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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]

Approximately 2% of children from birth through 2 years of age were identified as having disabilities in 2000. To provide effective intervention services to each child is important. Children with disabilities from birth to 3 years of age who receive early intervention services have an Individualized Family Service Plan (IFSP). In some community early intervention programs, IFSP data are collected and stored in paper-based format, making it difficult to retrieve information in an efficient manner. Information technology strategies could address these problems, thus enhancing the efficiency of the information management. This paper discusses a user requirement analysis, design and development of a Tablet-PC computer application for collecting IFSP data for young children in Milwaukee, Wisconsin. An early prototype for the Tablet-PC computer application for the IFSP was successfully developed and a preliminarily evaluation was conducted at two early intervention programs. [ABSTRACT FROM AUTHOR]

The administrative simplification provisions of the Health Insurance Portability and Accountability Act (HIPAA) of 1996 are considered to have a considerable impact on the health care industry in the United States of America. The HIPAA-mandated electronic transactions using Electronic Data Interchange (henceforth EDI) methods have brought significant issues on privacy, confidentiality and security for individually identifiable information not to mention technical issues. The purpose of this paper is to facilitate the understanding of those issues surrounding the administrative simplification part of HIPAA. Toward this goal, the paper provides an overview of HIPAA through a systematic approach of understanding its evolution and its requirements. [ABSTRACT FROM AUTHOR]

In this paper, we describe the planning, development, and overall impact of Electronic Medical Records (EMRs) on Dryden Family Medicine (DFM), a rural family practice in Dryden, New York. The EMR system was installed in July, 2003, and the full conversion to the new system was accomplished in three phases. Significant efficiency gains were achieved in billing, prescription ordering, and other routine processes. Protocols were developed to enhance quality management, preventive medicine, and disease management. Job responsibilities changed and expanded for all members of the practice and coding issues became paramount. Practice revenue increased by about 20 percent and average “case-mix” increased by 10 percent in the second year following the implementation of EMRs due to improved billing documentation. We focus on the implementation process, the lessons learned, the obstacles encountered, and the successes achieved by one rural family practice. [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]

This paper investigates the benefits of using less intrusive wireless technologies for heart monitoring. By replacing well established heart monitoring devices (i.e. Holter) with wireless ECG based Body Area Networks (BAN), improved healthcare performance can be achieved, reflected in (1) high quality ECG recordings during physical activities and (2) increased patient satisfaction. A small scale clinical trial was conducted to compare both technologies and the results illustrate that the wireless ECG monitor was able to detect ECG signals intended for arrhythmia diagnostics. Furthermore, from a patient’s perspective, both technologies were evaluated using three dimensions, namely; hygienic aspects, physical activity, and skin reactions. Results demonstrate that the wireless ECG BAN showed better performance, especially regarding the hygienic aspects. It was also favourable for use during physical activities, and the signal quality of the wireless sensor system demonstrated good performance regarding signal noise and artefact disturbances. This paper concludes that wireless cardiac monitoring systems have significant benefits from a patient’s perspective, and further clinical trials should be conducted to further evaluate the new ECG based BAN system, to identify the possibility of widespread adoption and utilisation of wireless technology for arrhythmia diagnostics. [ABSTRACT FROM AUTHOR]

Two-way satellite broadband communication technologies, such as the Digital Video Broadcasting with Return Channel via Satellite (DVB-RCS) technology, endeavour to offer attractive wide-area broadband connectivity for telemedicine applications, taking into consideration the available data rates, Quality of Service (QoS) provision, survivability, flexibility and operational costs, even in remote areas and isolated regions where the terrestrial technologies suffer. This paper describes a wide-area tele-medicine platform, specially suited for homecare services, based on the DVB-RCS and Wi-Fi communication technologies. The presented platform combines medical data acquisition and transfer, patient remote monitoring and teleconference services. Possible operational scenarios concerning this platform and experimental results regarding tele-monitoring, videoconference and medical data transfer are also provided and discussed in the paper. [ABSTRACT FROM AUTHOR]

