Please note that this list is indicative and class topics may change due to teaching needs in this highly evolving field.
Introduction to e-health: A view on healthcare in EU in an ageing society. What is e-health? Concepts and definitions. Overview definition and history of e-health and differences compared to biomedical informatics. Types of e-health applications. Success factors and barriers to e-health implementation. Hype Cycle of technology.
Mobile applications and potential of the smartphone technology for health: consumer technology and m-health. Digital health in the patinet journey. New EU Medical Device Regulation, comparison with US FDA. Softare and medical device - implications for Artificial Intelligence and apps. Methods for app quality assessment
Big data: What is big data and how it is generated. The Value of big data. Big data in Healthcare and relevant barriers. Big data analytics.
Telehealth: Historical perspective of telehealth. Categorization of resources. Remote monitoring. Telemedicine. Telepresence. Barriers to telehealth implementations. Telehealth in low income environments. Teleealth and COVID-19.
Adherence: Dimensions of adherence. How to measure adherence. Technology to improve unintentional non-adherence. Understanding intentional non-adherence. Case-study on hyperthensive patients.
Consumer Health Informatics: personal health record, use of social media, apomediation, data liquidity, difference vs EHR, usability testing.
Ethical issues in e-health: Appropriate use. Ethical issues for AI. Exploring the concept of privacy in EU and USA. Privacy and violations using technology. EU General Data Protection Regulation (GDPR). Confidentiality and public health research. Pseudonomyzation. Biometric access to data.
E-health for patients: Who is the patient? Differences in patient groups. E-patients. Patient literacy and assessment tools. Cognitive behavioural models. Behavioural change techniques. Technology to change behaviour. Persuasive System Design model. Patient engagement and empowerment. Self-tracking, wearables, mobile applications and virtual reality.
Italian and Lombardy health information system: a case study.
Use of e-health data for research: Medical literature retrieval: systematic review. Registries of e-health data. Adherence to guidelines. Reporting quality of care. Evidence-based medicine: Comparative effectiveness analysis and large scale clinical trials. Geospatial data in health. European Health Data Space for primary and secondary use of health data.
Overview of e-health data: Data flow in healthcare, Classification of Data type, Data collection and recording, Use of e-health data, From data to information to knowledge based system in healthcare.
Methods to design, develop and evaluate e-health technologies: the holistic user centered approach starting from the contextual inquiries, and the identification of functional requirements and technical specifications, towards the development of prototypes, and the iterative and final testing.
The design, development and testing of serious games for the screening and training of learning disabilities.
e-health terminologies: Definitions of terminologies, classifications and onthologies; Some relevant examples: Systematized Nomenclature for Medicine (SNOMED CT), Unified Medical Language System (UMLS) and Metamap.
Information retrieval techniques for data-mining of medical records: Natural language processing
Decision making in e-health: Biomedical Decision Making: probabilistic clinical reasoning; Management of uncertainties for decision making; Clinical Decision support system; Mining hospital data to predict patient outcomes.
e-health for clinical trials: the use of computerized systems in clinical investigations: electronic report form, electronic patient reported outcome measures, e-health technologies to support randomized clinical trials, towards decentralised clinical trials.
Digital medicine for Ageing in place: smart Internet of thing objects for remote monitoring; transparent daily life monitoring as a frontiers towards early diagnosis and a new model of care. The role of Artificial Intelligence in the context of Ageing in place.
A project laboratory is mandatory and is an integral part of the course. The objective of projects is to give the students the opportunity to tackle a real problem using concepts learned in class, working in small groups and organizing their work. Projects will be assigned at the beginning of the semester and are expected to be given in at fixed deadlines defined by the time the project will be assigned. The evaluation of projects will be based on the produced reports and on a final presentation.