I- INTRODUCTION TO HEALTH INFORMATICS (2 CREDITS)
• Introduction to fundamental healthcare IT concepts (12 contact hours)
Course Description:
This unit provides students with the basic ideas of how information is modelled to facilitate easy access to knowledge. The concepts of data, information and knowledge are defined and their interconnections explained in order to introduce the foundations of health informatics. These include data management using database systems, healthcare data warehousing and overview of current challenges in use of healthcare data.
• History of healthcare IT, overview of current and future (12 contact hours)
Course Description:
This unit presents a brief history of the healthcare informatics field emphasizing long recognized key capabilities and the reasons for their relatively slow adoption relative to other sectors.
It identifies factors that helped create and sustain this new field, the key players involved, and the impact health information technology (HIT) is having on the delivery of care in a rapidly changing healthcare field. An overview of the most prominent HIT applications both in public health and clinical areas is presented as well as the dynamics of its evolution and the awaited potential impact in the future.
• Introduction to healthcare data analytics (3 contact hours)
Course Description:
This unit teaches students how to use biomedical analytics methods to improve patient care and make healthcare systems more efficient. It covers clinical intelligence and the role of biomedical analytics, including dashboards, in supporting adaptable data-driven healthcare systems. The unit also examines epidemiological concepts in healthcare analytics and their application to patient and population outcomes research, including basic concepts in health statistics and epidemiology, as well as trend analysis techniques and approaches for statistical prediction and classification. The unit includes also a quick introduction to the use of popular business intelligence software tools.
• Healthcare information standards and terminologies, health information exchange, integration and interoperability (3 contact hours)
Course Description:
This unit provides concepts and practical examples of health care information interoperability, standard terminologies, messaging standards, health information exchanges (HIEs), and projects deploying these capabilities. Topics covered by the unit include the importance of standards; information architecture and application programming interfaces (APIs). Core principles, challenges, benefits, and limitations will be discussed in each of these topics.
II- Introduction to Artificial Intelligence (3 credits)
• Fundamental concepts and computational models of Artificial Intelligence (6 contact hours )
Course Description:
The goal of this unit is to introduce the underlying concepts and foundational methods in artificial intelligence (AI) which apply to healthcare use. This includes defining and showcasing the major domains and subdomains of AI, presenting a number of popular computational methods in AI, the different forms of learning from datasets, knowledge representation techniques, and illustrating their use in a number of examples.
• Introduction to Machine Learning and predictive models lifecycle (15 contact hours)
Course Description:
This unit will introduce the principles, algorithms, and applications of machine learning from the point of view of modeling and prediction. It covers the methodologies related to predictive model lifecycle, from data collection and preparation, to training, evaluation, implementation and monitoring. It also includes formulation of learning problems and concepts of representation, over-fitting, and generalization, that can affect model performance.
• Introduction to knowledge representation and rules-based reasoning (6 contact hours)
Course Description:
The unit will cover the basics of knowledge representation (in particular using ontologies and semantic web) and rules-based reasoning. Advantages and limitations of these techniques will be explained, in addition to their interesting use cases, especially in clinical practice guidelines modeling.
• Natural Language Processing and Large Language Models (6 contact hours)
Course Description:
This unit aims to familiarize the students with applications of the Natural Language Processing and text mining in health care, providing a short introduction to commonly used algorithms, techniques, and software. It will showcase a number of existing healthcare applications including clinical records and narratives.
• Artificial Intelligence challenges: biases, explainability, generalizability (12 contact hours)
Course Description:
This unit will introduce the main challenges and shortcomings of AI-based models and systems, namely: biases and fairness, lack of explainability and limited generalizability. It will also propose best practices and mitigation methods that could be used to tackle such challenges when designing AI-based applications.