This paper reviews the literature on the use of collaborative technologies by healthcare teams between 1980 and 2003. Multiple databases were searched with explicit inclusion criteria that yielded 17 conceptual and empirical papers. The discussions of these literatures centered on the individual, team, and technological dimensions of collaborative technology use within healthcare teams. Results show that collaborative healthcare technologies can have positive effects on team work processes at both the individual and group level. The limited number of research studies accentuates the need for additional research in this area. Future research should focus on defining team tasks; determining which type of groupware works for a particular health setting; and exploring the effects of groupware on patient care delivery and the organization. Without research in these areas, it will be difficult to harness the full advantages of using groupware technologies by collaborative healthcare teams. [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 healthcare industry is streamlining processes to offer more timely and effective services to all patients. Computerized software algorithm and smart devices can streamline the relation between users and doctors by providing more services inside the healthcare telemonitoring systems. This paper proposes a multi-sources framework to support advanced healthcare applications. The proposed framework named Multi Sources Healthcare Architecture (MSHA) considers multi-sources: sensors (ECG, SpO2 and Blood Pressure) and text-based inputs from wireless and pervasive devices of Wireless Body Area Network. The proposed framework is used to improve the healthcare scalability efficiency by enhancing the remote triaging and remote prioritization processes for the patients. The proposed framework is also used to provide intelligent services over telemonitoring healthcare services systems by using data fusion method and prioritization technique. As telemonitoring system consists of three tiers (Sensors/ sources, Base station and Server), the simulation of the MSHA algorithm in the base station is demonstrated in this paper. The achievement of a high level of accuracy in the prioritization and triaging patients remotely, is set to be our main goal. Meanwhile, the role of multi sources data fusion in the telemonitoring healthcare services systems has been demonstrated. In addition to that, we discuss how the proposed framework can be applied in a healthcare telemonitoring scenario. Simulation results, for different symptoms relate to different emergency levels of heart chronic diseases, demonstrate the superiority of our algorithm compared with conventional algorithms in terms of classify and prioritize the patients remotely. [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]

The clinical symptoms of metabolic disorders during neonatal period are often not apparent. If not treated early, irreversible damages such as mental retardation may occur, even death. Therefore, practicing newborn screening is essential, imperative to prevent neonatal from these damages. In the paper, we establish a newborn screening model that utilizes Support Vector Machines (SVM) techniques and enhancements to evaluate, interpret the Methylmalonic Acidemia (MMA) metabolic disorders. The model encompasses the Feature Selections, Grid Search, Cross Validations as well as multi model Voting Mechanism. In the model, the predicting accuracy, sensitivity and specificity of MMA can be improved dramatically. The model will be able to apply to other metabolic diseases as well. [ABSTRACT FROM AUTHOR]

In this paper, we established a newborn screening system under the HL7/Web Services frameworks. We rebuilt the NTUH Newborn Screening Laboratory’s original standalone architecture, having various heterogeneous systems operating individually, and restructured it into a Service-Oriented Architecture (SOA), distributed platform for further integrity and enhancements of sample collections, testing, diagnoses, evaluations, treatments or follow-up services, screening database management, as well as collaboration, communication among hospitals; decision supports and improving screening accuracy over the Taiwan neonatal systems are also addressed. In addition, the new system not only integrates the newborn screening procedures among phlebotomy clinics, referral hospitals, as well as the newborn screening center in Taiwan, but also introduces new models of screening procedures for the associated, medical practitioners. Furthermore, it reduces the burden of manual operations, especially the reporting services, those were heavily dependent upon previously. The new system can accelerate the whole procedures effectively and efficiently. It improves the accuracy and the reliability of the screening by ensuring the quality control during the processing as well. [ABSTRACT FROM AUTHOR]

OASIS is a non-for-profit consortium that drives the development convergence and adoption of open standards for the global information society. It involves more than 600 organizations and individuals as well as IT leaders Sun, Microsoft, IBM and Oracle. One of its standards is XACML which appeared a few years ago and now there are about 150,000 hits on Google. XACML (eXtensible Access Control Markup Language) is not technology related. Sun published in 2004 open source Sun XACML which is in compliance with XACML 1.0. specification and now works to make it comply with XACML 2.0. The heart of XACML are attributes values of defined type and name that is to be attached to a subject, a resource, an action and an environment in which a subject request action on resource. In that way XACML is to replace Role Based Access Control which dominated for years. The paper examines performances in CEN 13 606 and ISO 22 600 based healthcare system which uses XACML for access control. [ABSTRACT FROM AUTHOR]

