Progressive Usage of Business and Spatial Intelligence for Decision Support in the Delivery of Educational Services in Developing Countries

2013 ◽  
pp. 71-91
Author(s):  
Patricia E. Nalwoga Lutu
Keyword(s):  
Developing Countries ◽  
Decision Support ◽  
Educational Services ◽  
Spatial Intelligence

An Intelligent Computer-Assisted Instruction System Designed for Rural Health Workers in Developing Countries

1992 ◽  
Vol 31 (03) ◽  
pp. 193-203 ◽  
Author(s):  
B. Auvert ◽  
V. Gilbos ◽  
F. Andrianiriana ◽  
W. E. Bertrand ◽  
X. Emmanuelli ◽  
...  
Keyword(s):  
Developing Countries ◽  
Decision Support ◽  
Rural Health ◽  
Health Workers ◽  
Computer Assisted Instruction ◽  
Computer Assisted ◽  
Instruction System ◽  
Knowledge Based ◽  
Assisted Instruction ◽  
Intelligent Computer

Abstract:This paper describes an intelligent computer-assisted instruction system that was designed for rural health workers in developing countries. This system, called Consult-EAO, includes an expert module and a coaching module. The expert module, which is derived from the knowledge-based decision support system Tropicaid, covers most of medical practice in developing countries. It allows for the creation of outpatient simulations without the help of a teacher. The student may practice his knowledge by solving problems with these simulations. The system gives some initial facts and controls the simulation during the session by guiding the student toward the most efficient decisions. All student answers are analyzed and, if necessary, criticized. The messages are adapted to the situation due to the pedagogical rules of the coaching module. This system runs on PC-compatible computer.

Operator models for delivering municipal solid waste management services in developing countries: Part B: Decision support

2017 ◽  
Vol 35 (8) ◽  
pp. 842-862 ◽  
Author(s):  
Reka Soós ◽  
Andrew D Whiteman ◽  
David C Wilson ◽  
Cosmin Briciu ◽  
Sofia Nürnberger ◽  
...  
Keyword(s):  
Developing Countries ◽  
Decision Support ◽  
Waste Management ◽  
Municipal Solid Waste ◽  
Solid Waste ◽  
Solid Waste Management ◽  
Municipal Solid Waste Management ◽  
Management Services

Implementing Decision Support Systems in Developing Countries

1989 ◽  
Vol 89 (7) ◽  
pp. 21-26 ◽  
Author(s):  
Ming‐te Lu ◽  
Ching‐cha Hsieh ◽  
Chien‐chun Pan
Keyword(s):  
Developing Countries ◽  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems

scholarly journals Applying spatial intelligence for decision support systems

2018 ◽  
Vol 3 (2) ◽  
pp. 384-390 ◽  
Author(s):  
Amira M. Idrees ◽  
Mohamed H. Ibrahim ◽  
Ahmed I. El Seddawy
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Spatial Intelligence

Clinical Decision Support System Adoption & Implementation in African Health Systems: A SWOT Analysis. (Preprint)

2021 ◽  
Author(s):  
Victoria Oluwafunmilola Kolawole
Keyword(s):  
Developing Countries ◽  
Decision Support ◽  
Health Systems ◽  
Clinical Decision Support ◽  
Clinical Decision Support System ◽  
Swot Analysis ◽  
Clinical Decision ◽  
Developed Countries ◽  
Evidence Based ◽  
African Health

BACKGROUND The clinical decision support system (CDSS) has been an important achievement of health technology in the 21st century. In developed countries, it has transformed the way health services are being delivered and has shown to be a tool that reduces medical errors and misdiagnoses in Healthcare. However, CDSS remains underutilized in developing countries in Africa. OBJECTIVE This study aims to review the literature to improve our understanding of the “strengths, weaknesses, opportunities and threats (SWOT)” associated with CDSS implementation in African health systems. METHODS This study included a literature review conducted in PubMed with a total of 19 articles between the year 2010 to date (past 10years) reviewed for key themes and categorized into one of 4 possible areas within the SWOT analysis. RESULTS Articles reviewed showed common strengths of efficiency at the workplace, Improved healthcare quality, benefits in developed countries, good examples of evidence-based decision making. unreliable electric power supply, inconsistent Internet connectivity, clinician's limited computer skills, and lack of enough published evidence of benefits in developing countries are listed as a weakness. The opportunities are high demand for evidence-based practice in healthcare, a strong demand for quality healthcare, growing interest to use modern technologies. The common threats identified are government policy, political instability, low funding and resistance of use by providers. CONCLUSIONS There’s the need to work on the technical, organizational and financial barriers to ensure high adoption and implementation of the CDSS in African Health systems. Also, the lag on the knowledge available on its impact in developing countries must be worked on by supporting more studies to add to the body of knowledge.

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