The Intellectual Structure of Health and Medical Informatics

2012 ◽  
pp. 1-16
Author(s):  
Wullianallur Raghupathi ◽  
Sridhar Nerur
Keyword(s):  
Citation Analysis ◽  
Medical Informatics ◽  
Health Informatics ◽  
Clinical Decision Support Systems ◽  
Health Information Systems ◽  
Clinical Decision ◽  
Intellectual Structure ◽  
Future Research ◽  
Editorial Boards ◽  
Body Of Knowledge

This paper presents the results of an author co-citation analysis of the health and medical informatics discipline. It updates a smaller study that focused on health information systems. Drawing on such sub-fields as bio informatics, clinical decision support systems, computational genomics, e-health, health informatics, and others, this body of knowledge defines the core internal structure of the discipline and delineates its sub-fields. An author co-citation analysis was performed for a nine-year period using the members of editorial boards of several medical informatics-related journals as an initial author sample (N = 272). Several multivariate analyses, including cluster analysis, factor analysis and multidimensional scaling, were performed. The authors results confirm that several established sub-fields still stand but a number of new sub-fields are emerging. Future research can build on this work and examine other journals and additional authors to gain insights into the collaborative and interdisciplinary nature of the health and medical informatics discipline.

The Intellectual Structure of Health and Medical Informatics

2010 ◽  
Vol 5 (4) ◽  
pp. 20-34 ◽  
Author(s):  
Wullianallur Raghupathi ◽  
Sridhar Nerur
Keyword(s):  
Citation Analysis ◽  
Medical Informatics ◽  
Health Informatics ◽  
Multivariate Analyses ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Intellectual Structure ◽  
Future Research ◽  
Editorial Boards ◽  
Body Of Knowledge

This paper presents the results of an author co-citation analysis of the health and medical informatics discipline. It updates a smaller study that focused on health information systems. Drawing on such sub-fields as bio informatics, clinical decision support systems, computational genomics, e-health, health informatics, and others, this body of knowledge defines the core internal structure of the discipline and delineates its sub-fields. An author co-citation analysis was performed for a nine-year period using the members of editorial boards of several medical informatics-related journals as an initial author sample (N = 272). Several multivariate analyses, including cluster analysis, factor analysis and multidimensional scaling, were performed. The authors results confirm that several established sub-fields still stand but a number of new sub-fields are emerging. Future research can build on this work and examine other journals and additional authors to gain insights into the collaborative and interdisciplinary nature of the health and medical informatics discipline.

Achieving Effective Health Information Systems

2009 ◽  
pp. 560-583
Author(s):  
Jim Warren ◽  
Karen Day ◽  
Martin Orr
Keyword(s):  
Information Systems ◽  
Health Information ◽  
Health Informatics ◽  
Clinical Decision Support Systems ◽  
Health Information Systems ◽  
Clinical Decision ◽  
Commodity Culture ◽  
Domain Specific ◽  
Quantitative And Qualitative Research ◽  
Hierarchy Of Evidence

In this chapter we aim to promote an understanding of the complexity of healthcare as a setting for information systems and how this complexity influences the achievement of successful implementations. We define health informatics and examine its role as an enabler in the delivery of healthcare. Then we look at the knowledge commodity culture of healthcare, with the gold standard of systematic reviews and its hierarchy of evidence. We examine the different forms of quantitative and qualitative research that are most commonly found in healthcare and how they influence the requirements for health information systems. We also examine some domain-specific issues that must be considered by health information systems developers, including those around clinical decision support systems and clinical classification and coding systems. We conclude with a discussion of the challenges that must be balanced by the health systems implementer in delivering robust systems that support evidence-based healthcare processes.

Business-IT Alignment Literature Review

2018 ◽  
Vol 31 (3) ◽  
pp. 34-53 ◽  
Author(s):  
Yu Jia ◽  
Nianxin Wang ◽  
Shilun Ge
Keyword(s):  
Citation Analysis ◽  
Knowledge Flows ◽  
Intellectual Structure ◽  
Future Research ◽  
Research Directions ◽  
It Alignment ◽  
Research Themes ◽  
Future Research Directions ◽  
Business It Alignment ◽  
New Perspective

The purpose of this article is to portray the knowledge evolution paths of business-IT alignment (BITA) research and identify a set of important papers in the development of BITA, and elucidate the intellectual structure of this field. This study collected 309 papers published during the period 1983-2015 from the Web of Science (WOS) database. Using a variety of bibliometric and visualization analytic techniques such as citation analysis, co-citation analysis and main path analysis, this article (1) delineates the significant knowledge flows of BITA research and identifies 15 important papers in this field; (2) graphically maps the influential countries, institutions, and journals of BITA research; (3) identifies four major research themes: BITA model, measurement, antecedents, and dynamics, and visualizing the relationships among them. Based on these findings, recommendations for the future research directions have suggested. This article provides IT practitioners, executives, and scholars with a new perspective to get a better understanding of BITA.