In order to provide enhanced medication safety for inpatients, the medical mechanism which adopts the modified grouping proof protocol is proposed in this paper. By using the grouping proof protocol, the medical staffs could confirm the authentication and integrity of a group of Radio-Frequency Identification (RFID) tags which are embedded on inpatient bracelets and the containers of drugs. This mechanism is designed to be compatible with EPCglobal Class-1 Generation-2 standard which is the most popular specification of RFID tags. Due to the light-weight computational capacity of passive tags, only the pseudo-random number generator (PRNG) and cyclic redundancy code (CRC) are allowed to be used in the communication protocol. Furthermore, a practical scenario of using this proposed mechanism in hospital to examine the medication safety is also presented. [ABSTRACT FROM AUTHOR]

Since life is invaluable, the patient safety is always an important issue. How to reduce the malpractices and advance the patient safety is the primary goal of many countries. The current problem is that the hospitals cannot quickly and precisely identify the name of medicine, the position of patient and staff and the servicing time and dosage taken by patients. The application of Radio Frequency Identification (RFID) is rocketing in popularity as varieties of expanded uses. However, due to the investment consideration, there are few cases that practically implement such a technology in healthcare industries. This paper presents a Wisely Aware RFID Dosage (WARD) system, which based on an integration of barcodes and RFID tags, to demonstrate effective and safe patient care environment, for preventing the risk of medication error. Finally, through an evaluation of users’ satisfaction, a reliability of 0.92 and a criterion-related validity of 0.82 show that this system is able to effectively construct the patient-safety-centric environment. [ABSTRACT FROM AUTHOR]

In this paper we have discussed the application of artificial intelligence in telemedicine using mobile device. The main goal of our research is to develop methods and systems to collect, analyze, distribute and use medical diagnostics information from multiple knowledge sources and areas of expertise. Physicians may collect and analyze information obtained from experts worldwide with the help of a medical decision support system. In this information retrieval system, modern communication tools such as computers and mobile phones can be used efficiently. In this work we propose a medical decision support system using the general packet radio service (GPRS). GPRS, a data extension of the mobile telephony standard Global system for mobile communications (GSM) is emerging as the first true packet-switched architecture to allow mobile subscribers to benefit from high-speed transmission rates and run JAVA based applications from their mobile terminals. An academic prototype of a medical decision support system using mobile device was implemented. The results reveal that the system could find acceptance from the medical community and it could be an effective means of providing quality health care in developing countries. [ABSTRACT FROM AUTHOR]

Health information technology research has historically suffered from the persistence of paper-based systems as a barrier to research and refinement of information models. While the field of (non-medical) information science offers a potentially rich source of data, there exist relatively few theoretical links between medical and non-medical information models. This paper argues that the establishment of an integrated translational research pathway is not only useful, but is a critical and necessary step in the realization of a national health information infrastructure in the USA. [ABSTRACT FROM AUTHOR]

The adoption of new technology within healthcare has been promoted as a way to reduce costs and increase efficiencies as well as improve quality. The literature has documented a significant number of implementation failures by large groups and hospitals with access to IT skills and resources. Given the low adoption rate among physicians, the challenges facing small practices can be daunting. While financial and technical barriers have been explored at the physician level, the actual implementation challenges facing small groups have not been explored. This paper presents a qualitative three case analysis of physician groups that have employed EMRs and the pre and post adoption insights. Results show that planning was a key common variable missing; the anticipated downtime was longer than expected and the workflow disruption and maintenance costs were underestimated. [ABSTRACT FROM AUTHOR]

