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The purpose of this paper is to discuss a GIS application created for health care planning at Jeddah city, Saudi Arabia. The application covers important health care facilities planning issues including defining accessibility to health care facilities, identifying and classifying the distribution of health demand at Jeddah city and modeling spatial variation of patient locations.

In order to build this application, a geo‐database is created that covers points, lines and polygon features such as health care facility location, road network and population districts. In addition, raster surface models are produced using Kriging function which produces raster surfaces for predicting health demand values at the study area.

The outputs of this application can be used to help health care planners in evaluating the existing location of health care facilities and see if these locations are concentrated at certain city districts. In addition, local health planners can use the created models when deciding where to allocate a new health care facility at Jeddah city.

This application is considered as a spatial decision support system for health planners in Jeddah city. It can be used to define and evaluate location of health centers as well as to identify the spatial accessibility to health centers.

The proposed article is aimed at exploring the opportunities, challenges and possible outcomes of incorporating big data analytics (BDA) into health-care sector. The purpose of this study is to find the research gaps in the literature and to investigate the scope of incorporating new strategies in the health-care sector for increasing the efficiency of the system.

Fora state-of-the-art literature review, a systematic literature review has been carried out to find out research gaps in the field of healthcare using big data (BD) applications. A detailed research methodology including material collection, descriptive analysis and categorization is utilized to carry out the literature review.

BD analysis is rapidly being adopted in health-care sector for utilizing precious information available in terms of BD. However, it puts forth certain challenges that need to be focused upon. The article identifies and explains the challenges thoroughly.

The proposed study will provide useful guidance to the health-care sector professionals for managing health-care system. It will help academicians and physicians for evaluating, improving and benchmarking the health-care strategies through BDA in the health-care sector. One of the limitations of the study is that it is based on literature review and more in-depth studies may be carried out for the generalization of results.

There are certain effective tools available in the market today that are currently being used by both small and large businesses and corporations. One of them is BD, which may be very useful for health-care sector. A comprehensive literature review is carried out for research papers published between 1974 and 2021.

Virtual and augmented reality solutions in medicine are generally applied in communication, training, simulation and therapy. However, like most new digital developments, these technologies face a large number of institutional barriers that are inherent to the medical sector.

Following Richard Scott's view on institutions and organizations, a multiple case study is used to analyze regulatory, normative and cultural-cognitive institutional pillars in the medical industry.

The results of the study demonstrate that (1) the regulatory pillar inhibits the advancement of new technologies in the approach to treatment, regulation of patient data, educational processes for medical staff, and information and financial flows; (2) the number of barriers increases based on the solution's level of disruption and the number of variable conventional procedures; (3) trust between participants in the medical industry plays an important role in introducing new technologies; (4) new participants need to address certain pillars depending on the area of application.

The authors discuss top-down and bottom-up approaches for overcoming institutional barriers when implementing augmented and virtual reality solutions for companies focusing on the medical market.

Industry 4.0, artificial intelligence and digitalization have got a momentum in health care. However, scholars and practitioners do not agree on their implications on health services' quality and effectiveness. The article aims at shedding light on the applications, aftermaths and drawbacks of industry 4.0 in health care, summarizing the state of the art.

A systematic literature review was undertaken. We arranged an ad hoc research design, which was tailored to the study purposes. Three citation databases were queried. We collected 1,194 scientific papers which were carefully considered for inclusion in this systematic literature review. After three rounds of analysis, 40 papers were taken into consideration.

Industry 4.0, artificial intelligence and digitalization are revolutionizing the design and the delivery of care. They are expected to enhance health services' quality and effectiveness, paving the way for more direct patient–provider relationships. In addition, they have been argued to allow a more appropriate use of available resources. There is a dark side of health care 4.0 involving both management and ethical issues.

Industry 4.0 in health care should not be conceived as a self-nourishing innovation; rather, it needs to be carefully steered at both the policy and management levels. On the one hand, comprehensive governance models are required to realize the full potential of health 4.0. On the other hand, the drawbacks of industry 4.0 should be timely recognized and thoroughly addressed.

The article contextualizes the state of the art of industry 4.0 in the health care context, providing some insights for further conceptual and empirical developments.

This paper aims to identify barriers toward the adoption of blockchain (BC) technology in Indian health-care industry and also examines the significant issues of BC applications in health-care industry.

