Summary
A project is an endeavor that focuses on accomplishing planned objectives that can be grouped in terms of benefits or outputs. It is termed success if it has attained the goals within the budget ad set timescale. Project management utilizes the skills, knowledge, techniques, and tools that aid in achieving the project requirements. It requires planning the organization’s resources to enable task completion. Project managers use PERT charts and Gantt charts to enhance the workflow through visual representation (Kim, 2018). The 4Ps are significant in enhancing a successful project, and they include people, products, processes, and projects (Collins, 2017). The project management approach has various advantages. This includes handling risky, costly, and complex assignments, managing projects in a specified period, and providing task orientation to the organization’s personnel.
Project management has been in operation for thousands of years and is dated back to Egyptian times. Around the 1950s, most organizations began using project management tools and other techniques to handle complex projects. Modern project management tools originate from the United States, which was involved with the U.S. Navy. They focused on managing the Polaris Missile project, whose contracts comprised manufacturing parts, research, and development. The project uncertainty was very high as the cost and the required time could not be estimated. This made the project depend on probabilities and the optimistic nature of the project managers. The time estimates were done using the program evaluation review technique (PERT), which did not evaluate the project’s cost.
E.I du Pont de Nemours Company was also used to estimate the time and cost as the company was involved in constructing enormous chemical plants in the U.S. The company provided reasonable estimates because of its significance in construction, which requires accurate time and cost estimates. This company developed the methodology of project planning and scheduling (PPS) (Gillett & Tennent, 2020). This methodology was later designed for the critical path method (CPM), which was very popular in the construction sector. Between the 1960s and 1970s, the CPM and PERT were very popular in both the private and the public sectors.
Various groups use these features, and some of the top users include the defense department in several countries, NASA, and other engineering companies in handling mega projects. These expanded with the development of computer systems (Gillett & Tennent, 2020). By1990s, many organizations embraced project management techniques to manage their projects with minimum risk. E-health systems are computer systems used in the healthcare environment to facilitate healthcare services and improvement. It involves the use of the internet, mobile device, and computers. It is used hand in hand with the non-digital approaches that direct the consumers’ wellness. The Semantic Web is the technology that enhances the sharing and management of medical information and the semantic interaction of healthcare information systems. The project focuses on the implementation of e-Health systems and semantic web to improve the daily performance of people’s health.
Literature Review
Semantic Web
Semantic Web (S.W.) was brought forth to be the future of the Web, where information can be processed by machines and humans. The main agenda is to enhance the interaction between humans and machines, making it easy to work together. The semantic web concept promotes the Web’s development and dispersion in terms of information (Karami & Rahimi, 2019). It forms the universal intelligent space machine where the knowledge bases are grouped to process information meant for humans rather than machines. The pioneers of the Semantic Web believed that it was crucial in marinating the quality of the Web and promoting the search of websites by researchers (Karami et al., 2017). One of the critical challenges in the healthcare sector is the extraction of data from heterogeneous data and knowledge sources. The Semantic Web is crucial in enhancing care quality by using data silos. This is because decision-making in healthcare is a process that depends on collaboration through the sharing of information, thereby helping the physicians and the medical staff gather the required information.
It should be noted that appropriate knowledge sharing requires three levels of communication, which include the syntax level, the semantic level, and the pragmatic level. Health level 7 (H.L. 7) presented the interoperability frameworks such as process, semantic, and technical. Technical interoperability is the transfer of data from one system to another, i.e., from system A to system B, without knowing what is being exchanged and counterbalancing the distance effect (Karami & Rahimi, 2019). Semantic interoperability provides meaning to the exchanged data such that the two systems understand data without ambiguity. The process is enhanced by the use of identifiers and codes. The process interoperability ensures coordination between the two systems making a common understanding of the data shared.
Significance of Semantic Web
The interoperability between healthcare information systems is a significant challenge, according to Abdalla and Mishra (2018). Interoperability is crucial as it facilitates the sharing of knowledge in various environments that are complicated. Previously, paper-based medical records were vital as they held necessary information without connection. The healthcare organizations had significant information but with no connection. However, through computerization, the data is informed of structured, unstructured, and visual data (Karami et al., 2017). This makes the interoperability between the structures complex hence the need for other mediums that will aid in exchanging information. The Semantic Web uses ontology to create common communication and interoperability standards.
