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.

Current State of Dental Informatics in the Field of Health Information Systems: Systematic Review (Preprint)

2021 ◽  
Author(s):  
Benoit Ballester ◽  
Frédéric Bukiet ◽  
Jean-Charles Dufour
Keyword(s):  
Information Systems ◽  
Patient Care ◽  
Health Information ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Health Information Systems ◽  
Clinical Decision ◽  
Data Capture ◽  
Clinical Coding ◽  
Coding Systems

BACKGROUND Over the past 50 years, dental informatics has developed significantly in the field of health information systems. Accordingly, several studies have been conducted on standardized clinical coding systems, data capture, and clinical data reuse in dentistry. OBJECTIVE The primary objective of this systematic review was to summarize studies on standardized clinical coding systems and electronic dental record (EDR) data capture in dentistry. The secondary objective was to explore the practical implications of reusing EDR data in clinical decision support systems, quality measure development, and clinical research. METHODS Based on the definition of health information systems, we divided the literature search into 3 specific sub-searches: “standardized clinical coding systems,” “data capture,” and “reuse of routine patient care data.” PubMed and Web of Science were searched for peer-reviewed articles. The review was conducted following the PRISMA protocol. RESULTS A total of 43 articles were identified for inclusion in the review. Of these, 15 were related to “standardized clinical coding systems,” 15 to “data capture,” and 13 to “reuse of routine patient care data.” Articles related to standardized clinical coding systems focused on the design and/or development of proposed systems, on their evaluation and validation, on their adoption in academic settings, and on user perception. Articles related to data capture addressed the issue of data completeness, evaluated user interfaces and workflow integration, and proposed technical solutions. Finally, articles related to reuse of routine patient care data focused on clinical decision support systems centered on patient care, institutional or population-based health monitoring support systems, and clinical research. CONCLUSIONS While the development of health information systems, and especially standardized clinical coding systems, has led to significant progress in research and quality measures, the vast majority of reviewed articles were published in the US. Clinical decision support systems that reuse EDR data have been little studied. Likewise, few studies have examined the working environment of dental practitioners or the pedagogical value of using health information systems in dentistry.

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.

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.

scholarly journals Health Information Systems Training for a Countrywide Implementation in Uruguay

2009 ◽  
Vol 18 (01) ◽  
pp. 153-157
Author(s):  
A. Vero ◽  
L. Bessonart ◽  
A. Barbiel ◽  
M. Ferla ◽  
A. Margolis
Keyword(s):  
Information Systems ◽  
Health Information ◽  
Health Informatics ◽  
Health Information Systems ◽  
Tertiary Referral ◽  
Tertiary Referral Center ◽  
Active Involvement ◽  
Systems Implementation ◽  
Informatics Training ◽  
Clinical Systems

Summary Objectives Health Information systems training is one of the bottlenecks in clinical systems implementation. In this article, a strategy to massively create and train interdisciplinary coordinating teams is described for a project in Uruguay at FEMI, a non-academic setting which includes 23 health care institutions across the country and a tertiary referral center in Montevideo. Methods A series of educational activities were designed for the local coordinating teams. They included both onsite and online formats, site visits, integrated with some of the project tasks. Results In total, 128 professionals from all the Institutions participated in one or more of the training sessions (onsite and online) and 87 of them accomplished one of the forms of training. Conclusions Massive basic health informatics training was possible in Uruguay through collaboration with academic institutions at the country and regional level. Next steps include an active involvement of nurses in the educational events and planning of massive training of end users.

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.

Types of Resources and their Discover in HealthGrids

2011 ◽  
pp. 30-45 ◽  
Author(s):  
Aisha Naseer ◽  
Lampros Stergiolas
Keyword(s):  
Information Systems ◽  
Health Information ◽  
Health Information Systems ◽  
Resource Discovery ◽  
Healthcare Operations ◽  
Fully Integrated ◽  
Domain Specific ◽  
Critical Data ◽  
Different Types ◽  
Technical Issues

