The third phase discusses the validation of the proposed system. [ABSTRACT FROM AUTHOR]

Assessing employee performance is one of the most important issue in healthcare management services. Because of their direct relationship with patients, nurses are also the most influential hospital staff who play a vital role in providing healthcare services. In this paper, a novel Data Envelopment Analysis Matrix (DEAM) approach is proposed for assessing the performance of nurses based on relative efficiency. The proposed model consists of five input variables (including type of employment, work experience, training hours, working hours and overtime hours) and eight output variables (the outputs are amount of hours each nurse spend on each of the eight activities including documentation, medical instructions, wound care and patient drainage, laboratory sampling, assessment and control care, follow-up and counseling and para-clinical measures, attendance during visiting and discharge suction) have been tested on 30 nurses from the heart department of a hospital in Iran. After determining the relative efficiency of each nurse based on the DEA model, the nurses’ performance were evaluated in a DEAM format. As results the nurses were divided into four groups; superstars, potential stars, those who are needed to be trained effectively and question marks. Finally, based on the proposed approach, we have drawn some recommendations to policy makers in order to improve and maintain the performance of each of these groups. The proposed approach provides a practical framework for hospital managers so that they can assess the relative efficiency of nurses, plan and take steps to improve the quality of healthcare delivery. [ABSTRACT FROM AUTHOR]

Will Artificial Intelligence (AI) re-humanize or de-humanize medicine? As AI becomes pervasive in clinical medicine, we argue that the ethical framework that sustains a responsible implementation of such technologies should be reconsidered. The emergence of AI in the clinical setting will challenge our traditional moral boundaries surrounding discussions of patient care since it is unclear how the implementation of AI will promote and enhance the humanistic dimensions of medical practice. There are reasons for concern regarding 1) the anthropological implications of AI in the clinical context; 2) the method(s) and framework(s) used to address ethical issues in medicine; and 3) the impact of AI on clinical practice, particularly in the nature of clinical judgment. These three concerns are examined in hopes of providing pointers for subsequent and more in-depth discussions regarding clinical practice but the training of future health professionals. [ABSTRACT FROM AUTHOR]

There is a rapidly evolving need for e-health to support chronic disease self-management and connect patients with their healthcare teams. Patients with cirrhosis have a high symptom burden, significant comorbidities, and a range of psychological and cognitive issues. Patients with cirrhosis were assessed for their readiness and interest in e-health. Adults attending one of two outpatient cirrhosis clinics in Alberta were recruited. Eligible participants were not required to own or have experience with digital technologies or the Internet. Medical history, socioeconomic status, and attitudes regarding e-health, the Computer Proficiency Questionnaire, and the Mobile Device Proficiency Questionnaire were used to describe participants' knowledge and skills. Of the 117 recruited patients, 68.4% owned a computer and 84.6% owned a mobile device. Patients had mean proficiency scores of 72.8% (SD 25.9%) and 69.3% (SD 26.4%) for these devices, respectively. In multiple regression analyses, significant predictors of device proficiency were age, education, and household income. Most patients (78.7%) were confident they could participate in videoconferencing after training and most (61.5%) were interested in an online personalized health management program. This diverse group of patients with cirrhosis had technology ownership, proficiency, and online behaviours similar to the general population. Moreover, the patients were very receptive to e-health if training was provided. This promising data is timely given the unique demands of COVID-19 and its influence on self-management and healthcare delivery to a vulnerable population. [ABSTRACT FROM AUTHOR]

The coronavirus disease 2019 (COVID-19), which is caused by a novel coronavirus (SARS-COV-2), has compromised health care systems and normal management of patients with cardiovascular diseases [1–3]. Patients with non-communicable diseases, including acute myocardial infarction (AMI) are vulnerable to this stress [4-5]. Acute ST segment elevation myocardial infarction (STEMI), the most critical type of AMI, is associated with high mortality even with modern medicine [6–8]. Timely reperfusion therapy is critical for STEMI patients because a short ischemia time is associated with better clinical outcomes and lower acute and long -term mortality [9–12]. The COVID-19 pandemic placed the management of STEMI patients in a difficult situation due to the need to balance timely reperfusion therapy and maintaining strict infection control practices [13-14]. Telemedicine, which is used to deliver health care services using information or communication technology, provides an opportunity to carry out the evaluation, diagnosis, and even monitor the patients after discharge when social distancing is needed [15]. In this article, we reported our preliminary experience with the usefulness of telemedicine in managing STEMI patients during the COVID-19 pandemic. We also provided a review of this topic. [ABSTRACT FROM AUTHOR]

