A Next Generation of IT

A potential new generation computing environment is emerging which combines wiki technology with semantic web concepts. This has brought about the fusion of the wiki execution ecosystem, a semantic web for model-driven applications, and a high-level language as an extension to wiki text for accelerated development. Semantic MediaWiki provides this platform and a fragment of a health record, including allergy intolerance as structured in HL7 FHIR with terminology bindings to SNOMED CT and to HL7 terminologies was developed by the author in a short timeframe (approximately 10 hours). The system navigates around the health record and controls the entry of terms in the record from controlled ValueSets. All terminologies and ValueSets are integrated into the prototype.

Robotics in Surgical Techniques Robotics in Surgical Techniques

Healthcare robotic applications are a growing trend due to rapid demographic changes that affect healthcare systems, professionals and quality of life indicators, for the elderly, the injured and the disabled. Current technological advances in robotic systems offer an exciting field for medical research, as the interdisciplinary approach of robotics in healthcare and specifically in surgery is continuously gaining ground. This chapter features a review of current applications, from external large scale robotic devices to nanoscale swarm robots programmed to interact on a cellular level.

Opportunities and Challenges of Big Data in Healthcare

Healthcare big data streams from multiple information sources at an alarming volume, velocity, and variety. The challenge that faces the healthcare industry is extracting meaningful value from such sources. This chapter investigates the diversity and forms of data in the healthcare sector, reviews the methods used to search and analyze these data throughout the past years, and the use of machine learning and data mining techniques to mine useful knowledge from such data. The chapter will also highlight innovations of particular systems and tools which spot the fine approaches for different healthcare data, raise the standard of care and recap the tools and data collection methods. The authors emphasize some of ethical issues regarding processing these records and some data privacy issues.

Real Time Analysis Based on Intelligent Applications of Big Data and IoT in Smart Health Care Systems

Currently, there is an expanding interest for additional medical data from patients about their healthcare choices and related decisions, and they further need investment in their basic health issues. Big data provides patients presumptuous data to help them settle on the best choice and align with their medicinal treatment plan. One of the very advanced concepts related to the synthesis of big data sets to reveal the hidden pattern in them is big data analytics. It involves demanding techniques to mine and extract relevant data that includes the actions of piercing a database, effectively mine the data, query and inspect the data and is committed to enhance the technical execution of various task segments. The capacity to synthesize a lot of data can enable an association to manage data that can influence the business. In this way, the primary goal of big data analytics is to help business relationships to have enhanced comprehension of data, and subsequently, settle on proficient and very much educated decisions. Big data analytics empowers data diggers and researchers to examine an extensive volume of data that may not be outfit utilizing customary apparatuses. Big data analytics require advances and statistical instruments that can change a lot of organized, unstructured, and semi-organized data into more reasonable data and metadata designed for explanatory procedures. There is tremendous positive potential concerning the application of big data in human health care services and many related major applications are still in their developmental stages. The deployment of big data in health service demonstrates enhancing health care results and controlling the expenses of common people due to treatment, as proven by some developing use cases. Keeping in view such powerful processing capacity of big data analytics in various technical fields of modern civilization related to health care, the current research article presents a comprehensive study and investigation on big data analytics and its application in multiple sectors of society with significance in health care applications.

An Investigation Into the Efficacy of Deep Learning Tools for Big Data Analysis in Health Care

This article describes how machine learning (ML) algorithms are very useful for analysis of data and finding some meaningful information out of them, which could be used in various other applications. In the last few years, an explosive growth has been seen in the dimension and structure of data. There are several difficulties faced by conventional ML algorithms while dealing with such highly voluminous and unstructured big data. The modern ML tools are designed and used to deal with all sorts of complexities of data. Deep learning (DL) is one of the modern ML tools which are commonly used to find the hidden structure and cohesion among these large data sets by giving proper training in parallel platforms with intelligent optimization techniques to further analyze and interpret the data for future prediction and classification. This article focuses on the use of DL tools and software which are used in past couple of years in various areas and especially in the area of healthcare applications.