The exchange standards facilitated by the Semantic Web are significant as they ensure the health information exchange (HIE). The health exchange standards include FHIR, LOINC, CDA, UMLS, HL7, and SNOMED. The standards have evolved from time to time to manage the shortcomings presented by the previous standards. The Fast Healthcare Interoperable Resources (FHIR) is built from the HL7 standards, making implementation easy. FHIR facilitates interoperability among the healthcare systems making it easy for the medical personnel to provide healthcare information on multiple devices such as tablets, smartphones, and computers. Furthermore, it enables the third party to provide medical applications that can easily be integrated into the current systems.
The semantic Web is primarily used in the healthcare structure for interoperability and information sharing. The resource description framework (RDF) and the semantic databases (Triple stores) are used in the execution of the European patient diagram structure (Dawood & Sah, 2021). The patient summary system unifies the union’s countries with objectives such as patient information sharing with security enhancements and protections. In the U.S., the Semantic Web has been applied to the Center for Disease Control (CDC), which has enhanced the Public Health Information Network PHIN for Healthcare frameworks (Karami, 2018). The Semantic Web is also utilized in health business forms. This has proven useful, enhancing performance in various areas, such as the determination procedure. It was helpful as it aided in identifying the Miral Valve Prolapse sickness. Furthermore, Semantic Web has also promoted the development of the model system that developed the clinical data information for the conclusion of coronary disease. Additionally, it is used to implement clinical methods.
Ontology in Healthcare
The use of an ontology through the Semantic Web focuses on the organization and representation of medical terminologies. Various medical engineers specializing in computer systems have developed a computer language that makes it easy for the systems to share and communicate patient information and general medical data (Dawood & Sah, 2021). The terminologies in this area are made easy for humans to understand. The medical frameworks are dependent on ontology as it is crucial for definite medical concepts. Ontology has various advantages, including fabricating grounded frameworks that promote data collaboration in healthcare (Dawood & Sah, 2021). This promotes data trading between medical systems, enhancing the performance and quality of the care provided. It fosters the reusing of the patients’ data through data transmission and transfer. Furthermore, it is necessary for data coordination, thereby bringing together healthcare frameworks.
Semantically Enhanced Patient and Clinical Information
For the healthcare organization to enhance the performance of the patients through the provision of biomedical items, it is imperative that information is shared between health organizations. The target of information sharing is not based on sharing heterogeneous data and the joining of health organizations but on improving e-health systems based on gauges and gadgets (Dawood & Sah, 2021). This information is vital in creating clinical devices that will provide a massive volume of data for clinical records. This enhances the development of biomedical items.
Karami and Rahimi (2019) argue that the semantic Web arranges the patients’ data into a structure stored in scattered domains. The healthcare data, information, and application systems are distributed in various spaces, areas, branches, and offices. With vast applications, information sharing is done through appointed habits. The information dissemination, sharing, and corresponding do not follow a specified rule. The use of specified rules, such as the HL7 has not been implemented in most healthcare systems (Dawood & Sah, 2021). This makes it hard to share information with systems, not in the space. The semantic Web provides versatile information that is increasingly extensive. It is conveyed via structures that utilize standards that connect the URL or the URI, similar to the webspace. There is a security challenge even with the HL7 protocols when transferring data using the Semantic Web structure. This is due to the escape clauses associated with the HL7, making information accessible by various devices and gadgets.
Semantic for Healthcare Data Acquisition
According to Taouli et al. (2018) and Lyko et al. (2016), data acquisition is extracting and gathering data before it can be stored for later analysis. Big data acquisition is usually controlled by three processes: volume, velocity, and variety. Good data analysis is majorly dependent on the quality of the information stored. The Semantic Web is essential as it is helpful in the extraction of important data. This process enables the identification and exclusion of unnecessary information, which may contain errors and other irregularities before storage in the repository (Taouli et al., 2018).
In healthcare data acquisition, the semantic technique used is mainly based on the ontologies that follow the three steps. The first step is converting the source data to RDF, the second step is the usage of ontologies to apply the set standards to the data, and the last step is loading the data processed to the final repositories. Ding et al. (2010) designed a Semantic platform that provided patients with their medical data, including physicians, prescriptions, diseases, and lab results. The portal enabled the users to search, disclose, and visualize the semantic data efficiently and reliably. Furthermore, the portal allowed data transfer from one format to another, i.e., relational to RDF format (Zenuni et al., 2015). The Ambient Assisted Living (AAL) system is significant in enhancing the quality of older people and the disabled. Forkan et al. (2014) proposed CoCaMAAL, a cloud-based solution to the AAL system problem of data processing and gathering. Forkan et al. (2014) utilized the Semantic Web ontology to handle the AAL by providing a unified virtual community involving computer servers, devices, and patients. The model used a service-oriented architecture (SOA) which was attained by integrating and processing collected sensor data using a context management system (CMS).
