AIM 5030 - Introduction to Artificial Intelligence in Medicine

3 Credits

This course provides a comprehensive introduction to the fundamental concepts and methods of Artificial Intelligence, with a particular emphasis on applications in medicine and healthcare. Students will explore how intelligent systems perceive their environment, make decisions, learn from data, and reason under uncertainty. The course covers foundational AI topics including intelligent agents, classical search, adversarial decision-making, constraint satisfaction, knowledge representation, logical reasoning, automated planning, and probabilistic inference. Throughout the semester, students will gain hands-on experience implementing core AI algorithms and applying them to real-world problems. By integrating theory with practical examples, students will develop a solid understanding of how AI systems can support diagnostics, decision support, and intelligent automation.

AIM 5430 - Reinforcement Learning for Clinical Decision Making

3 Credits

This course introduces the theory and application of Reinforcement Learning (RL) for improving clinical decision making in healthcare. Students will learn how sequential decision processes can be modeled using Markov Decision Processes (MDPs) and solved using modern RL algorithms such as dynamic programming, temporal-difference learning, Q-learning, and policy gradient methods. The course emphasizes challenges unique to healthcare limited data, partial observability, delayed outcomes, safety, fairness, interpretability, and clinician-in-the-loop design. Through a combination of lectures, hands-on programming, and case studies, students will analyze observational clinical datasets, build RL models for treatment optimization, and critically evaluate the reliability and ethical implications of deploying RL in real-world clinical settings. By the end of the course, students will be able to design, implement, and assess RL-based decision support tools tailored for medical applications.

AIM 5530 - Telehealth & Telemedicine

3 Credits

This course introduces students to the principles, technologies, and applications of telehealth and telemedicine in modern healthcare. Students will explore the infrastructure, regulatory frameworks, and digital tools that enable remote clinical care, patient monitoring, and health data exchange. Emphasis is placed on integrating telehealth into healthcare delivery systems, evaluating clinical outcomes, and understanding the ethical, legal, and privacy considerations associated with digital health. The course combines theoretical knowledge with practical insights into designing, implementing, and managing telemedicine programs across diverse clinical contexts.

AIM 5630 - AI for Precision Medicine, Genomics & Diagnostics

3 Credits

This course explores the application of artificial intelligence (AI) and machine learning (ML) techniques in precision medicine, genomics, and clinical diagnostics. Students will learn to analyze high-dimensional biological and clinical data, develop predictive models for disease risk, progression, and treatment response, and integrate multi-omics and electronic health record (EHR) data for personalized healthcare solutions. The course emphasizes ethical, regulatory, and interpretability considerations, preparing students to design AI-driven solutions that improve diagnosis, prognosis, and therapeutic decision-making.

AIM 5910 - Graduate Internship

1-3 Credits (Repeatable up to 6 credits)

Graduate Internship.

AIM 5960 - Capstone Experience

3 Credits

This course provides students in the Artificial Intelligence in Medicine program with a structured, experiential learning opportunity in an industry or clinical setting. Students apply AI methodologies to real-world healthcare challenges, gain insight into the interdisciplinary roles within data science and healthcare innovation teams, and develop professional networks that support career advancement and leadership in AI-driven medicine.

HDS 5000 - Foundations in Health Data Science

3 Credits

This dynamic, innovative course immerses first-semester graduate students in the rapidly evolving world of health data science. Focusing on real-world healthcare challenges, students will explore introduction to data, visualization, statistics and analytics, machine learning, and artificial intelligence. Delve into critical topics like personalized medicine, population health trends, and real-time clinical decision-making. Ethical use of artificial intelligence, data privacy, and regulatory frameworks will also be explored, preparing you to be a leader in the future of data-driven healthcare.

HDS 5130 - Healthcare Organization, Management, and Policy

3 Credits

This course offers students a comprehensive exploration of health policy and the U.S. healthcare system, with a focus on recent reforms and emerging trends. It equips students with the knowledge and tools to navigate the evolving healthcare landscape, covering the organization, financing, and regulation of healthcare while critically evaluating key policy initiatives. Special attention is given to the impact of the Affordable Care Act (ACA), ongoing healthcare reforms, and developments such as value-based care, health equity initiatives, and digital health legislation. Students will analyze the challenges of access, cost, and quality in the U.S. healthcare system and explore innovative solutions to improve care delivery and outcomes. Through interactive discussions, case studies, and policy simulations, students will apply evidence-based strategies to real-world scenarios, preparing them to lead and advocate for future healthcare reforms.