HealthGrids in Health Informatics

2011 ◽  
pp. 913-932
Author(s):  
Aisha Naseer ◽  
Lampros K. Stergioulas
Keyword(s):  
Information Systems ◽  
Health Information ◽  
Health Informatics ◽  
Health Information Systems ◽  
Emerging Technology ◽  
Future Research ◽  
Hierarchical Order ◽  
Different Types ◽  
Ubiquitous Access

Healthcare is a vast domain encapsulating not only multiple sub-domains or sub-sectors but also many diverse operations and logistics within each sub-sector. This diversity needs to be handled in a systematic and well-characterized manner in order to maintain consistency of various healthcare tasks. Integration of health information systems within each healthcare sub-sectors is crucial for ubiquitous access and sharing of information. The emerging technology of HealthGrids holds the promise to successfully integrate health information systems and various healthcare entities onto a common, globally shared and easily accessible platform. Many different types of HealthGrids exist but there lacks a taxonomy to classify them into a hierarchical order. This chapter presents a well-characterized taxonomy of different types of HealthGrid and classifies them into four major types, namely BioGrid, MediGrid, PharmaGrid and CareGrid. Each of these HealthGrids possesses dedicated features and functionalities. The proposed taxonomy serves to better understand the realtionship among various HealthGrid types and would lay a basis for future research.

HealthGrids in Health Informatics

2010 ◽  
pp. 124-143 ◽  
Author(s):  
Aisha Naseer ◽  
Lampros K. Stergioulas
Keyword(s):  
Information Systems ◽  
Health Information ◽  
Health Informatics ◽  
Health Information Systems ◽  
Emerging Technology ◽  
Future Research ◽  
Hierarchical Order ◽  
Different Types ◽  
Ubiquitous Access

Healthcare is a vast domain encapsulating not only multiple sub-domains or sub-sectors but also many diverse operations and logistics within each sub-sector. This diversity needs to be handled in a systematic and well-characterized manner in order to maintain consistency of various healthcare tasks. Integration of health information systems within each healthcare sub-sectors is crucial for ubiquitous access and sharing of information. The emerging technology of HealthGrids holds the promise to successfully integrate health information systems and various healthcare entities onto a common, globally shared and easily accessible platform. Many different types of HealthGrids exist but there lacks a taxonomy to classify them into a hierarchical order. This chapter presents a well-characterized taxonomy of different types of HealthGrid and classifies them into four major types, namely BioGrid, MediGrid, PharmaGrid and CareGrid. Each of these HealthGrids possesses dedicated features and functionalities. The proposed taxonomy serves to better understand the realtionship among various HealthGrid types and would lay a basis for future research.

Evaluating Diagnostic Tests

1993 ◽  
Vol 7 (2) ◽  
pp. 66-69 ◽  
Author(s):  
C.W. Douglass
Keyword(s):  
Predictive Value ◽  
Diagnostic Tests ◽  
Clinical Decision Making ◽  
New Technologies ◽  
Clinical Decision ◽  
Future Research ◽  
Scientific Support ◽  
Body Of Knowledge ◽  
Efficacy Parameters ◽  
Sensitivity Specificity

The presentations at this conference will discuss new technologies and rapid scientific developments that have resulted in new diagnostic tests for periodontal disease, musculoskeletal imaging, temporomandibular joint dysfunctions, and incipient coronal and root dental caries. However, for many of these claims, there has been insufficient scientific support regarding the sensitivity and specificity of the tests, or their ability to predict the percent of cases in which the disease or condition progresses to the next state of development. Research is needed that will yield the basic diagnostic parameters of new diagnostic tests, i.e., their accuracy, precision, sensitivity, specificity, positive predictive value, and negative predictive value. The purpose and methods for calculating each of these measures are described in this paper. Five questions are then presented that will need to be addressed in future research regarding new diagnostic tests: (1) Does the scientific theory of the test fit with our current body of knowledge? (2) Have the efficacy parameters of the test been reliably determined? (3) How does the test affect clinical decision-making? (4) Does using the test improve the patient's health? and (5) Is the added expense of the test justified by increased effectiveness or by avoiding other health expenditures?