III- Applications of healthcare informatics (3 credits)
• Clinical Information Systems: from digitalization to clinical transformation (9 contact hours)
Course Description:
This unit covers clinical information systems used in healthcare institutions. Topics focus on system functionality required to support care in inpatient and outpatient settings and associated data and workflows, in order to leverage these systems to ensure not only a digitalization of records, but a true clinical transformation of practices. This unit is designed to provide an understanding of electronic health record components, levels of maturity, types of interfacing and integration, and observed operational challenges.
• Applications for healthcare quality and safety (6 contact hours)
Course Description:
This unit will explore the use of technology to improve quality of care and patient safety and potentially impact patient outcomes. Students will learn how the latest developments in health informatics can be applied to improve patient care, prevent clinical errors, and reduce adverse events. Topics covered will include the use of data mining through the global triggers tools (GTT) methodology to identify potential adverse events in healthcare institutions, in addition to the application of artificial intelligence (AI) in predicting and managing patients at high risk for adverse events.
• Applications for patient education and engagement (6 contact hours)
Course Description:
This unit will introduce the emerging application area for patient empowerment through technology, the aim of which is to encourage patients to better manage and influence their health and wellness, access healthcare services, and improve interactions with their caregivers by leveraging digital health solutions and services. Topics include solutions that emphasize the patient experience, the use of innovative wearables, and behavioral management mobile applications to improve adherence to therapy and decision support. Additional topics include the management of patient generated healthcare data and the evolution of patient/provider inter-activity.
• Applications for healthcare management (6 contact hours)
Course Description:
This course will explore the use of technology to tackle challenges in healthcare managerial processes. Topics covered will include the use of data-mining and artificial intelligence based indicators and dashboards to monitor certain processes in hospitals and identify delays, trends in patient outcomes, operational performance indicators, public health and population health indicators, and measure adherence to certain practice guidelines.
• Application to healthcare professional’s education and acquisition of clinical knowledge and practices (6 contact hours)
Course Description:
This course will explore the use of technology to improve education in healthcare. Topics covered will include the use of simulation (semi-immersive and full immersion scenarios), Virtual Reality and Augmented Reality, and how to enhance learning through using of Large Language Models-based applications (such as chatGPT ) for access to knowledge bases, in addition to other AI practical applications beneficial to the learning process. Students will gain an understanding of how technology can be used to create realistic simulations and provide efficient learning experiences.
• Telehealth, mHealth, Internet of Things and Wearables (6 contact hours)
Course Description:
This course will explore the use of technology to improve healthcare delivery through telehealth, mHealth, Internet of Things (IoT), and wearables. Topics covered will include the use of telehealth to provide remote medical consultations, the use of mHealth to monitor patient health and provide personalized care, the application of IoT to connect medical devices and improve patient outcomes, and the use of wearables to track patient health and acute problems.
Students will learn about the latest developments in the field and the challenges and opportunities related to these technologies.
• Applications and tools for clinical and epidemiological research (6 contact hours)
Course Description:
This unit will explore the use of technology to support clinical and epidemiological research, with a focus on the use of big data and data warehousing techniques. Topics covered will include the use of electronic health records (EHRs) to collect and analyze large volumes of patient data, the application of data mining and machine learning techniques to identify patterns and trends in health data, and the use of data warehousing to store, manage, and analyze large datasets. Students will gain an understanding of how technology can be used to collect, analyze, and interpret large volumes of health data.
IV- Healthcare IT systems design lifecycle (2 credits)
• Clinical Decision Support Systems lifecycle: from model design to a usable clinical tool (18 contact hours)
Course Description:
This unit addresses how decisions are made in healthcare, why decision-making goes wrong and how technology can help improve the decision process and reduce potential clinical errors. The module also explores the best practices for the design, validation and implementation of computer-based decision support systems (CDSS), as well as with methods for assessing their impact in clinical practice.
This unit then looks at clinical decision support systems in a wider perspective, studying the methodological and technical issues that arise when attempting to integrate decision support systems with electronic health records systems, as well as the factors improving adoption and acceptability, and issues to mitigate at all stages of the system lifecycle, such as explainability, existence of induced biases and the lack of generalizability.