Jiang et al. (2016) argued that using a context-awareness wearable sensor system is helpful in handling the enormous data volumes that are generated from the monitoring devices that are used by the elderly. This will enable alerts to reach the required people and transfer valuable information through a big data solution. Tilahun et al. (2014) developed a Semantic Web by the name of Linked Open Data (LOD) to connect and publish heterogeneous health data. The data are stored in the RDF graphs, which use silk for connection, an open-source framework that integrates different data sources. Ullah et al. (2017) came up with Semantic Interoperability Model for Big-Information in IoT (SIMB-IoT), whose purpose is to perform the interoperability between various information system data. Ullah et al. (2017) utilized annotations, data storage in an RDF format, and SPARQL to perform querying services for data retrieval in the RDF graphs. Yoon et al. (2018) proposed DiTex, a web-based automated extraction system for the extraction of infections topics using the semantic algorithm and natural language processing. Pacaci et al. (2018) came up with a semantic transformation technique, which extracted data from the electronic health records by conversion and loading. Conversion converts the data from datasets to RDF loaded to the final repository.
E-Health Systems
The focus on e-Health has mainly been on electronic health records and their significance in monitoring and diagnosis of patients. A patient usually has a variety of healthcare providers. They include primary care physicians, therapists, and specialists. However, record sharing through electronic methods has various drawbacks, with privacy and security being significant problems (Zhang & Lin, 2018). A patient may have an infection related to another, and in such a situation, the accuracy of the diagnosis depends on the precision of the patient’s information. The doctor queries the patient to acquire the necessary information (Alazzam et al., 2021). However, this method is not effective because the patient may have forgotten information regarding the treatment, which affects the patient’s diagnosis.
Additionally, the patients cannot describe the prescription professionally because of their limited knowledge, affecting the doctor’s judgment. The E-health system effectively solves the problem (Alazzam et al., 2021). It allows the sharing of patients’ information through various medical systems, giving the intended doctor access to the necessary information. This is important as it helps any other physician that the patients get access to in the same hospital, as they can access the records under the patient’s consent (Zhang & Lin, 2018). The different institutions can access the information as long as the personal health information sharing (PHI) agreement is met. Loud information sharing has been developed in the e-Health system because of its capability to manage and store data (Alazzam et al., 2021). These have enhanced the PHI sharing in various medical institutions with a critical concern on privacy and security preservations. Despite the focus on improving the system’s security, there are multiple challenges that cloud network has posed.
From the study conducted by Zishan et al. (2019), the largest population of Bangladesh and Malaysia reside in rural areas. This makes it challenging for them to access the healthcare facility. This group has a significant problem in terms of e-Health. Well-equipped facilities are in the urban areas, located far from these places. The rural areas are poorly connected by road and rail communication which makes it difficult to get to the urban areas to get treatment, and most of the patients succumb to death on their way to hospitals (Zishan et al., 2019). The introduction of an e-health system is crucial in promoting these people’s healthcare as it provides the necessary data for emergency medical treatment. In most developing nations, the healthcare budget is low compared to the population present, leading to a shortage of healthcare provided, particularly in marginal areas. The E-Health system is significant in handling such situations, as the people of rural areas are unable to cater to the medical bills in urban healthcare facilities.
Furthermore, these people are mostly illiterate and cannot clearly describe their problems to physicians or tell their previous medical history. This system reduces the cost of accessing healthcare services among this group, as the system will store their medical records and lower the chances of miscommunication. It is challenging for the government to have the same medical facilities in rural and urban areas in both developing and developed nations (Zishan et al., 2019). Additionally, the private sectors are usually not interested in investing in these areas. However, e-Health systems have the ability to bring the urban hospital to rural areas in a virtual way. Many doctors prefer not to work in rural areas because of the lack of the necessary facilities. These systems can change this as they provide doctors with an excellent virtual environment.