Bidirectional Spreading Activation Method for Finding Human Diseases Relatedness Using Well-Formed Disease Ontology

The successful application of semantic web in medical informatics and the fast expanding of biomedical knowledge have prompted to the requirement for a standardized representation of knowledge and an efficient algorithm for querying this extensive information. Spreading activation algorithm is suitable to work on incomplete and large datasets. This article presents a method called SAOO (Spreading Activation over Ontology) which identifies the relatedness between two human diseases by applying spreading activation algorithm based on bidirectional search technique over large disease ontology. The proposed methodology is divided into two phases: Semantic matching and Disease relatedness detection. In Semantic Matching, semantically identify diseases in user's query in the ontology. In the Disease Relatedness Detection, URIs of the diseases are passed to the relatedness detector which returns the set of diseases that may connect them. The proposed method improves the non-semantic medical systems by considering semantic domain knowledge to infer diseases relatedness.

Exploring the Role of Service Quality and Knowledge for Mobile Health Services

The use of mobile technology-based services has made healthcare delivery more accessible and affordable in recent times. In fact, mobile health services today act as an effective means of providing healthcare knowledge to users directly from providers. However, the cynical behavior of users regarding this medium of healthcare services often encircles around the quality of such services. The aim of this paper is to examine the role of service quality and knowledge among other underlying factors that can influence future use intentions of m-Health services in the context of Bangladesh. The conceptual model of the study identifies that certain aspects of service qualities like reliability, privacy, responsiveness, empathy and information quality along with facilitating conditions, effort expectancy, performance expectancy and social influence plays an important role in capturing users' overall perceptions of mobile health services. Finally, the study highlights managerial implications, future research directions and limitations from the Bangladesh perspective.

The Performance of Educational Institutions Through the Electronic Records Management Systems

This article discusses the relationship between educational institutes that use ERMS and the performance of those institutions. This article uses a mixed explanatory method that incorporated quantitative and qualitative approaches. The quantitative method collected the responses from 364 participants. This was followed by a qualitative approach where experts were interviewed to verify the model. The results generated using the quantitative approach demonstrated that the quality of the system, information, and service as well as the security provided by the system had a significant positive relationship with the successful adoption of ERMS, which in turn improved performance. Moreover, the qualitative results that gathered through the experts confirmed the findings and contributed to enriching the understanding of the adoption of ERMS in educational institutions.

Hierarchy Similarity Analyser-An Approach to Securely Share Electronic Health Records

Health professionals need an access to various dimensions of Electronic Health Records (EHR). Depending on technical constraints, each organization defines its own access control schema exhibiting heterogeneity in organizational rules and policies. Achieving interoperability between such schemas often result in contradictory rules thereby exposing data to undue disclosures. Permitting interoperable sharing of EHRs and simultaneously restricting unauthorized access is the major objective of this paper. An Extensible Access Control Markup Language (XACML)-based framework, Hierarchy Similarity Analyser (HSA), is proposed which fine-grains access control policies of disparate healthcare organizations to achieve interoperable and secured sharing of EHR under set authorizations. The proposed framework is implemented and verified using automated Access Control Policy Testing (ACPT) tool developed by NIST. Experimental results identify the users receive secured and restricted access as per their authorizations and role hierarchy in the organization.

Ethics of Electronic Health Record Systems

This article describes how healthcare and IT are combatting the ethical implications of electronic health records (EHRs) in order to make them adopted by over 90% of small practices. There is a lack of trust in EHRs and uneasiness about what they will accomplish. Furthermore, security concerns have become more prevalent as a result of increased hacker activity. The objective of this article is to analyze these ethical issues in an effort to eliminate them as a hinderance to EHR implementation. As of now, 98% of all hospitals use EHRs. Between 2009 and 2015, the government allocated money and resources for incentive programs to get EHRs into every healthcare providers' office. During this time period, over $800 million dollars facilitated EHR implementation. Using this as a tool EHRs negative perception can be revitalized and combated with the meaningful use program. This article will highlight the ethical implications of EHRs and suggest ways in which to avoid them to make EHRs available in every healthcare provider.