• Principles of User Interface Design and Human-Machine interaction (6 contact hours)
Course Description:
This unit is intended to give students an introduction to the latest design frameworks and methodologies that focus on the end user experience. Students will learn how a user focused design process can be used to solve the most challenging problems facing businesses and organizations today. Students will be introduced to the latest trends in design thinking, the importance of iterative design frameworks, researching user needs, prototyping, collaboration and critical feedback.
This unit covers also basic concepts of human-machine interaction theory and application from an integrated-approach of knowledge domains, i.e., the cognitive, behavioral, and social aspects of users and user context, relevant to the design and usability testing of interactive systems.
• Human Factors Engineering principles and application to systems design and usability optimization (6 contact hours)
Course Description:
Human factors engineering is the scientific and practice-based discipline concerned with studying and improving work performance in sociotechnical systems. In this unit, students will be introduced to emerging human factors engineering approaches, concepts, and methods and apply them to contemporary health informatics problems, in particular to optimize usability and outcomes of designed CDSS solutions. Example human factors engineering topics include automation, cognitive task analysis, field research methods, human information processing, process redesign, product design, safety science, team cognition, usability engineering, user-centered design, work system models, and workflow assessment.
V- Challenges and issues related to healthcare IT applications (2 credits)
• Ethical, security, confidentiality and legal considerations relative to the management of healthcare data (9 contact hours)
Course Description:
This unit will explore the ethical, security and privacy, confidentiality, and legal considerations that arise when managing healthcare data and using AI in healthcare. Topics covered will include the principles of data protection and privacy, the use of encryption and other security measures to protect patient data, the ethical considerations surrounding the use of AI in healthcare decision-making, and the legal frameworks governing the use of healthcare data.
Students will learn how to analyze a situation in order to ensure the ethical, secure, confidential, and legal management of healthcare data is met. They will gain an understanding of the principles of data protection and privacy, as well as the security measures that can be used to protect patient data. Through case studies and practical exercises, students will develop the skills needed to apply these principles in real-world healthcare settings.
Topics such as informed consent, electronic records over the internet, remote patient monitoring, and wireless technology privacy concerns will also be discussed.
• Assessing and mitigating biases in artificial intelligence applications (6 contact hours)
Course Description:
With the growing adoption of artificial intelligence (AI) in various areas of our personal and social lives, it has become increasingly important to understand the implications of biases in algorithms, and how they can impact the fairness and accuracy of the decisions made by AI systems. In this unit, we will explore fundamental issues of fairness and bias in machine learning algorithms and in data selection and preparation. From human bias to dataset awareness, we will explore many aspects of building more ethical models.
• Usability challenges related to the use of healthcare IT applications (3 contact hours)
Course Description:
This unit will explore the usability challenges that arise when using healthcare IT applications. Topics covered will include the complexity of user interfaces, the lack of customization, and the need for extensive training to use applications effectively.
Students will gain an understanding of the common usability challenges that arise when using these applications, and will learn strategies for overcoming these challenges. Through case studies and practical exercises, students will develop the skills needed to recognize the importance of user-friendly healthcare IT applications.
• Project management for healthcare information applications (6 contact hours)
Course Description:
This course introduces best practices in project management, covering the full project life cycle with a focus on globally accepted standards. Topics include project initiating, planning and development, project management tools, budgeting, human resource management, project monitoring, task scheduling, resource allocation, effective project communication and reporting. Using methods and models from this course, students should be able to execute data science and data engineering projects more effectively.
• The role and impact of health informatics professionals (6 contact hours)
Course Description:
This unit will highlight the critical role of healthcare informatics professionals in the clinical world and how they can use their understanding of technology to improve patient care, enhance and bridge the communication among healthcare providers, and streamline clinical workflows. Their added value in designing, implementing, and maintaining EHR systems that enable healthcare providers to access and share patient information in real-time will be highlighted, as well as their role in optimizing the implementation and usability of Clinical Decision Support Systems that can help professionals apply best practices, ultimately improving efficiency, and leading to better outcomes for patients.