Challenges Facing E-Health Systems
A cloud is an effective tool that has gained popularity in recent times. This is because it provides an easy way to share information between various devices. However, it poses a significant threat to healthcare because of the sensitivity of the data within this area and the services involved. Privacy has become a significant problem in this area as it may compromise patients’ healthcare (Buccafurri et al., 2019). The service delivered by the cloud has various issues regarding the leakage of private information. The anonymous method does not lower or even prevent adversary situations. Buccafurri et al. (2019) proposed an authentication scheme that focuses on the unlinkability of services and user anonymity. This method aims to combine the cryptographic protocol that operates in conjunction with the P2p approach to provide access to cloud services that are secured. Furthermore, through this operation, the third party accessing the cloud-based services is granted tickets making it easy to monitor user access to any illegal activities.
ICTs should include the low-level workers that are usually excluded from the networks of healthcare and other sectors. It is essential that they are involved in creating e-Health information systems (Khumalo & Mnjama, 2019). There are various challenges associated with the e-Health information system, including maintenance costs and financial risks because of the software solutions. The implementation of the top-down technology against local stakeholder entitlement in creating the system and implementation has mainly focused on the technical side, leaving the critical areas that make it susceptible to failure. This system is vulnerable to failure as the vertical systems drive fragmentation. This increases the workload among healthcare workers because of the data overlap. There are certain cases where duplications have been enormous. The quality of data in the system is dependent on the available infrastructure as it is dependent on the collected data and information (Khumalo & Mnjama, 2019). The system also has the effect of not working in conjunction with the healthcare organization’s usual routine, making it problematic for healthcare providers to integrate with its workflow.
It requires an investment of the necessary stakeholders to enable the adoption of the required technology. There are cases where the technology used cannot fully integrate the e-health systems making it unable to fulfill the needs and requirements. There are instances where the data is of poor quality, making it difficult to be used by healthcare personnel (Khumalo & Mnjama, 2019). The system requires appropriate tools that enhance data translation as in the raw format, and they are unusable. Furthermore, this system has problems, especially in developing countries, as maintaining them has been problematic after the researchers and donors have left.
Solutions to Challenges Posed
Blockchain is considered the potential solution for handling security issue in cloud-based systems. This is because it can maintain a continuous list of immutable and distributed records (Alazzam et al., 2021). It is perceived as a distributed ledger whose primary role is to store health records for exchanging and sharing between various stakeholders. Having PHI built based on the blockchain helps manage the e-Health system by securely transferring records. However, there are challenges that blockchain has, including the design of the consensus block to meet the security privacy without violating the patient’s privacy. Unlinkability is also a significant problem when searching patients’ records. In addition, the unauthorized entity has a problem linking multiple records to a particular patient (Alazzam et al., 2021). Furthermore, there is no guarantee that the authorized medical physician is the only person accessing the intended the PHI
Zhang and Lin (2018) suggested the construction of a consortium, which will secure the PHI record sharing among various healthcare institutions. The system has two blockchains, the private blockchain that stores the patients’ PHI that is encrypted and the consortium blockchain that stores records of secure indexes. Every healthcare institution will have their PHI stored in the private blockchain that has the benefits of significant privacy preservation and protection, better security performance, fast transaction, and low cost (Zhang & Lin, 2018). Additionally, the healthcare institutions are organized in a consortium blockchain that stores the PHI search indexes. The physician can access the records by searching the consortium blockchain. This is done by accessing the private blockchain of the corresponding healthcare institution. The core components of the blockchain have various block structures that are devised for the two consortiums (Zhang & Lin, 2018). The BSSP protocol was proposed for the e-Health blockchain, securing the PHI. The patients’ records are encrypted and only accessed during the diagnosis. Furthermore, doctors can only access historical records and future records.
E-Health and Empowerment
Health information technology (HIT) has a significant impact on clinical research. It requires the healthcare community’s services to enable it to face technical and human challenges. The main objective of introducing HIT in the medical industry is to improve the quality of healthcare (Ginter et al., 2018). Additionally, the precision of the data used within healthcare is also becoming significant. ICT in the Healthcare area has a significant function in enhancing information flow between various departments of the institution (Anshari et al., 2021). The e-Health systems have gained a significant attraction to many healthcare facilities in most nations because of their significance in improving the healthcare administration and the patients and caregivers. For instance, in Ontario, Canada, the e-Health consortium provides three ways to promote diabetes control, waiting for times and management of the prescription. These goals are in line with the goals set up by the World Health Organization (WHO), comprising research, healthcare services, knowledge, health surveillance, and health literature and education. E-Health allows communication between patients and their healthcare providers via the internet, creating various avenues for interaction (Halpert, 2018; Steinberg, 2018). This model puts the consumer at the center of the information transformation process, and it portrays the significance of incorporating valid and relevant data into the decision-making process. It shows that it improves the patients’ satisfaction in the care delivery process, which is measured by the outcomes of the user (Rupali & Zervos, 2018). The use of health information in making knowledgeable decisions has become vital among healthcare users.