Adoption of cutting edge technologies in order to facilitate various healthcare operations and tasks is significant. There is a need for health information systems to be fully integrated with each other and provide interoperability across various organizational domains for ubiquitous access and sharing. 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. This chapter presents a systematic taxonomy of different types of HealthGrid resources, where the specialized resources can be categorised into three major types; namely, Data or Information or Files (DIF); Applications & Peripherals (AP); and Services. Resource discovery in HealthGrids is an emerging challenge comprising many technical issues encapsulating performance, consistency, compatibility, heterogeneity, integrity, aggregation and security of life-critical data. To address these challenges, a systematic search strategy could be devised and adopted, as the discovered resource should be valid, refined and relevant to the query. Standards could be implemented on domain-specific metadata. This chapter proposes potential solutions for the discovery of different types of HealthGrid resources and reflects on discovering and integrating data resources.

scholarly journals Toward Reducing Health Information Inequities in the Caribbean: Our Experience Building a Participatory Health Informatics Project

2020 ◽  
Vol 30 (Suppl 1) ◽  
pp. 193-202
Author(s):  
Karen Wang ◽  
Ian Hambleton ◽  
Erika Linnander ◽  
Luis Marenco ◽  
Saria Hassan ◽  
...  
Keyword(s):  
Health Disparities ◽  
Information Systems ◽  
Precision Medicine ◽  
Health Information ◽  
Health Informatics ◽  
Health Information Systems ◽  
Caribbean Diaspora ◽  
The Caribbean ◽  
Basic Infrastructure ◽  
Existing Data

Precision medicine seeks to leverage technology to improve the health for all individuals. Successful health information systems rely fundamentally on the integra­tion and sharing of data from a range of disparate sources. In many settings, basic infrastructure inequities exist that limit the usefulness of health information systems. We discuss the work of the Yale Transdis­ciplinary Collaborative Center for Health Disparities focused on Precision Medicine, which aims to improve the health of people in the Caribbean and Caribbean diaspora by leveraging precision medicine approaches. We describe a participatory informatics ap­proach to sharing data as a potential mecha­nism to reducing inequities in the existing data infrastructure.Ethn Dis. 2020;30(Suppl 1):193-202; doi:10.18865/ed.30.S1.193

Strengthening Healthcare Systems and Health Information Systems

2017 ◽  
Author(s):  
Sundeep Sahay ◽  
T Sundararaman ◽  
Jørn Braa
Keyword(s):  
Information Systems ◽  
Health Information ◽  
Health Informatics ◽  
Health Sector ◽  
Systems Design ◽  
Health Information Systems ◽  
Health Systems Strengthening ◽  
Health Coverage ◽  
Public Health Informatics ◽  
Reform Processes

Establishment of health information systems has been a central objective of health sector reform in nearly all LMICs over the last two to three decades. Historically, reform processes have taken introduction of health information systems as inhertently strengthening health sector performance. But today it is more appropriate to talk of health sector strengthening as co-evolving with health information systems strengthening, each reinforcing the performance and reform agendas of the other. The need to build synergies is heightened as there are a multitude of global and national health reform processes underway, like those assoicated with the sustainable development goals or with universal health coverage and each of these have expanded informational needs, requiring robust, flexible, and evolving health information systems. An understanding of the challenges faced by efforts at health systems strengthening helps provide meaningful inputs into health information systems design and vice versa. Such an understanding will enrich public health informatics as an academic discipline, as an area of practice, and as a policy domain.

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 Towards an Integrative Cognitive-Socio-Technical Approach in Health Informatics: Analyzing Technology-Induced Error Involving Health Information Systems to Improve Patient Safety

2010 ◽  
Vol 4 (1) ◽  
pp. 181-187 ◽  
Author(s):  
E.M Borycki ◽  
A.W Kushniruk
Keyword(s):  
Information Systems ◽  
Health Information ◽  
Health Informatics ◽  
System Development ◽  
Improve Patient Safety ◽  
Health Information System ◽  
Health Information Systems ◽  
Technical Aspects ◽  
Information System Design ◽  
The Impact

The purpose of this paper is to argue for an integration of cognitive and socio-technical approaches to assessing the impact of health information systems. Historically, health informatics research has examined the cognitive and socio-technical aspects of health information systems separately. In this paper we argue that evaluations of health information systems should consider aspects related to cognition as well as socio-technical aspects including impact on workflow (i.e. an integrated view). Using examples from the study of technology-induced error in healthcare, we argue for the use of simulations to evaluate the cognitive-socio-technical impacts of health information technology [36]. Implications of clinical simulations and analysis of cognitive-social-technical impacts are discussed within the context of the system development life cycle to improve health information system design, implementation and evaluation.

Sign in / Sign up

Export Citation Format

Share Document