Although information technology (IT)-based applications in healthcare have existed for more than three decades, methods to evaluate outputs and outcomes of the use of IT-based systems in medical informatics is still a challenge for decision makers, as well as to those who want to measure the effects of ICT in healthcare settings. The aim of this paper is to review published articles in the area evaluations of IT-based systems in order to gain knowledge about methodologies used and findings obtained from the evaluation of IT-based systems applied in healthcare settings. The literature review includes studies of IT-based systems between 2003 and 2005. The findings show that economic and organizational aspects dominate evaluation studies in this area. However, the results focus mostly on positive outputs such as user satisfaction, financial benefits and improved organizational work. This review shows that there is no standard framework for evaluation effects and outputs of implementation and use of IT in the healthcare setting and that until today no studies explore the impact of IT on the healthcare system’ productivity and effectiveness. [ABSTRACT FROM AUTHOR]

This paper is an attempt to examine the effects of antenatal care (ANC) utilization on birthweight. The analysis is based on the data collected from ever-married women by Turkey Demographic and Health Survey 1998 (TDHS). Cost-effectiveness based health sector reform requires careful estimation of costs and productivity of health interventions as well as the substitution possibility among health inputs. If the parameters of production function are known, policy makers can estimate the health outcome effects of various input-mix. Two-stage least squares (TSLS) and ordinary least squares (OLS) estimation procedures are used in this paper to estimate the effect of health care input, namely antenatal care visits, on birthweight. Since the utilization of medical care itself may be dependent on women's expectation about the pregnancy outcome, the parameters estimated through OLS may underestimate the true productivity of the input. The estimated functions indicate that antenatal care, woman's health status, and birth order are significant determinants in birthweight. The TSLS estimate of marginal productivity of ANC visits was about four times the marginal productivity estimate in the OLS model. The sensitivity of the parameter estimates to model specifications and empirical procedures followed demonstrates the importance of selecting the right model from both theoretical and empirical point of view. [ABSTRACT FROM AUTHOR]

The volume of patient monitoring video acquired in hospitals is very huge and hence there is a need for better compression of the same for effective storage and transmission. This paper presents a new motion segmentation technique, which improves the compression of patient monitoring video. The proposed motion segmentation technique makes use of a binary mask, which is obtained by thresholding the standard deviation values of the pixels along the temporal axis. Two compression methods, which make use of the proposed motion segmentation technique, are presented. The first method uses MPEG-4 coder and 9/7-biorthogonal wavelet for compressing the moving and stationary portions of the video respectively. The second method uses 5/3-biorthogonal wavelet for compressing both the moving and the stationary portions of the video. The performances of these compression algorithms are evaluated in terms of PSNR and bitrate. From the experimental results, it is found that the proposed motion technique improves the performance of the MPEG-4 coder. Among the two compression methods presented, the MPEG-4 based method performs better for bitrates less than 767 Kbps whereas for bitrates above 767 Kbps the performance of the wavelet based method is found superior. [ABSTRACT FROM AUTHOR]

In the current paper, we describe the challenges in measuring return on investment (ROI) and review published ROI studies on health IT. In addition, given the absence of a robust ROI literature base, we review the general benefits and potential costs of various health IT applications including electronic health records (EHRs), computerized physicians order entry (CPOE) systems, and clinical decision support systems (CDSS). We conclude that articles examining these benefits are much more common than studies examining ROI itself. This trend suggests the early stage of this knowledge base. Additional research utilizing broader perspectives and multidisciplinary techniques will be needed before a better understanding of ROI from health IT is achieved. [ABSTRACT FROM AUTHOR]

Recent developments in networking and computing technologies and the expansion of the electronic health record system have enabled the possibility of online collaboration between geographically distributed medical personnel. In this context, the paper presents a Web-based application, which implements a collaborative working environment for physicians by enabling the peer-to-peer exchange of electronic health records. The paper treats technological issues such as Video, Audio and Message Communication, Workspace Management, Distributed Medical Data Management and exchange, while it emphasizes on the Security issues arisen, due to the sensitive and private nature of the medical information. In the paper, we present initial results from the system in practice and measurements regarding transmission times and bandwidth requirements. A wavelet based image compression scheme is also introduced for reducing network delays. A number of physicians were asked to use the platform for testing purposes and for measuring user acceptance. The system was considered by them to be very useful, as they found that the platform simulated very well the personal contact between them and their colleagues during medical meetings. [ABSTRACT FROM AUTHOR]

