Curriculum Overview

The curriculum for Health Informatics at Aiph University Bhubaneswar is designed to provide a comprehensive and progressive educational experience, combining foundational sciences with practical skills in information technology and clinical practice. The program spans eight semesters and includes core courses, departmental electives, science electives, and laboratory components.

Semester-wise Course Structure

Semester	Course Code	Course Title	Credit (L-T-P-C)	Prerequisites
1	CHM101	Chemistry for Life Sciences	3-0-0-3	-
1	BIO101	Biology Fundamentals	3-0-0-3	-
1	MAT101	Calculus I	3-0-0-3	-
1	CS101	Introduction to Programming	2-0-2-3	-
1	PHY101	Physics for Biological Sciences	3-0-0-3	-
2	MAT201	Calculus II	3-0-0-3	MAT101
2	CHM201	Organic Chemistry	3-0-0-3	CHM101
2	BIO201	Cell Biology	3-0-0-3	BIO101
2	CS201	Data Structures and Algorithms	3-0-0-3	CS101
2	PHY201	Modern Physics	3-0-0-3	PHY101
3	MAT301	Probability and Statistics	3-0-0-3	MAT201
3	BIO301	Genetics and Molecular Biology	3-0-0-3	BIO201
3	CS301	Database Systems	3-0-0-3	CS201
3	ECO101	Economics for Health Informatics	3-0-0-3	-
3	HCI101	Human Computer Interaction	2-0-2-3	CS201
4	MAT401	Linear Algebra and Numerical Methods	3-0-0-3	MAT301
4	BIO401	Biomedical Informatics	3-0-0-3	BIO301
4	CS401	Software Engineering	3-0-0-3	CS301
4	PHYS201	Biophysics	3-0-0-3	PHY201
4	ECO201	Healthcare Economics	3-0-0-3	ECO101
5	CS501	Machine Learning for Healthcare	3-0-0-3	CS401
5	BIO501	Genomic Data Analysis	3-0-0-3	BIO401
5	HCI201	Usability Testing and Evaluation	2-0-2-3	HCI101
5	PHYS301	Medical Physics and Imaging	3-0-0-3	PHYS201
5	CS502	Digital Health Systems	3-0-0-3	CS401
6	CS601	Advanced Data Analytics	3-0-0-3	CS501
6	BIO601	Biostatistics and Clinical Trials	3-0-0-3	BIO501
6	CS602	Clinical Decision Support Systems	3-0-0-3	CS502
6	PHYS401	Healthcare Technology Ethics	3-0-0-3	PHYS301
6	CS603	Healthcare Cybersecurity	3-0-0-3	CS502
7	CS701	Capstone Project I	4-0-0-4	-
7	BIO701	Research Methodology in Health Informatics	2-0-0-2	-
8	CS801	Capstone Project II	4-0-0-4	CS701
8	BIO801	Thesis Writing and Presentation	2-0-0-2	BIO701

Advanced Departmental Elective Courses

The department offers a range of advanced elective courses designed to deepen students' understanding of specific areas within health informatics:

• Machine Learning for Healthcare: This course explores the application of machine learning algorithms in diagnosing diseases, predicting patient outcomes, and optimizing treatment plans. Students learn about neural networks, decision trees, clustering techniques, and their implementation using Python and TensorFlow.
• Public Health Informatics: Students study how health data can be used to monitor population health trends, evaluate public health interventions, and design policies that improve community wellbeing. The course includes exposure to surveillance systems, epidemiological modeling, and health data visualization tools.
• Clinical Data Management: This elective focuses on the principles of managing electronic health records (EHRs), ensuring data integrity, and adhering to regulatory compliance standards such as HIPAA. Students engage in hands-on projects involving EHR implementation and data warehousing.
• Digital Therapeutics: This course delves into how software-based interventions can be used to treat or manage diseases. Topics include mobile health apps, digital therapeutics devices, regulatory frameworks, and clinical validation processes.
• Healthcare Cybersecurity: Students gain insight into protecting sensitive health data from cyber threats, implementing secure communication protocols, and ensuring privacy compliance. The course covers encryption methods, threat detection, incident response strategies, and risk assessment frameworks.
• Bioinformatics and Genomic Data Analysis: This course introduces students to computational tools for analyzing genomic sequences, understanding genetic variations, and applying bioinformatics techniques in personalized medicine. Students work with databases like NCBI and Ensembl to perform sequence alignment and annotation tasks.
• Medical Imaging and Visualization: Focused on the principles of image acquisition, processing, and interpretation in diagnostic medicine, this course provides practical training in radiology, MRI, CT scans, and ultrasound imaging. Students also learn about image enhancement techniques and computer-assisted diagnosis tools.
• Health Information Systems Design: This course explores the design and development of scalable health information systems that integrate workflow management with data analytics. Students study system architecture, user interface design, database integration, and performance optimization.

Project-Based Learning Philosophy

The department strongly believes in project-based learning as a core component of the educational experience. Through this approach, students are encouraged to apply theoretical knowledge to real-world challenges, fostering innovation and critical thinking skills.

Mini-projects begin in the third semester, where students work on small-scale tasks under faculty supervision. These projects help build foundational research and analytical skills while exposing students to industry practices. The final-year thesis or capstone project is a comprehensive endeavor that allows students to explore an area of personal interest within health informatics.

Projects are selected based on student interests, faculty expertise, and available resources. Each project has a dedicated mentor who guides the team throughout the research process, from problem identification to solution development and presentation. Evaluation criteria include creativity, technical execution, clarity of communication, and impact potential.