scholarly journals Semantic Web: A Context for Medical Knowledge Discovering and Sharing

2018 ◽  
Vol 7 ◽  
pp. 2
Author(s):  
Mahtab Karami
Keyword(s):  
Semantic Web ◽  
Medical Research ◽  
Medical Knowledge ◽  
Quality Of Healthcare ◽  
Future Research ◽  
General View ◽  
Medical Sciences ◽  
Research Information ◽  
Healthcare Organizations ◽  
Web Standards

Background and Aim: This article will discuss Semantic Web standards and ontologies in two areas: (1) the research and (2) healthcare. Semantic Web standards are important in the medical sciences since much of the medical research that is available needs an avenue to be shared across disparate computer systems.Methods: This review article was performed based on a literature review and internet search through scientific databases such as PubMed, Scopus, Web of science and Google Scholar.Conclusion: Ontologies can provide a basis for the searching of context-based medical research information so that it can be integrated and used as a foundation for future research. The healthcare industry will be examined specifically in its use of electronic health records (EHR), which need Semantic Web standards to be communicated across different EHR systems. The increased use of EHRs across healthcare organizations will also require ontologies to support context-sensitive searching of information, as well as creating context-based rules for appointments, procedures, and tests so that the quality of healthcare is improved. Literature in these areas has been combined in this article to provide a general view of how Semantic Web standards and ontologies are used, and to give examples of applications in the areas of healthcare and the medical sciences.

Semantic Web Standards and Ontologies in the Medical Sciences and Healthcare

2007 ◽  
pp. 405-420
Author(s):  
Sherrie D. Cannoy ◽  
Lakshmi Iyer
Keyword(s):  
Semantic Web ◽  
Medical Research ◽  
Quality Of Healthcare ◽  
Future Research ◽  
Healthcare Industry ◽  
Medical Sciences ◽  
Research Information ◽  
Healthcare Organizations ◽  
Context Sensitive ◽  
Web Standards

This chapter will discuss Semantic Web standards and ontologies in two areas: (1) the medical sciences field and (2) the healthcare industry. Semantic Web standards are important in the medical sciences since much of the medical research that is available needs an avenue to be shared across disparate computer systems. Ontologies can provide a basis for the searching of context-based medical research information so that it can be integrated and used as a foundation for future research. The healthcare industry will be examined specifically in its use of electronic health records (EHR), which need Semantic Web standards to be communicated across different EHR systems. The increased use of EHRs across healthcare organizations will also require ontologies to support context-sensitive searching of information, as well as creating context-based rules for appointments, procedures, and tests so that the quality of healthcare is improved. Literature in these areas has been combined in this chapter to provide a general view of how Semantic Web standards and ontologies are used, and to give examples of applications in the areas of healthcare and the medical sciences.

Semantic Web Standards and Ontologies in the Medical Sciences and Healthcare

2009 ◽  
pp. 2323-2335
Author(s):  
Sherrie D. Cannoy ◽  
Lakshmi Iyer
Keyword(s):  
Semantic Web ◽  
Medical Research ◽  
Quality Of Healthcare ◽  
Future Research ◽  
Healthcare Industry ◽  
Medical Sciences ◽  
Research Information ◽  
Healthcare Organizations ◽  
Context Sensitive ◽  
Web Standards

This chapter will discuss Semantic Web standards and ontologies in two areas: (1) the medical sciences field and (2) the healthcare industry. Semantic Web standards are important in the medical sciences since much of the medical research that is available needs an avenue to be shared across disparate computer systems. Ontologies can provide a basis for the searching of context-based medical research information so that it can be integrated and used as a foundation for future research. The healthcare industry will be examined specifically in its use of electronic health records (EHR), which need Semantic Web standards to be communicated across different EHR systems. The increased use of EHRs across healthcare organizations will also require ontologies to support context-sensitive searching of information, as well as creating context-based rules for appointments, procedures, and tests so that the quality of healthcare is improved. Literature in these areas has been combined in this chapter to provide a general view of how Semantic Web standards and ontologies are used, and to give examples of applications in the areas of healthcare and the medical sciences.