This paper discusses the challenges associated with privacy in health care in the electronic information age based on the Health Insurance Portability and Accountability Act (HIPAA) and the Security Rules. We examine the storing and transmission of sensitive patient data in the modern health care system and discuss current security practices that health care providers institute to comply with HIPAA Security Rule regulations. Based on our research results, we address current outstanding issues that act as impediments to the successful implementation of security measures and conclude the discussion and offer possible avenues of future research. [ABSTRACT FROM AUTHOR]

Clinical Paths (Paths) have been introduced by different hospitals for patient care management. An Electronic Clinical Path (ECP) with onsite access provision seems to improve the efficiency of medical staffs because they can share vast medical information about patients at a time and also can reuse accumulated data easily, which is impossible with paper-based Path. Data model is the basis for implementing ECP. However, there is no established model for ECP. The purpose of this study is to introduce a model for ECP and implement an ECP with onsite access system. We introduced a Semistructured Data Model (SSDM) for ECP, and implemented a Web application system based on this model using Personal Digital Assistant (PDA) as inputting device. Our system functioned as expected with wireless LAN, and users handled the data on bedside using PDA. By introducing SSDM, we showed the correspondence between schema of Paths and implementation of ECP. [ABSTRACT FROM AUTHOR]

Presently, software architects face the challenge of integrating and linking different application software packages built on different computer platforms. Often they inherit various systems, ranging from mainframe to PDA-based applications. The first stage before programming different interfaces is to identify the means of communications most suitable for particular systems. The experience of the Mount Sinai Medical Center in New York, which combines a hospital, a healthcare network, a research center and a medical school, illustrates common problems faced by a number of medical institutions. This paper will discuss the various options, including the Internet, software architects have, and how they can use them during the development of an infrastructure for health care systems. [ABSTRACT FROM AUTHOR]

Policy makers continue to debate whether or not to increase the share of health care expenditures in United Kingdom. On the other hand, the pressure of operating within tight budgets and the advances in technology are forcing more locally based hospitals to close. One that could be used by policy makers as a benchmark is the measure of relative performance of hospitals. Many researchers have examined the source of inefficiency in health sectors (see, for example, Harris et al.Oper. Res. Soc. 57: 801–811, 2000, Ozcan et al., Med. Case 30:781–784, 1992; Ozcan et al., J. Med. Syst. 20(3)141–150, 1996; and Grosskopf and Valdmanis, J. Health. Econ. 6:89–107, 1987 but there is no evidence of measuring performance of neonatal care units of Scottish hospitals in the DEA literature. The purpose of this paper is to measure both technical and scale efficiency using data envelopment analysis in a selection of 22 neonatal care units in Scotland. The analysis suggests that major inefficiency likely exists in health care production in United Kingdom. There is potential for improving productivity by 20%. [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]

An introduction to the journal is presented in which the editors discuss reports within the issue about Body Area Network (BAN), an autonomous sensor network.

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]

mWard is a project whose purpose is to enhance existing clinical and administrative decision support and to consider mobile computers, connected via wireless network, for bringing clinical information to the point of care. The mWard project allowed a limited number of users to test and evaluate a selected range of mobile-wireless infrastructure and mobile health care computing devices at the neuroscience ward at Southern Health’s Monash Medical Centre, Victoria, Australia. Before the project commenced, the ward had two PC’s which were used as terminals by all ward-based staff and numerous multi-disciplinary staff who visited the ward each day. The first stage of the research, outlined in this paper, evaluates a selected range of mobile-wireless infrastructure. [ABSTRACT FROM AUTHOR]

Emergency medical services (EMS) play a crucial role in the overall quality and performance of health services. The performance of these systems heavily depends on operational success of emergency services in which emergency vehicles, medical personnel and supporting equipment and facilities are the main resources. Optimally locating and sizing of such services is an important task to enhance the responsiveness and the utilization of limited resources. In this study, an integer optimization model is presented to decide locations and types of service stations, regions covered by these stations under service constraints in order to minimize the total cost of the overall system. The model can produce optimal solutions within a reasonable time for large cities having up to 130 districts or regions. This model is tested for the EMS system of Adana metropolitan area in Turkey. Case study and computational findings of the model are discussed in detail in the paper. [ABSTRACT FROM AUTHOR]