EhcoBUTLER is an Information and Communication Technology (ICT) solution funded by the European Union (H2020; ID: 643566) and intended especially for elderly people with mild cognitive impairment (MCI) to improve their health, independence and quality of life, particularly at the social level. The purpose of this study is to assess the acceptability of ehcoBUTLER based on a survey delivered to potential users and actors involved in their care, exploring their expectations and preferences, while anticipating the system's functional requirements. The survey was delivered online to 313 participants (11% end users, 25% informal caregivers, 48% formal caregivers and 16% administration/management staff) from eight countries. Participants rated the different functionalities of ehcoBUTLER positively, 86.1% perceiving it as an interesting and useful system. Likewise, they assessed it as a commercially attractive product (75.1%). End users expressed a stronger preference for the social module. Nevertheless, they would be ready to pay a low monthly price for ehcoBUTLER. Professionals would be willing to pay choosing its functionalities modularly, but they would also expect it to be funded by the National Health System, centres or businesses. The conclusion is that all participants found ehcoBUTLER interesting, useful and ergonomic. However, to effectively implement it, it is necessary to bridge the digital gap and address the issue of insufficient investment in products aimed at older adults with cognitive impairment. To supplement cognitive training systems with social, emotional or entertainment functionalities could improve adherence to their use. [ABSTRACT FROM AUTHOR]

One main challenge of health care systems in future will be the care and treatment of dementia patients. To their advanced age, dementia patients do not only have limitations in cognition, perception, and articulation, but also suffer from several other diseases (multimorbidity). These patients have sophisticated needs for assistance, care, and treatment relying on different health care sectors. Well-known communication and coordination deficits between sectors are intensified in the case of dementia, to the above described limitations. Coordination processes concerning the transition of patients from hospitals to inpatient or outpatient aftercare pose particularly difficult. To show a possible future direction for improving intersectoral care of dementia patients, we developed the web-based case management system CASEPLUS-SimPat as part of the project SimPat "Securing Integrated Care for Multi-morbid Patients with Dementia using an IT-based Service Concept". CASEPLUS-SimPat allows health care professionals, hospital employees and general practitioners, to jointly access patient data and exchange information about the treatment of patients. The case management system was implemented in a three-tier architecture with a role-based authorization concept. A portal for relatives complements the system. By providing precise information and e-learning services, caring relatives are integrated into the care process. In a pilot test, CASEPLUS-SimPat shows a good usability. Adapting aspects, such as keywords used to structure entries will make it easier to find required information, and thus will increase usability. Next steps comprise integration of further health care professionals and care facilities as well as real-life testing in terms of feasibility and effectiveness. [ABSTRACT FROM AUTHOR]

Computer assisted automatic smart pattern analysis of cancer affected pixel structure takes critical role in pre-interventional decision making for oral cancer treatment. Internet of Things (IoT) in healthcare systems is now emerging solution for modern e-healthcare system to provide high quality medical care. In this research work, we proposed a novel method which utilizes a modified vesselness measurement and a Deep Convolutional Neural Network (DCNN) to identify the oral cancer region structure in IoT based smart healthcare system. The robust vesselness filtering scheme handles noise while reserving small structures, while the CNN framework considerably improves classification accuracy by deblurring focused region of interest (ROI) through integrating with multi-dimensional information from feature vector selection step. The marked feature vector points are extracted from each connected component in the region and used as input for training the CNN. During classification, each connected part is individually analysed using the trained DCNN by considering the feature vector values that belong to its region. For a training of 1500 image dataset, an accuracy of 96.8% and sensitivity of 92% is obtained. Hence, the results of this work validate that the proposed algorithm is effective and accurate in terms of classification of oral cancer region in accurate decision making. The developed system can be used in IoT based diagnosis in health care systems, where accuracy and real time diagnosis are essential. [ABSTRACT FROM AUTHOR]