HDS 5210 - Programming for Health Data Scientists

3 Credits

Students will be introduced to concepts in computer programming using the Python programming language. Students will learn to conceptualize steps required to perform a task, manipulate files, create loops, and functions. By the end of this course, students will have a basic understanding of computer programming, a working knowledge of the Python programming language, and they will be able to share their scripts to collaborate with other team members.

Attributes: BME Graduate Elective, MPH-Biostatistics, Grad Pol Sci Skills, Social Work PhD Specilization

HDS 5230 - High-Performance Computing and Health Artificial Intelligence

3 Credits

This course explores the introduction of high-performance computing (HPC) and advanced artificial intelligence (AI) in addressing complex health data challenges. Students will gain hands-on experience with scalable computing platforms and AI-driven methodologies to analyze large-scale health data sets. Emphasis will be placed on optimizing computational workflows, deploying AI solutions in real-world health settings, and ensuring ethical and equitable applications in healthcare innovation.

Prerequisite(s): HDS 5310; HDS 5210

Attributes: Social Work PhD Specilization

HDS 5310 - Analytics, Statistics & Visualization Methods in Health Data Science

3 Credits (Repeatable for credit)

This course offers a modern and immersive introduction to analytics, statistics, and visualization methods utilizing Python, SAS, and R programming, tailored for the fast-evolving field of health data science. Through engaging, hands-on projects and real-world applications, students will develop not just a basic understanding of programming but the practical skills to leverage Python, SAS, and R for data manipulation, automation, and collaboration within healthcare settings. This course also emphasizes innovative approaches, including cloud-based coding environments and collaborative tools like GitHub, enabling students to work on team-based projects and share code seamlessly. By the end of the course, students will be able to build functions, automate tasks, and work efficiently in data-driven health research.

Attributes: Bioinformatics & Comp Bio Elec, MPH-Epidemiology, MPH-Health Management & Policy, Social Work PhD Specilization

HDS 5320 - Inferential Modeling

3 Credits

Students will learn to conceptualize research questions as statistical models, and parameterize those models from real-world data. The course will start by introducing the linear model, then expand into generalized linear models, nonlinear models, mixed and multilevel models, and Cox survival models. Students will have a working knowledge of how to use statistical models to gain an understanding of the influence of individual predictor variables on health outcomes.

Prerequisite(s): HDS 5310

HDS 5330 - Predictive Modeling and Health Machine Learning

3 Credits

This course will focus on the application of sophisticated machine learning models and statistical techniques to healthcare data. Students will explore algorithms such as regression, decision trees, ensemble methods, neural networks among other deep learning methods. Emphasizing the unique challenges of healthcare data, the course addresses high-dimensionality, missing values, ARIMA, classification, clustering, and visualization equipping students with strategies to optimize model performance and reliability. Through hands-on work with real-world datasets, students will develop the skills to design, implement, and evaluate advanced machine learning models while effectively communicating results to technical and non-technical audiences to support innovation and decision-making in healthcare.

Attributes: AI Applications, Bioinformatics & Comp Bio Elec, MPH-Epidemiology, MPH-Biostatistics, Social Work PhD Specilization

HDS 5430 - Image Processing and Deep Learning Diagnostics

3 Credits

This level course equips students with advanced skills in visualizing and analyzing complex health data, integrating both traditional data visualization techniques and modern approaches such as image processing and deep learning. Students will learn to transform raw health data, including clinical and imaging data, into meaningful and interactive visual representations that support data-driven decision-making and diagnostics. The course covers image segmentation and edge detection, visual analytics, and dashboard design, alongside innovative applications of deep learning for medical image analysis and diagnostics. Additionally, deep learning predictive modeling techniques will cover CNN, RNN, and LSTM. By the end of the course, students will be proficient in using advanced visualization tools and deep learning techniques to effectively present and interpret health data for diverse healthcare stakeholders.