Implications of E-Health on Medical Science
There is a global transformation where smartphone users have increased to approximately 8.5 billion people. Mobile phones have become a common thing in people’s daily lives. This shows that health interventions using mobile devices and other applications have a higher acceptance rate. Video conferencing through WLAN, short message services (SMS), and the Global positioning system (GPS) identify patients’ immediate support. The patients can determine their physical conditions, understand the bio-signals, and go for consultation whenever necessary (Zaman et al., 2017). Chronic diseases are the leading cause of death in many parts of the world. The clinic’s traditional method of episodic care has not been sufficient in handling such diseases. For instance, integrated care installed in a device is significant in enabling the patient to practice self-management. They promote the patient’s acceptance of the prior treatment. Increased devotion of chronic disease patients to medication is significant in enhancing their performance.
Telemedicine is an e-Health strategy that was first introduced in the 1920s. With the expansion of the technological world, e-Health expanded in the 20th century as most of it is based on electronic communication (Zaman et al., 2017). Operating effectively in remote areas has made it suitable for use in emergency times. E-Health has been used to handle medical issues in various disasters, such as during Hurricanes Katrina in the U.S. Developed nations such as the U.S. and England have significantly invested in this area to promote health for their population.
Implementation of E-Health Technology on a Major Scale
Application of the technology has various limitations, as there are circumstances when they become vulnerable to various issues. The current health monitoring devices operate through smartphones while the sensors receive the data. The young generations have an easy transition because of the usage and interaction with technology (Zaman et al., 2017). However, the elderly have a significant challenge because it seems complicated to use and interact with. The implementation of e-Health is essential in fulfilling the need of the rural population community. E-Health is considered the top priority in developed countries. However, it is hugely dependent on the various multidisciplinary approaches as the team associated with the health project needs to have the necessary expertise (Zaman et al., 2017). This team includes information technology experts, scientists, pharmacists, policymakers, nurses, and physicians. E-Health plays a crucial role in promoting the health system of developed and developing countries. This can be done by taking the benefits from various platforms the system has implemented. Screening follow-up, birth and death registration, and emergency interventions are significant tools (Zaman et al., 2017). Clinical decision support (CDS) is significant in assisting healthcare providers with accurate decisions through access to critical medical data useful in improving health infrastructure. This is important as it lowers the cost of treatments while providing better services to the patients.
Implementation Challenges
The complexity of the healthcare organization makes it difficult to assimilate the e-Health systems. The assimilation of technology is dependent on the interaction between the community, technology, and individuals (Shabaya et al., 2020). Therefore, it is necessary for all the stakeholders to be associated with enabling the successful implementation of the e-Health system. The physicians and nurses have to ensure that the key information is fed into the system. The study shows that the human touch is the most significant challenge in the assimilation of the e-Health system.
References
Abdalla, R., & Mishra, A. (2018). Using agent-based methodologies in healthcare information systems. Cybernetics and Information Technologies, 18(2), 123-132.
Alazzam, M., Al Khatib, H., Mohammad, W., & Alassery, F. (2021). E-Health System characteristics, medical performance, and healthcare quality at Jordan’s health centers. Journal of Healthcare Engineering, 2021, 1-7.
Anshari, M., Almunawar, M., Younis, M., & Kisa, A. (2021). Modeling users’ empowerment in e-health systems. Sustainability, 13(23), 1-9.
Buccafurri, F., De Angelis, V., Lax, G., Nicolazzo, S., & Nocera, A. (2019). The challenge of privacy in the cloud. Encyclopedia of Bioinformatics and Computational Biology, 265-271.
Collins, G. (2017). Agile project management. Project Management, Planning and Control, 529-554.
Dawood, B., & Sah, M. (2021). Semantic Web and healthcare system in IoT-enabled smart cities. Innovations in Smart Cities Applications Volume 4, 546-557.
Ding, Y., Sun, Y., Chen, B., Borner, K., Ding, L., Wild, D., Wu, M., Difranzo, D., Fuenzalida, A. G., Li, D., Milojevic, S., Chen, S., Sankaranarayanan, M. & Toma, I. (2010). Semantic Web Portal: A Platform for better browsing and visualizing semantic data. Active Media Technology, 448-460.