The barriers of the study are identified by two phases including the review of literature and semistructured interviews with hospital staff and administration operating in India. The experts (N = 15) are being taken from top-level management, IT experts and patients from the hospitals. The study implemented integrated total interpretative structural modeling-FUZZY-Cross-impact matrix multiplication applied to classification (TISM-FUZZY-MICMAC) methods for identifying the interrelationship among the barriers.

A total of 15 barriers have been determined in the Indian health-care industry through discussion with the selected experts. TISM is applied to develop multilevel structure for BC barriers. Further, FUZZY-MICMAC has been used to compute driving and dependent barriers. The findings suggest that low awareness related to legal issues and low support from high level of management have maximum driving power.

The present study applies multicriterion approach to identify the limited barriers in BC adoption in health care. Future studies may develop the relationship and mark down the steps for implementation of BC in health-care setting of a developing economy. Empirical study can be conducted to verify the results along with selected case studies.

The present study identifies the BC adoption barriers in health-care industry. The study examines the pertinent issues in context to major support required, bottlenecks in adoption, key benefits of adoption planning and activities. The technology adoption practices are expected to provide applications such as distributed, secured medical and clinical data and patient centric systems that will enhance the efficiency of the health-care industry.

The study is among few primary studies that identify and analyze the BC adoption in health-care industry.

With the development of emerging engineering technology and industrialization, there are greater changes in the life style of people in smart urban cities; therefore, there is also more chance of various health problems in urban areas. The life style of persons in metro urban areas with the expansive volume of population is similarly influenced by different application and administration frameworks. These are affecting the human health system up to an extended extent and there are more health-related issues and health hazard concerns that can be identified in urban areas. The purpose of this paper is to present an analytical study on various aspects of the smart health care system in a smart perspective by analyzing them with respect to emerging engineering technologies such as mobile network, cloud computing, Internet of Things (IoT), big data analytics and ubiquitous computing. This paper also carries out a detailed survey of health issues and improved solutions in automated systems using these technologies. Second, the paper also presents a novel health care system using smart and safe ambulances and their appropriate control at traffic points with safety and security features in a smart city, so that the valuable life of patients can be saved in time by immediate treatment in nearest hospital or health care units.

In this paper, an analytical survey was conducted for improvement in the health care sector using computer technology and IoT-based various modern health care applications. An idea of Smart Health Care Hospital using sensors, mobile agent smart vehicle configuration and safety traffic control for ambulance was proposed.

A simulation was carried out to see the performance of a safety mechanism in the proposed approach. Comparative analysis was carried out with other approaches to know the execution time, response time and probable delay due to the implementation of this approach.

It is an original research work with motivation inspired from current emergent technology to apply in the health care system.

The purpose of this study was to investigate how Lean Six Sigma (LSS) may help mitigate the impact of COVID-19 within health care environments. The goals of this study were to understand the current knowledge of LSS and COVID-19 through a systematic review of the current literature, identify the gap in the current knowledge of LSS in COVID-19 mitigation within health care environments and define the principles of LSS, within organizational resilience that support a health care organization’s ability to mitigate the impact of COVID-19.

A narrative literature review was conducted to identify relevant research. A total of 21 subject matter experts (SMEs) meeting the inclusion criteria were approached through a guided interview process. Content analysis was conducted to describe how LSS principles contribute to supporting health care organizations operating in the era of COVID-19.

Study results report that personal safety is the primary subject, followed by supporting dimensions of process redesign, and telemedicine. LSS topics that directly relate to COVID-19 are in four thematic areas: tools, applications, benefits and challenges. Particular areas of application, techniques, challenges and benefits are identified and discussed that could be applied proactively and reactively, to organizational and supply chain resilience to recover from COVID-19.

There were a number of limitations to the generalizability of this work. The sample size was small and purposeful, thus, external validity of the study results are not determined. The SMEs in this study have not implemented the practices noted in the results at the time of the study, and knowledge of results is limited to the study aims.

This study of LSS principles and COVID-19 has implications for practitioners and offers specific guidance for areas of health care adoption of LSS techniques and tools that benefit patient safety, challenges for the user to be mindful of and potential benefits in resilience of operations in the era of COVID-19.

The purpose of this paper is to design a future value stream map of the system and patient pathway with lean health care application tools in terms of quality improvement (QI) in order to decrease the non-value-added activities for patients, doctors, and nurses for a radiology unit in a public hospital based in Istanbul, Turkey.

Data are collected from personal observations, information technologies unit, and authorized employees. In order to measure subprocesses, some qualitative data are transformed into quantitative data via authorized employees’ experiences. Moreover, data tracking and keeping systems are too poor for revealing the current situation.