HDS 5530 - Natural Language Processing in Medicine

3 Credits

This graduate-level course offers an in-depth exploration of Natural Language Processing (NLP) and the application of large language models (LLMs) in the healthcare sector. Students will tackle the complexities of processing unstructured clinical text, medical records, and other health-related documents using advanced NLP techniques. Core topics include text mining, named entity recognition, sentiment analysis, and document classification, with a focus on implementing innovative LLMs like Generative Pre-trained Transformers (GPT) for tasks such as clinical note summarization, patient data extraction, and decision support. Additionally, this course will cover concepts of Generative Adversarial Network (GANs) and diffusion models and their applications in healthcare analytics. The course also addresses ethical concerns, data privacy, and the transformative impact of NLP on patient care and health outcomes. By the end of the course, students will have the expertise to design, develop, and apply NLP models to solve critical healthcare challenges.

HDS 5910 - Graduate Internship

1-3 Credits (Repeatable up to 6 credits)

Graduate Internship.

HDS 5930 - Special Topics

3 Credits (Repeatable for credit)

HDS 5960 - Capstone Experience

3 Credits

This course is designed to offer data science students an opportunity to practice their skills in an industry setting, to learn the roles that various members of a data science team play in an organization, and to begin building a network of professional contacts and references.

Prerequisite(s): ORES 5300; HDS 5210; HDS 5310

HDS 5980 - Graduate Independent Study in Health Data Science

1 or 3 Credits (Repeatable for credit)

HI 5210 - Health Privacy, Security, and Regulatory Compliance

3 Credits

This course provides an in-depth exploration of the legal, ethical, and technical frameworks that govern health information privacy, data security, and regulatory compliance. Students will examine HIPAA, HITECH, CMS requirements, state-level regulations, and emerging policies related to digital health. Through case studies and applied exercises, learners will assess risks, evaluate compliance strategies, and analyze safeguards that uphold confidentiality, integrity, and availability of health data across clinical and administrative settings.

HI 5220 - Clinical Decision Support & Quality Improvement

3 Credits

This course examines the principles, design, implementation, and evaluation of Clinical Decision Support (CDS) systems and their role in improving quality, safety, and efficiency in healthcare. Students will explore CDS types, evidence-based guideline integration, data-driven decision tools, and the relationship between CDS, clinical workflows, and quality improvement frameworks. Through hands-on examples and analysis of real-world systems, learners will gain the skills to design effective CDS interventions that enhance clinical outcomes..

HI 5260 - Electronic Health Record Implementation and Management

3 Credits

This course prepares students to understand, evaluate, and support the implementation and optimization of Electronic Health Record (EHR) systems. Students will explore EHR architecture, data standards, interoperability, workflow integration, vendor selection, and change management. Emphasis is placed on user-centered design, implementation planning, system testing, training, and post-go-live evaluation to ensure safe, efficient, and high-quality clinical operations.

HI 5910 - Health Informatics Internship

1-3 Credits (Repeatable up to 3 credits)

The Health Informatics Internship is a supervised, experiential learning opportunity that enables students to apply health informatics theory, data management principles, and health information technologies in a real-world healthcare, public health, or industry setting. Under professional mentorship, students engage in projects related to electronic health records, data governance, clinical decision support, quality improvement, interoperability, analytics, or health systems optimization. The internship fosters interdisciplinary collaboration, professional skill development, and practical experience necessary for leadership roles in health informatics and healthcare innovation.

HI 5960 - Capstone Experience

3 Credits

This course provides students in the Health Informatics program with a structured, experiential learning opportunity in a healthcare, public health, or industry setting. Students apply health informatics principles, data governance frameworks, and health information technologies to address real-world challenges in clinical operations, population health, and healthcare systems improvement. Through hands-on engagement, students gain insight into interdisciplinary team roles across clinical, technical, and administrative domains while building professional networks that support career advancement and leadership in health informatics.an industry setting, to learn the roles that various members of a data science team play in an organization, and to begin building a network of professional contacts and references.

ORES 5010 - Introduction to Biostatistics for Health Outcomes

3 Credits

This course is designed to introduce basic principles of descriptive and inferential statistics. The course will cover fundamental concepts and techniques of descriptive and inferential statistics with application to health outcomes research. This course contributes to the First Dimension by preparing students for advanced study in areas related to Outcomes Research and contributes to the Second Dimension by teaching students tools and methods of research.