Intellectual structure of trust in business and management: a co-citation analysis

2016 ◽  
Vol 34 (3) ◽  
pp. 358-370 ◽  
Author(s):  
Keng-Chieh Yang
Keyword(s):  
Citation Analysis ◽  
Strategic Alliance ◽  
Intellectual Structure ◽  
Future Research ◽  
Content Type ◽  
Knowledge Database ◽  
Organizational Behaviour ◽  
Business Operations ◽  
Thomson Reuters ◽  
Business And Management

Purpose In the past few decades, there has been a lot of literature about trust research for business and management. However, few authors have applied co-citation analysis. Design/methodology/approach Trust is one of the most discussed issues in management, as it has proved to have an essential role in business operations. In this study, all citation documents are included in Thomson Reuters ISI Web of Knowledge database from 1992 to 2010. Findings By using statistics analysis including factor analysis, cluster analysis and multidimensional scaling, researchers identified four domains, including organizational behaviour, strategic alliance, marketing and social capital. Directions for future research are discussed. Originality/value This study is the first to apply co-citation techniques in the fields of trust. Therefore, the major contribution of this study is to provide an intellectual structure and trends within the field of trust from an objective and quantitative perspective.

scholarly journals The Effectiveness of Health Informatics

2010 ◽  
pp. 13-37
Author(s):  
Francesco Paolucci ◽  
Henry Ergas ◽  
Terry Hannan ◽  
Jos Aarts
Keyword(s):  
Health Care ◽  
Health Information ◽  
Information Management ◽  
Health Informatics ◽  
Clinical Decision Making ◽  
Care Delivery ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Long Run ◽  
Information Management Systems

Health care is complex and there are few sectors that can compare to it in complexity and in the need for almost instantaneous information management and access to knowledge resources during clinical decision-making. There is substantial evidence available of the actual, and potential, benefits of e-health tools that use computerized clinical decision support systems (CDSS) as a means for improving health care delivery. CDSS and associated technologies will not only lead to an improvement in health care but will also change the nature of what we call electronic health records (EHR) The technologies that “define” the EHR will change the nature of how we deliver care in the future. Significant challenges relating to the evaluation of these health information management systems relate to demonstrating their ongoing cost-benefit, cost-effectiveness, and effects on the quality of care and patient outcomes. However, health information technology is still mainly about the effectiveness of processes and process outcomes, and the technology is still not mature, which may lead to unintended consequences, but it remains promising and unavoidable in the long run.

scholarly journals Education and Research in Health Informatics at the Faculty of Medicine of Marseille, Laboratory for Education and Research in Medical Information Processing (LERTIM)

2002 ◽  
Vol 11 (01) ◽  
pp. 154-159
Author(s):  
D. Fieschi ◽  
J. Gouvernet ◽  
M. Joubert ◽  
G. Soula ◽  
M. Fieschi
Keyword(s):  
Medical Education ◽  
Medical Informatics ◽  
Health Informatics ◽  
Medical Information ◽  
Medical Training ◽  
Training Programs ◽  
Clinical Decision ◽  
Training Curriculum ◽  
Decision Systems ◽  
And Training

Abstract:This paper is a brief review of the research and training programs offered in Medical Informatics at the Faculty of Medicine of Marseille (LERTIM). Our laboratory teaches medical informatics and bio-statistics in the medical training curriculum, and prepares for specialised degrees and provides continuing medical education. The research projects developed by our team fall into four groups: clinical decision systems, health information systems, medical education systems, integration systems.

An Empirical Study of Green Finance Research Through Bibliometrics

2019 ◽  
pp. 84-106
Author(s):  
Feng Hu ◽  
Haibo Wei
Keyword(s):  
Citation Analysis ◽  
Research Trends ◽  
Intellectual Structure ◽  
Future Research ◽  
Research Front ◽  
Word Analysis ◽  
Emerging Trends ◽  
Green Finance ◽  
Research Hotspots ◽  
The Web

Green finance issues have triggered the increasing research enthusiasm of researchers. With the rapid growing of publications related to green finance, it is difficult for readers to deeply understand the intellectual structure, research hotspots, and trends. In addition, the dynamic nature of a research front poses challenges for the scientists, research policymakers, and many others to keep up with the rapid advances of the state of the art in science. Therefore, the authors conducted a bibliometric analysis from the Web of Science over the period of 1998–2017. Co-word analysis and co-citation analysis are employed to explore institution distribution, journal co-citation analysis, author co-citation analysis, document co-citation analysis, and keyword co-word analysis, particularly in high frequency items, intellectual turning points, burst points, and emerging trends. The results can be useful for institutions and researchers worldwide to understand the panorama of green finance research, find the potential research gaps, and focus on the future research trends.

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