Semantic Web Standards and Ontologies in the Medical Sciences and Healthcare

2011 ◽  
pp. 65-77
Author(s):  
Sherrie D. Cannoy
Keyword(s):  
Semantic Web ◽  
Medical Research ◽  
Quality Of Healthcare ◽  
Future Research ◽  
Healthcare Industry ◽  
Medical Sciences ◽  
Research Information ◽  
Healthcare Organizations ◽  
Context Sensitive ◽  
Web Standards

This chapter will discuss Semantic Web standards and ontologies in two areas: (1) the medical sciences field and (2) the healthcare industry. Semantic Web standards are important in the medical sciences since much of the medical research that is available needs an avenue to be shared across disparate computer systems. Ontologies can provide a basis for the searching of context-based medical research information so that it can be integrated and used as a foundation for future research. The healthcare industry will be examined specifically in its use of electronic health records (EHR), which need Semantic Web standards to be communicated across different EHR systems. The increased use of EHRs across healthcare organizations will also require ontologies to support context-sensitive searching of information, as well as creating context-based rules for appointments, procedures, and tests so that the quality of healthcare is improved. Literature in these areas has been combined in this chapter to provide a general view of how Semantic Web standards and ontologies are used, and to give examples of applications in the areas of healthcare and the medical sciences.

Hospital

2006 ◽  
Vol 23 (2-3) ◽  
pp. 573-579 ◽  
Author(s):  
Bryan S. Turner
Keyword(s):  
Medical Research ◽  
Knowledge Production ◽  
Medical Knowledge ◽  
Scientific Research ◽  
General Function ◽  
Medical Sciences ◽  
Historical Origin ◽  
Medical Faculties ◽  
Private Corporations ◽  
The University

Hospitals are traditional sites, not only of care, but of knowledge production. The word ‘hospital’ is derived from ‘hospitality’, and is also associated with ‘spital’, ‘hotel’ and ‘hospice’. In medieval society, the hospice was a place of rest, security and entertainment. The Knights Hospitallers were an order of military monks that took its historical origin from a hospital founded in Jerusalem in 1048. Before the rise of the modern research hospital, these spitals had a more general function as charitable institutions for the care and maintenance of the aged, infirm and impoverished. Hospitals were important in the historical emergence of the university, but with the dominance of bio-medical sciences medical faculties have become increasingly separated geographically and administratively from other faculties. Medical research is dominated by private corporations and increasingly medical knowledge exists outside the conventional procedures and norms of scientific research.

scholarly journals Analysis of Facilitators and Barriers to the Delivery of Routine Care during the COVID-19 Global Pandemic: A Systematic Review

Healthcare ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 528
Author(s):  
Cristian Lieneck ◽  
Brooke Herzog ◽  
Raven Krips
Keyword(s):  
Systematic Review ◽  
Health Care ◽  
Health Care Services ◽  
Care Delivery ◽  
Routine Care ◽  
Future Research ◽  
Healthcare Organizations ◽  
Care Services ◽  
Global Pandemic ◽  
Facilitators And Barriers

The delivery of routine health care during the COVID-19 global pandemic continues to be challenged as public health guidelines and other local/regional/state and other policies are enforced to help prevent the spread of the virus. The objective of this systematic review is to identify the facilitators and barriers affecting the delivery of routine health care services during the pandemic to provide a framework for future research. In total, 32 articles were identified for common themes surrounding facilitators of routine care during COVID-19. Identified constructed in the literature include enhanced education initiatives for parents/patients regarding routine vaccinations, an importance of routine vaccinations as compared to the risk of COVID-19 infection, an enhanced use of telehealth resources (including diagnostic imagery) and identified patient throughput/PPE initiatives. Reviewers identified the following barriers to the delivery of routine care: conservation of medical providers and PPE for non-routine (acute) care delivery needs, specific routine care services incongruent the telehealth care delivery methods, and job-loss/food insecurity. Review results can assist healthcare organizations with process-related challenges related to current and/or future delivery of routine care and support future research initiatives as the global pandemic continues.

scholarly journals Approaches to the rationalization of surgical instrument trays: scoping review and research agenda

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Bruno Miranda dos Santos ◽  
Flavio Sanson Fogliatto ◽  
Carolina Melecardi Zani ◽  
Fernanda Araujo Pimentel Peres
Keyword(s):  
Scoping Review ◽  
Surgical Instruments ◽  
Future Research ◽  
Healthcare Organizations ◽  
Research Directions ◽  
Lean Practices ◽  
Study Selection ◽  
Expert Analysis ◽  
Future Research Directions ◽  
Research Questions

Abstract Background Surgical Tray Rationalization (STR) consists of a systematic reduction in the number of surgical instruments to perform specific procedures without compromising patient safety while reducing losses in the sterilization and assembly of trays. STR is one example of initiatives to improve process performance that have been widely reported in industrial settings but only recently have gained popularity in healthcare organizations. Methods We conduct a scoping review of the literature to identify and map available evidence on surgical tray management. Five methodological stages are implemented and reported; they are: identifying research questions, identifying relevant studies, study selection, charting the data, and collating, summarizing and reporting the results. Results We reviewed forty-eight articles on STR, which were grouped according to their main proposed approaches: expert analysis, lean practices, and mathematical programming. We identify the most frequently used techniques within each approach and point to their potential contributions to operational and economic dimensions of STR. We also consolidate our findings, proposing a roadmap to STR with four generic steps (prepare, rationalize, implement, and consolidate) and recommended associated techniques. Conclusions To the best of our knowledge, ours is the first study that reviews and systematizes the existing literature on the subject of STR. Our study closes with the proposition of future research directions, which are presented as nine research questions associated with the four generic steps proposed in the STR roadmap.