Attributes: Health & Rehab Sci Research

ORES 5100 - Research Methods in Health & Medicine

3 Credits

This online course is designed to provide an introduction to the techniques, methods, and tools used for research in the health sciences. Students will obtain an understanding of the research process and scientific method, specific study designs, methods for data collection and analysis. This is a very applied and hands-on course and is focused entirely on the unique aspects of research in the health sciences. This course will utilize Blackboard for all lectures, online discussions, assignment submission, and examinations.

Attributes: Aviation Elective (Graduate), Aviation Research (Graduate), Health & Rehab Sci Research

ORES 5160 - Data Management and Programming in Healthcare

3 Credits

This course provides essential skills for maintaining databases, ensuring data quality, and manipulating data effectively, with a strong focus on practical applications in Python, R, SQL, and cloud computing. Students will engage in hands-on experiences in database design and management, learning to navigate modern data environments relevant to health outcomes research. The course emphasizes the integration of current technologies and best practices in health data management and storage. By fostering proficiency in data tools and methodologies, this course contributes to the development of critical data management skills essential for addressing contemporary challenges in healthcare delivery.

Attributes: MPH-Epidemiology, MPH-Global Health, MPH-Health Management & Policy, Social Work PhD Specilization

ORES 5210 - Foundations of Medical Diagnosis and Treatment

3 Credits

Taught by medical school faculty, this course in an introduction to clinical medicine for graduate students. Basic science concepts include anatomy, physiology, microbiology/hematology, infectious diseases, genetics, immunology, endocrinology and metabolic pathways. Primary organ systems and their associated diseases will also be covered, with special emphasis on their diagnosis and treatment.

ORES 5260 - Pharmacoepidemiology

3 Credits

This course is an introduction to pharmacoepidemiology - the use and effects of drugs in human populations. It provides an overview of the principles of pharmacoepidemiology, sources of pharmacoepidemiology data, and special issues in pharmacoepidemiology methodology. It reviews commonly used study designs, special topics and advanced methodologies for pharmacoepidemiologic studies.

Attributes: MPH-Maternal & Child Health

ORES 5300 - Foundations of Health Outcomes Research

3 Credits

This course introduces students to the methodologies, scientific writing and resources, and data collection processes fundamental to health outcomes research, health measurement, establishing a foundation for evidence-based decision-making in healthcare. Students will explore a range of research designs—learning to select methodologies that best align with specific research objectives and constraints. A major focus will be on ICD codes, clinical terms, data collection techniques, and observational data gathering. Through hands-on projects, students will gain practical experience in designing data collection instruments, evaluating measurement validity and reliability, and addressing challenges like sampling bias and data quality. By the end of the course, students will possess a comprehensive understanding of how to collect, evaluate, and manage data effectively to conduct rigorous outcomes research capable of driving healthcare improvements.

Attributes: Health & Rehab Sci Research, Social Work PhD Specilization

ORES 5320 - Scientific Writing and Communication

3 Credits

The purpose of this course is to take students step-by-step through the process of writing a journal article appropriate for publication in a scientific journal. We will focus on each section of the article for several weeks as students complete assignments related to successfully writing the section and receive feedback on weekly assignments. The last part of the course will focus on taking the research findings presented in the journal article and preparing a poster that could be presented at a research conference. Overall, students will improve their ability to communicate complex research findings in writing to their peers via publication in the peer-reviewed literature and to the broader scientific community through presentation of a poster.

Attributes: MPH-Behavior Sci & Health Equi, MPH-Epidemiology, MPH-Biostatistics

ORES 5400 - Pharmacoeconomics

3 Credits

Pharmacoeconomics is one of the cornerstones of Health Outcomes Research. This course is designed to teach clinicians and new researchers how to incorporate pharmacoeconomics into study design and data analysis. Participants will learn how to collect and calculate the costs of different alternatives, determine the economic impact of clinical outcomes, and how to identify, track and assign costs to different types of health care resources used. This is a required course for the MS in Outcomes Research and Evaluation Sciences but may also be of interest to students in Public Health and Health Administration. This course contributes to the First Dimension by providing students with advanced skills in highly valued research area and contributes to the Second Dimension by developing students’ ability to effectively communication complex information.

