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This paper aims to outline the results of a study of the potential use of sensor technology such as radio frequency identification (RFID) and/or ZigBee technology in providing real-time tracking and tracing of patients and equipment in hospitals. The government of Saudi Arabia has given high priority to providing the best practice in patients’ care. However, the growing requirement of the healthcare industry to obtain real-time information and data from various applications that can improve the performance and accuracy of management systems has not been addressed seriously in Saudi Arabia.

ZigBee and RFID are both emerging technologies and have become important topics in recent years. RFID technology is a non-contact identification technology that does not require direct eyesight to the target object. It is cheap and reliable, but its coverage zone is limited. ZigBee is another communication technology, which has a larger coverage and can also be used as an automatic identification technology with the benefits of lower power consumption.

This paper proposes a smart hospital management system that can be used to detect, locate and monitor patients and track assets and equipment using modern sensor technologies in a real-time environment for e-health systems in Saudi Arabia.

A novel management information system/knowledge management system framework based on sensor technologies for supporting and speeding up development of healthcare systems.

This paper aims to integrate Big Data in e-government in Oman, also known as “e-Oman”, wherein Big Data might be better harnessed to tackle real-time challenges.

Besides a description of the concepts of e-government and Big Data in general, the paper underscores the dimensions of “e-Oman”. Following a qualitative approach, the paper asserts how integration of Big Data in “e-Oman” may be useful by invoking examples from four short case studies across different sectors.

The paper supports the integration of “e-Oman” and Big Data wherein besides providing smooth public services, the government is encouraged to forge inter- and intra-ministerial collaboration and public-private partnership. The paper probes through the challenges and opportunities in effecting this integration.

The paper provides a platform for the policymakers to conceive of a synchronized programme for integrating “e-Oman” and the Big Data generated by it. This integration would go a long way in building upon the economy of Oman, besides providing better public services to the individuals and businesses on a real-time basis.

The paper does throw light on the issues of privacy and confidentiality of data available with the government. There are challenges of cybercrime as well. Therefore, the paper posits that a robust fool-proof infrastructure should be instituted by the government for effecting integration of e-government and Big Data.

This paper seeks to fill the gap in extant literature which remains scant on the integration of e-government with Big Data. This is especially true in the case of Oman where not a single study has been presented to probe this issue. Given that “e-Oman” is expanding its scope over the years, this paper foresees the concomitant opportunities and challenges in the integration of Big Data in “e-Oman”.

Assess the realistic impacts of implementing an Radio Frequency Identification (RFID)/Internet of Things (IoT) uniforms’ distribution system for managing medical personnel’s scrubs in operating rooms. The authors use a hybrid simulation framework to address the following objectives and challenges: a) reduce and control operating rooms’ level of inventory; b) stabilize scrubs’ demand and c) improve infection control and prevention of cross-contamination (through scrubs over manipulation and hoarding).

The authors adopt a Design Science approach. This methodological approach is used to design, develop, create and evaluate information technology “artifacts” (e.g. constructs, models, methods and instantiations) intended to solve organizational problems and make research contributions (Peffers et al., 2007). More specifically, the authors follow the Design Science Research Methodology process model which includes six steps: problem identification and motivation, definition of the objectives for a solution, design and development, demonstration, evaluation, and communication.

To assess the realistic impacts of implementing an RFID-IoT uniforms’ distribution system for managing medical personnel’s scrubs in operating rooms, the authors adopted a design science approach and initiated the research by documenting the business case and reviewed the existing literature to build a comparative analysis of existing uniforms’ distribution systems. The authors used a hybrid simulation model to assess the impact of three business cases: present mode of operation, implementing smart shelves or the smart distributors. The authors show that smart dispensers allow a greater control on scrubs’ utilization while eliminating the cross-contamination of the medical personnel.

Through this research study, the authors provide hospitals’ managers a scientific support for uniforms’ (scrubs) distribution process improvement. The authors use a hybrid simulation model to compare innovative solutions for uniforms’ distribution systems in the form of “smart cabinets” supported by Radio Frequency Identification (RFID)/Internet of Things (IoT) technologies and choose the most appropriate design for the hospital to meet two main challenges: a) inefficiency of uniform replenishment-distribution system and b) noncompliancy with infection control regulations caused by the distribution system.

From a methodological perspective, this paper addresses concerns from researchers calling quantitative research methods and using case-based research strategy to address IoT issues and assess the system in practice. From a broader point of view, this work confirms the predominant interest of RFID-IoT research work in the arena of supply chain management and logistics as the technology is used for tracking purpose and for monitoring applications. It is also one response to the research community suggesting that “hospitals should evaluate the medical effectiveness of the new technologies as well as the cost before adoption”.

