Elective Course

Elective Course At least 5 courses, including course that you have taken

Course description

Common practices in health services, operation in the clinical setting, quality of health care organization, clinical process and judgment; terminologies commonly used in public health field, health practices, and medical diagnosis

Objectives of the subject are

Know the objectives of using the medical terminology
Decoding the medical terms by using root , prefix, suffix and conjugation
Develop skill to memorize and grouping words by many methods e.g. organ oriented groups, disease orientated groups, Eponyms.
Know the meanings and how to use the public health related words as well as the standard medical terminology abbreviations properly.

LO: Learning Outcome

LO1: Know the objectives of using the medical terminology.

LO2: Decode the medical terms by using root , prefix, suffix and conjugation.

LO3: Develop skill to memorize and grouping words by many methods e.g organ oriented groups, disease orientated groups, Eponyms.

LO4: Know the meanings and how to use the public health related words as well as the standard medical terminology abbreviations.

Course description

Overview of computer architecture, data organization, structure of programming languages, networking and data communication

LO: Learning Outcome

LO1: Explain tools and terminologies related to the public health informatics

LO2: Apply public health informatics principles, tools, methodologies to improve public health practices

LO3: Discuss the role of information system in public health practices

LO4: Identify key characteristics of decision support system

LO5: Discuss the impact of developments in Electronic Medical Records, e-health, and m-health technologies for improving population health

LO6: Analyze challenges in the development of public health information system, and their solutions

Course Description

Principles of good data management, case record form design, data management process, data management tool; data validation techniques

Course Objectives

1. Describe principles of good data management practices
2. Design appropriate case record form for effective data collection
3. Discuss data management processes in clinical research
4. Apply appropriate data management tools to assist clinical data management
5. Use data validation and data handling techniques to appropriately prepare data for analysis


Disease mapping of tropical diseases by Geographical Information System (GIS), Remote Sensing (RS), and Global Positioning System (GPS).


LO1: Describe concepts of Geographical Information system (GIS) and its application to Public health
LO2: Identify tools in GIS that can be used to study disease distribution
LO3: Create maps showing disease distribution using appropriate GIS program
LO4: Select appropriate functions in GIS programs to explore factors related to spatial distribution of disease
LO5: Interpret and present results of disease mapping in lay language


Concepts of data presentation and data visualization, human perception to graphics and color; appropriate graphic designs for data presentation; business intelligence techniques to transform health data into meaningful presentation, information dashboard to assist decision making


Describe key concepts in data visualization, including graphical perception, color and visual interaction
Identify important tools used for development of business intelligence platform
Prepare data for business intelligence platform
Develop a business intelligence platform (dashboard) for decision making


Spatial epidemiology, sampling and analysis; Cluster analysis; Surface analysis; Spatial autocorrelation and regression


LO1: Describe concepts of spatial epidemiology
LO2: Select appropriate statistical methods for spatial analysis
LO3: Demonstrate analytical techniques for spatial analysis
LO4: Interpret results and outcomes from spatial analysis


Modern methods used to analyze data into useful decision making information; various techniques for data exploration and data manipulation; data characteristics in terms of their patterns, correlation, and constraints; predictive models, association rules, decision tree analysis and cluster analysis; examples of applying data mining to improve operations and decisions in healthcare services


LO1: Describe the overall data mining process
LO2: Discuss major theories, practices, and techniques associated with the data mining process
LO3: Apply appropriate data mining techniques
LO4: Interpret results obtained from data mining techniques applied
LO5: Evaluate models using data mining techniques


Relevant mathematical modeling concept and its application in epidemiological studies; differential equations; R  Programming; mathematical model to answer research questions; recent published modeling articles; economics modeling and cost-effectiveness analysis to link any conclusion on modeling interventions to health policy program


Interesting topics in Biomedical and Health Informatics, literature review, principles of critical appraisal of published literature; examples of health information systems in different settings