The current value stream map of the system of the radiology department of a public education and research hospital was analyzed and the future value stream map of the system and patient pathway was redesigned with lean health care application tools in terms of QI.

The greatest limitation is the resistant to change and fear of talking on mistakes and low performances.

This paper states an example of a current and future value stream map showing step by step where the bottlenecks are and how these can be improved and what specific benefits will this analysis bring to the health care system. It will be useful for both academicians and practitioners on how to apply lean to health care.

Fog computing (FC) is a new field of research and has emerged as a complement to the cloud, which can mitigate the problems inherent to the cloud computing (CC) and internet of things (IoT) model such as unreliable latency, bandwidth constraints, security and mobility. Because there is no comprehensive study on the FC in health management processing systems techniques, this paper aims at surveying and analyzing the existing techniques systematically as well as offering some suggestions for upcoming works.

The paper complies with the methodological requirements of systematic literature reviews (SLR). The present paper investigates the newest systems and studies their practical techniques in detail. The applications of FC in health management systems have been categorized into three major groups, including review articles, data analysis, frameworks and models mechanisms.

The results have indicated that despite the popularity of FC as having real-time processing, low latency, dynamic configuration, scalability, low reaction time (less than a second), high bandwidth, battery life and network traffic, a few issues remain unanswered, such as security. The most recent research has focused on improvements in remote monitoring of the patients, such as less latency and rapid response. Also, the results have shown the application of qualitative methodology and case study in the use of FC in health management systems. While FC studies are growing in the clinical field, CC studies are decreasing.

This study aims to be comprehensive, but there are some limitations. This research has only surveyed the articles that are mined, according to a keyword exploration of FC health, FC health care, FC health big data and FC health management system. Fog-based applications in the health management system may not be published with determined keywords. Moreover, the publications written in non-English languages have been ignored. Some important research studies may be printed in a language other than English.

The results of this survey will be valuable for academicians, and these can provide visions into future research areas in this domain. This survey helps the hospitals and related industries to identify FC needs. Moreover, the disadvantages and advantages of the above systems have been studied, and their key issues have been emphasized to develop a more effective FC in health management processing mechanisms over IoT in the future.

Previous literature review studies in the field of SLR have used a simple literature review to find the tasks and challenges in the field. In this study, for the first time, the FC in health management processing systems is applied in a systematic review focused on the mediating role of the IoT and thereby provides a novel contribution. An SLR is conducted to find more specific answers to the proposed research questions. SLR helps to reduce implicit researcher bias. Through the adoption of broad search strategies, predefined search strings and uniform inclusion and exclusion criteria, SLR effectively forces researchers to search for studies beyond their subject areas and networks.

Due to its ability to support well-informed decision-making, business intelligence (BI) has grown in popularity among executives across a range of industries. However, given the volume of data collected in health-care organizations, there is a lack of exploration concerning its implementation. Consequently, this research paper aims to investigate the key factors affecting the acceptance and use of BI in healthcare organizations.

Leveraging the theoretical lens of the “unified theory of acceptance and use of technology” (UTAUT), a study framework was proposed and integrated with three context-related factors, including “rational decision-making culture” (RDC), “perceived threat to professional autonomy” (PTA) and “medical–legal risk” (MLR). The variables in the study framework were categorized as follows: information systems (IS) perspective; organizational perspective; and user perspective. In Jordan, 434 healthcare professionals participated in a cross-sectional online survey that was used to collect data.

The findings of the “structural equation modeling” revealed that professionals’ behavioral intentions toward using BI systems were significantly affected by performance expectancy, social influence, facilitating conditions, MLR, RDC and PTA. Also, an insignificant effect of PTA on PE was found based on the results of statistical analysis. These variables explained 68% of the variance (R²) in the individuals’ intentions to use BI-based health-care systems.

To promote the acceptance and use of BI technology in health-care settings, developers, designers, service providers and decision-makers will find this study to have a number of practical implications. Additionally, it will support the development of effective strategies and BI-based health-care systems based on these study results, attracting the interest of many users.

To the best of the author’s knowledge, this is one of the first studies that integrates the UTAUT model with three contextual factors (RDC, PTA and MLR) in addition to examining the suggested framework in a developing nation (Jordan). This study is one of the few in which the users’ acceptance behavior of BI systems was investigated in a health-care setting. More specifically, to the best of the author’s knowledge, this is the first study that reveals the critical antecedents of individuals’ intention to accept BI for health-care purposes in the Jordanian context.