Forkan, A., Khalil, I., & Tari, Z. (2014). CoCaMAAL: A cloud-oriented context-aware middleware in ambient assisted living. Future Generation Computer Systems, 35, 114-127.
Gillett, A., & Tennent, K. (2020). Sport and project management: a window into the development of temporary organizations. Handbook of Research on Management and Organizational History, 169-191. Web.
Ginter, P., Duncan, w., & Swayne, L. (2018). The Strategic Management of Health Care Organizations. John Wiley & Sons.
Halpert, A. (2018). Irritable Bowel Syndrome: Patient-provider interaction and patient education. Journal of Clinical Medicine, 7(1), 1-6.
Jiang, P., Winkley, J., Zhao, C., Munnoch, R., Min, G., & Yang, L. (2016). An intelligent information forwarder for healthcare big data systems with distributed wearable sensors. IEEE Systems Journal, 10(3), 1147-1159.
Karami, M. (2018). Semantic Web: A context for medical knowledge discovering and sharing. Iranian Journal of Medical Informatics, 7, 1-9.
Karami, M., & Rahimi, A. (2019). Semantic Web technologies for sharing clinical information in health care systems. Acta Informatica Medica, 27(1), 1-11.
Karami, M., Rahimi, A., & Shahmirzadi, A. (2017). Clinical Data Warehouse. The Health Care Manager, 36(4), 380-384.
Khumalo, N., & Mnjama, N. (2019). The effect of eHealth Information Systems on health information management in hospitals in Bulawayo, Zimbabwe. International Journal of Healthcare Information Systems and Informatics, 14(2), 17-27.
Kim, H. (2018). Developing the project budget and communicating the plan. PMP, 235-274.
Lyko, K., Nitzschke, M., & Ngonga Ngomo, A. (2016). Big data acquisition. New Horizons for a Data-Driven Economy, 39-61.
Pacaci, A., Gonul, S., Sinaci, A., Yuksel, M., & Laleci Erturkmen, G. (2018). A semantic transformation methodology for the secondary use of observational healthcare data in postmarketing safety studies. Frontiers in Pharmacology, 9, 1-12.
Rupali, P., & Zervos, M. (2018). Impact of an antimicrobial stewardship intervention in India: Evaluation of post prescription review and feedback as a method of promoting optimal antimicrobial use. Open Forum Infectious Diseases, 5(1), 1-13.
Shabaya, P., Ateya, I., & Wanyembi, G. (2020). Challenges of assimilation of e-Health Systems in healthcare. Proceedings of the 4Th International Conference on Medical and Health Informatics, 1-13.
Steinberg, K. (2018). Medical Decisions: Who Should Get to Decide?. Caring For the Ages, 19(7), 1-10.
Taouli, A., Djamel, A., Keskes, N., & Bencherif, K. (2018). Semantic for big data analysis: A survey. In INTIS2018: BigData & Internet of things IoT. Marakech.
Tilahun, B., Kauppinen, T., Keßler, C., & Fritz, F. (2014). Design and development of a linked open data-based health information representation and visualization system: Potentials and preliminary evaluation. JMIR Medical Informatics, 2(2), 1-13.
Ullah, F., Habib, M., Farhan, M., Khalid, S., Durrani, M., & Jabbar, S. (2017). Semantic interoperability for big-data in heterogeneous IoT infrastructure for healthcare. Sustainable Cities and Society, 34, 90-96. Web.
Yoon, J., Kim, J., & Jang, B. (2018). DiTeX: Disease-related topic extraction system through internet-based sources. PLOS ONE, 13(8), 1-8.
Zaman, S., Hossain, N., Ahammed, S., & Ahmed, Z. (2017). Contexts and opportunities of e-Health technology in medical care. Journal of Medical Research and Innovation, 1(2), 1-12.
Zenuni, X., Raufi, B., Ismaili, F., & Ajdari, J. (2015). State of the art of semantic Web for healthcare. Procedia – Social and Behavioral Sciences, 195, 1990-1998.
Zhang, A., & Lin, X. (2018). Towards secure and privacy-preserving data sharing in e-Health Systems via Consortium Blockchain. Journal of Medical Systems, 42(8).
Zishan, S., Hossain, C., Mohamed,, M., & Sharun, S. (2019). The Scenario of e-Health Systems in developing countries (Bangladesh and Malaysia). Nternational Journal of Recent Technology and Engineering (IJRTE), 8(1), 1-6.