Expert knowledge is an important organisational resource, and organisations need to retain the knowledge learned by experience, which can be shared as part of inter-professional learning. In a healthcare context, radio-frequency identification (RFID) and ZigBee technologies can be used together, to provide real-time information for decision support and to create a secure and reliable smart hospital management information system (SHMIS) that allows the dynamic control of objects and transforms operational processes, while minimising any potential risks to patients and staff. Currently, the RFID technology in Saudi Arabia is being solely used for the monitoring of newborn infants, and some difficulties have been encountered during the different stages of tagging and monitoring. The current system in Medina Maternity and Children’s Hospital (MMCH) uses battery-powered active RFID tags, which are expensive and require routine maintenance. This study aims to discuss the way in which the MMCH in Medina, Saudi Arabia, could be transformed into an SHMIS.

The extraordinary growth of RFID and ZigBee technologies has made it possible to identify, locate and track objects in various environments in real time. The RFID technology is a non-contact identification technology that is cheap and reliable but has limited range in the case of passive tags. ZigBee has greater range and lower power consumption, giving more precise location of the object’s movements (0.6 m). Passive RFID when combined with ZigBee technology can be used to improve services provided by healthcare organisations through continuous data collection and supporting real-time decision-making, by applying expert knowledge of domain experts to data produced by communication from electronic and sensor technologies.

A prototype object-tracking system using RFID and ZigBee was developed to support the knowledge transformation for knowledge reasoning for decision support (KRDS), and the outcome of this research was validated with domain experts in hospitals in Saudi Arabia. Two feasibility case studies were conducted at MMCH in Saudi Arabia, to evaluate the proposed system. A survey was also conducted to address the requirements at MMCH, and the researcher adopted a range of strategy techniques, including interviews and meetings with staff, and the setting up of communities of practise (CoPs) at the target hospital.

This paper has investigated the transformation process of an automatic healthcare tracking and monitoring systems for the purpose of developing a smart system in Saudi hospitals. For the scope of the project, the prototype implementation was restricted to a laboratory environment, to demonstrate the proposed proof of concept. The next phase will include conducting a scale up of the system, with implementation and testing done in a real hospital environment.

This paper proposes a prototype application of an (SHMIS that allows the dynamic control of objects and transforms operational processes, while minimising any potential risks to patients and staff.

Blood cold chain (BCC) represents a system for preserving the blood during its journey from the donor to the ultimate transfusion site. Existing BCCs have many drawbacks related to information transparency and information security. Secured and real-time information sharing in BCC can bring several benefits. The purpose of this paper is to summarise the issues in typical BCCs and to explore the scope of blockchain in the management of BCCs.

Issues in the existing BCCs are identified through a narrative review. To explain the potential of blockchain in mitigating these issues, a blockchain-based traceability solution is demonstrated with respect to a particular BCC scenario. The BCC management system discussed in this study makes use of the Ethereum blockchain’s smart contract feature and internet of things (IoT) technology. The smart contract is written in the solidity programming language and tested and validated using the Remix integrated development environment.

BCCs are concerned with several issues both from technical and non-technical perspectives. Blockchain technology is capable of troubleshooting the issues in the existing BCCs. Combining blockchain and IoT technology enables real-time information sharing among the entities. The demonstration presented in this work depicts how the blockchain-based smart contract can support operations in a typical BCC.

This paper explores the scope of blockchain in BCCs through a demonstration. To get insights into its technical and economical feasibilities, further investigations are needed.

Blockchain-based traceability system presented in this work can be adopted in BCCs to ensure the quality of blood or blood products. Blockchain-based smart contracts can aid the BCCs to achieve a proper balance between blood shortage and outdating.

The fourth industrial revolution and digital transformation have caused paradigm changes in the procedures of goods production and services through disruptive technologies, and they have formed new methods for business models. Health and medicine fields have been under the effect of these technology advancements. The concept of smart hospital is formed according to these technological transformations. The aim of this research, other than explanation of smart hospital components, is to present a model for evaluating a hospital readiness for becoming a smart hospital.

This research is an applied one, and has been carried out in three phases and according to design science research. Based on the previous studies, in the first phase, the components and technologies effecting a smart hospital are recognized. In the second phase, the extracted components are prioritized using type-2 fuzzy analytic hierarchical process based on the opinion of experts; later, the readiness model is designed. In the third phase, the presented model would be tested in a hospital.

The research results showed that the technologies of internet of things, robotics, artificial intelligence, radio-frequency identification as well as augmented and virtual reality had the most prominence in a smart hospital.