ORES 5410 - Evaluation Sciences

3 Credits

This course will examine methods for evaluation of health programs in both organizational and community contexts. Topics include formative research, process evaluation, impact assessment, cost analysis, monitoring outcomes, and evaluation implementation. Strengths and weaknesses of evaluation designs will be discussed. This is a required course for the MS in Outcomes Research and Evaluation Sciences Program but may also be of interest to students in Public Health, Health Administration, and Allied Health. This course contributes to the First Dimension by providing students with advanced skills in the evaluation sciences and contributes to the Second Dimension by developing students’ ability to effectively communicate complex statistical information.

ORES 5430 - Health Outcomes Measurement

3 Credits

This course is designed to introduce students to the principles of health outcomes measurement. Specifically, students will be introduced to the most common measures seen in health outcomes and health services research as well as measure development and assessment of psychometric properties. Topics will include generic vs. disease specific measures, instrument design, scaling, reliability and validity, addressing measurement error, Classical Test Theory, and Item Response Theory. This course contributes to the First Dimension by providing students with advanced skills in a highly valued research area and contributes to the Second Dimension by developing students' ability to effectively communicate complex statistical information.

Attributes: Health & Rehab Sci Research, MPH-Behavior Sci & Health Equi, MPH-Epidemiology, MPH-Global Health, MPH-Health Management & Policy, MPH-Maternal & Child Health, MPH-Biostatistics, Social Work PhD Specilization

ORES 5440 - Comparative Effectiveness Research

3 Credits

This course is designed to introduce students to the principles of comparative effectiveness research. Specifically, students will be introduced to the concept of comparative effectiveness research, common research methods and statistical analyses, and translation and dissemination. This course contributes to the First Dimension by providing students with advanced skills in a highly valued research area and contributes to the Second Dimension by developing students' ability to effectively communicate complex statistical information.

ORES 5520 - Economic Evaluation in Health Services Research

3 Credits

This course equips students with the analytical approaches used to evaluate the economic dimensions of health services and clinical outcomes research. Emphasis is placed on understanding how costs, resource use, and patient-centered outcomes interact within healthcare delivery systems. Students will engage with methods that quantify the value of interventions, compare alternative care strategies, and assess the consequences of clinical and organizational decisions on both health outcomes and system performance. Through applied exercises, examination of real-world evaluations, and development of structured analyses, learners will gain expertise in cost-effectiveness, cost-utility, and cost-benefit methodologies as they apply to health services research. By the conclusion of the course, students will be able to generate, interpret, and communicate economic evidence that informs quality improvement, population health initiatives, and policy decisions aimed at optimizing patient outcomes and healthcare system impact.

ORES 5530 - Implementation Science in Health Services Research

3 Credits

This course provides students with a comprehensive understanding of implementation science principles within health services research. Students will explore frameworks, strategies, and methodologies for translating evidence-based interventions into clinical and organizational practice. The course emphasizes evaluating the effectiveness, sustainability, and scalability of health interventions while addressing contextual, organizational, and policy-level barriers. Through case studies, hands-on exercises, and critical appraisal of current literature, students will develop the skills necessary to design, implement, and evaluate interventions that improve health outcomes and service delivery.

ORES 5550 - SAS Programming I

1 Credit

In the era of big data and outcomes research, skilled scientists can organize, manipulate, and analyze using many different tools. Programming in SAS is an essential skill. This course introduces the SAS environment and programming language. Students will learn data management, descriptive analysis, and statistical inference testing using a hands-on approach. By the end of the course, students will be able to import, organize, and analyze data as well as interpret the results.

Prerequisite(s): (ORES 5010, BST 5000, or BST 5020)

ORES 5910 - Graduate Internship

1-3 Credits (Repeatable up to 6 credits)

Graduate Internship.

ORES 5970 - Research Topics in Outcomes Research

0-3 Credits (Repeatable for credit)

ORES 5980 - Graduate Independent Study in Outcomes Research

1-3 Credits (Repeatable up to 6 credits)

ORES 6950 - Special Study for Exams

0 Credits (Repeatable for credit)

This Special Study for Exams course indicates that a student will be taking the exams the semester they are registered for.

ORES 6970 - Advanced Research Topics in Outcomes Research

1-3 Credits

ORES 6980 - Graduate Independent Study in Outcomes Research

0-3 Credits

ORES 6990 - Dissertation Research

0-6 Credits (Repeatable for credit)