Queuing is one of the very important criteria for assessing the performance and efficiency of any service industry, including healthcare. Data Envelopment Analysis (DEA) is one of the most widely-used techniques for performance measurement in healthcare. However, no queue management application has been reported in the health-related DEA literature. Most of the studies regarding patient flow systems had the objective of improving an already existing Appointment System. The current study presents a novel application of DEA for assessing the queuing process at an Outpatients' department of a large public hospital in a developing country where appointment systems do not exist. The main aim of the current study is to demonstrate the usefulness of DEA modelling in the evaluation of a queue system. The patient flow pathway considered for this study consists of two stages; consultation with a doctor and pharmacy. The DEA results indicated that waiting times and other related queuing variables included need considerable minimisation at both stages. [ABSTRACT FROM AUTHOR]

We propose an integrated semantic web framework consisting of formal ontologies, web services, a reasoner and a rule engine that together recommend appropriate level of patient-care based on the defined semantic rules and guidelines. The classification of healthcare-associated infections within the HAIKU (Hospital Acquired Infections - Knowledge in Use) framework enables hospitals to consistently follow the standards along with their routine clinical practice and diagnosis coding to improve quality of care and patient safety. The HAI ontology (HAIO) groups over thousands of codes into a consistent hierarchy of concepts, along with relationships and axioms to capture knowledge on hospital-associated infections and complications with focus on the big four types, surgical site infections (SSIs), catheter-associated urinary tract infection (CAUTI); hospital-acquired pneumonia, and blood stream infection. By employing statistical inferencing in our study we use a set of heuristics to define the rule axioms to improve the SSI case detection. We also demonstrate how the occurrence of an SSI is identified using semantic e-triggers. The e-triggers will be used to improve our risk assessment of post-operative surgical site infections (SSIs) for patients undergoing certain type of surgeries (e.g., coronary artery bypass graft surgery (CABG)). [ABSTRACT FROM AUTHOR]

As cloud computing and wearable devices technologies mature, relevant services have grown more and more popular in recent years. The healthcare field is one of the popular services for this technology that adopts wearable devices to sense signals of negative physiological events, and to notify users. The development and implementation of long-term healthcare monitoring that can prevent or quickly respond to the occurrence of disease and accidents present an interesting challenge for computing power and energy limits. This study proposed an adaptive sensor data segments selection method for wearable health care services, and considered the sensing frequency of the various signals from human body, as well as the data transmission among the devices. The healthcare service regulates the sensing frequency of devices by considering the overall cloud computing environment and the sensing variations of wearable health care services. The experimental results show that the proposed service can effectively transmit the sensing data and prolong the overall lifetime of health care services. [ABSTRACT FROM AUTHOR]

Using of EMR in health services and organizations is steadily increasing for quality improvement, cost effectiveness and performance development. However, no validated national and international instruments (scale, questionnaire, index, and inventory) have assessed the effectiveness, satisfaction, health care savings, patient safety and cost minimization of electronic medical and health systems from the viewpoint and perceptions of nurses in Turkish health services. The perceptions of health care professionals especially physicians and nurses can contribute important information that may predict their acceptance of EMR and desired mode of use for EMR, evaluation performance of EMR thus guiding EMR implementation in hospitals. This article is a report of validation of the instrument to measure nurses' views on the use, quality and user satisfaction with EMR in Turkish health system. Items in the questionnaire were designed and obtained following O.G. Otieno, H. Toyama, M. Asonuma, M. Kanai-Pak, K. Naitoh's questionnaire about Use, Quality and User Satisfaction with EMR systems. Reliability and validity were examined and investigated in terms of responses from 487 nurses from one education hospital in Ankara, Turkey. This study was planned and conducted at a university hospital. The validation process was based on construct validity in this study. The response rate was 74.92 %. Cronbach's alphas of three factors (use, quality and satisfaction of EMR) ranged from 0.78 to 0.94. Goodness-of-fit indices from the confirmatory factor analysis showed a reasonable model fit. Results of confirmatory factor analysis showed that χ statistic indicated significant result ( p < 0.001) and model fit was acceptable according to relative χ statistic (χ/df = 2.8 < 5). Further validation of the instrument could yield positive results in health systems in the different countries. Also further validation and reliability studies could be planned on physicians and other health professionals. [ABSTRACT FROM AUTHOR]

The article presents a study that deals with systematic literature review of primary care analysis using data envelopment analysis applications. Topics include primary healthcare, health care delivery, the impact of methodological frameworks on results, and their influence on the information provided to decision makers.

An introduction is presented in which the editor discusses various reports within the issue on topics including big data platform for wearable data in health care, a proposed multiple level-based QoS design at the MAC layer and the detection of drug use by analyzing several parameters.