The innovation and originality of the forthcoming research is to explain the concept of smart hospital, to rank its components and to provide a model for evaluating the readiness of smart hospital. Contribution of this research in terms of theory explains the concept of smart hospital and in terms of application presents a model for assessing the readiness of smart hospitals.

Abundant studies of outpatient visits apply traditional recurrent neural network (RNN) approaches; more recent methods, such as the deep long short-term memory (DLSTM) model, have yet to be implemented in efforts to forecast key hospital data. Therefore, the current study aims to reports on an application of the DLSTM model to forecast multiple streams of healthcare data.

As the most advanced machine learning (ML) method, static and dynamic DLSTM models aim to forecast time-series data, such as daily patient visits. With a comparative analysis conducted in a high-level, urban Chinese hospital, this study tests the proposed DLSTM model against several widely used time-series analyses as reference models.

The empirical results show that the static DLSTM approach outperforms seasonal autoregressive integrated moving averages (SARIMA), single and multiple RNN, deep gated recurrent units (DGRU), traditional long short-term memory (LSTM) and dynamic DLSTM, with smaller mean absolute, root mean square, mean absolute percentage and root mean square percentage errors (RMSPE). In particular, static DLSTM outperforms all other models for predicting daily patient visits, the number of daily medical examinations and prescriptions.

With these results, hospitals can achieve more precise predictions of outpatient visits, medical examinations and prescriptions, which can inform hospitals' construction plans and increase the efficiency with which the hospitals manage relevant information.

To address a persistent gap in smart hospital and ML literature, this study offers evidence of the best forecasting models with a comparative analysis. The study extends predictive methods for forecasting patient visits, medical examinations and prescriptions and advances insights into smart hospitals by testing a state-of-the-art, deep learning neural network method.

This paper proposes the adoption of Industry 4.0 (I4) technologies and lean techniques for improving operational performance in the healthcare sector.

The research adopted a systematic literature review and feedback of healthcare professionals to identify the inefficiencies in the current healthcare system. A questionnaire was used to get feedback from the patients and the hospital staff about the current practices and issues, and the expected impact of technology on existing practices. Data were analysed using descriptive statistics, correlation analysis and multiple regression analysis.

The results indicate that I4 technologies lead to the improvement of the operational performance, and the perceptions about I4 technologies are made through the pre-medical diagnosis. However, a weak correlation between lean practices and healthcare operational performance compared to that of I4 technologies and operational performance indicate that lean practices are not fully implemented in the Sri Lankan healthcare sector to their full potential.

This study is limited to two government hospitals, with insights from only the doctors and nurses in Sri Lanka. Furthermore, the study is limited to only selected aspects of I4 technologies (big data, cloud computing and IoT) and lean concepts (value stream mapping and 5S). Therefore, recommendations on the adoption of I4 technologies in the healthcare sector need to be made within the scope of the study investigation.

The implementation of I4 technologies needs careful consideration of process improvement as part of the overall plan for achieving the maximum benefits of technology adoption.

The findings of the research can be used as a benchmark/guide for other hospitals to explore the adoption of I4 technologies, and how process improvement from lean concepts could influence the overall operational performance.

Facilities management (FM) activities affect citizens’ health and safety, long-term urban environmental sustainability and the relationships between the built and natural environments of cities. This study aims to comprehend the status of smart FM tools application, how they are used to improve the delivery of FM services and the barriers to their implementation.

To investigate the current smart solutions that leverage the quality of FM service, a case study based on the FM practice in Hong Kong was carried out. The case study was conducted in two phases of data acquisition based on a qualitative research methodology. After conducting in-depth interviews to determine the application of smart FM tools in different types of properties and to identify the initiatives and barriers to smart FM tool application, three workshops were conducted to validate the findings and further investigate the influence of FM professionals on smart FM tools application in Hong Kong.

The findings of the case study revealed, firstly, that four types of smart FM tools – user-centric, safety and hygiene, maintenance and sustainability-oriented – are used to assist the delivery of FM services. Secondly, smart FM tools are shown to be useful in assisting FM activities. Thirdly, the existing barriers to smart FM tool application manifest differently in shopping malls, office buildings and residential buildings. Fourthly, smart FM tools are used to address the four attributes of user needs: comfort, health and wellbeing, convenience and information to occupants.

The value of this study lies in its focus on the industry level (FM industry) and the application process of smart FM tools in different types of property, revealing the benefits, initiatives and barriers to their future application. This study provides a comprehensive picture of the current status and elaborates the barriers to smart FM tool application, which will help FM practitioners to make strategic decisions on selecting and developing smart FM tools. Also, this study will facilitate smart FM tool application policy development.