scholarly journals Predictors of outpatients’ no-show: big data analytics using apache spark

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Tahani Daghistani ◽  
Huda AlGhamdi ◽  
Riyad Alshammari ◽  
Raed H. AlHazme
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Negative Impact ◽  
Big Data Analytics ◽  
Quality Of Healthcare ◽  
Machine Learning Techniques ◽  
Gradient Boosting ◽  
Healthcare Organizations ◽  
Data Framework ◽  
Learning Techniques

AbstractOutpatients who fail to attend their appointments have a negative impact on the healthcare outcome. Thus, healthcare organizations facing new opportunities, one of them is to improve the quality of healthcare. The main challenges is predictive analysis using techniques capable of handle the huge data generated. We propose a big data framework for identifying subject outpatients’ no-show via feature engineering and machine learning (MLlib) in the Spark platform. This study evaluates the performance of five machine learning techniques, using the (2,011,813‬) outpatients’ visits data. Conducting several experiments and using different validation methods, the Gradient Boosting (GB) performed best, resulting in an increase of accuracy and ROC to 79% and 81%, respectively. In addition, we showed that exploring and evaluating the performance of the machine learning models using various evaluation methods is critical as the accuracy of prediction can significantly differ. The aim of this paper is exploring factors that affect no-show rate and can be used to formulate predictions using big data machine learning techniques.

A systematic literature review of responsible leadership

2017 ◽  
Vol 8 (2) ◽  
pp. 281-299 ◽  
Author(s):  
Charbel Greige Frangieh ◽  
Hala Khayr Yaacoub
Keyword(s):  
Personal Characteristics ◽  
Organizational Structures ◽  
Future Research ◽  
Responsible Leadership ◽  
Leadership Studies ◽  
Medical Sciences ◽  
Content Type ◽  
Final Sample ◽  
Stakeholder Culture ◽  
Stakeholder Needs

Purpose This study aims to provide a point of reference and another of guidance for future research on the topic of responsible leadership by exploring its challenges, outcomes and practices. Design/methodology/approach A systematic review of the literature, originally adopted from the medical sciences but also used in management and leadership studies, was conducted to integrate research in an organized, translucent and reproducible manner. The final sample of 46 empirical and conceptual studies were scientifically screened and synthesized. Findings The synthesis revealed that balancing stakeholder needs, personal characteristics and organizational structures are the main challenges against responsible leadership, whereas financial benefits, employees-related benefits and reputational gains among others are the main outcomes. Practices pinpointed, while scarce, are represented in nurturing a stakeholder culture, and engaging employee-related and human-resource-responsible functions. Originality/value This study contributes to the development of responsible leadership.

scholarly journals Future of Medical Research in Rare Diseases and Cancers: Shift from Pharma to Biotech and the Golden Age of Medical Advancement

2017 ◽  
Vol 6 (2) ◽  
pp. 12
Author(s):  
Abhith Pallegar
Keyword(s):  
Big Data ◽  
Medical Research ◽  
Rare Diseases ◽  
Network Effects ◽  
Medical Knowledge ◽  
Economic Cost ◽  
Medical Data ◽  
Data Sets ◽  
Leading Role ◽  
Diverse Data

The objective of the paper is to elucidate how interconnected biological systems can be better mapped and understood using the rapidly growing area of Big Data. We can harness network efficiencies by analyzing diverse medical data and probe how we can effectively lower the economic cost of finding cures for rare diseases. Most rare diseases are due to genetic abnormalities, many forms of cancers develop due to genetic mutations. Finding cures for rare diseases requires us to understand the biology and biological processes of the human body. In this paper, we explore what the historical shift of focus from pharmacology to biotechnology means for accelerating biomedical solutions. With biotechnology playing a leading role in the field of medical research, we explore how network efficiencies can be harnessed by strengthening the existing knowledge base. Studying rare or orphan diseases provides rich observable statistical data that can be leveraged for finding solutions. Network effects can be squeezed from working with diverse data sets that enables us to generate the highest quality medical knowledge with the fewest resources. This paper examines gene manipulation technologies like Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) that can prevent diseases of genetic variety. We further explore the role of the emerging field of Big Data in analyzing large quantities of medical data with the rapid growth of computing power and some of the network efficiencies gained from this endeavor. 

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