Online Postgraduate Course in Digital Transformation for Health Professionals

This health online postgraduate course gives the digital skills to remove limits and reduce costs in innovation using Artificial Intelligence, enhancing the use of data, and analyzing clinical processes.

Developed in collaboration with the Institute of Fraunhofer and Oxford
within the EIT Health Campus project “Future Digital skills
for Health Professionals”.

Key Information





Digital innovations are revolutionising healthcare. The future of healthcare lies in working hand-in-hand with technology and healthcare professionals have to embrace emerging medical technologies in order to face the challenges efficiently in the coming years.

Driving innovations in healthcare will allow clinicians to allocate more time on patient treatment and improve significantly outcomes of healthcare systems.

From clinicians to hospital managers, healthcare professionals need to be able to innovate, adapt and engage with new technologies and the changing pace of medicine. Our postgraduate medical training enables you to lead projects and apply digital technologies in the health and care sectors.

In this online postgraduate healthcare course, through real clinical use cases, you will learn the basis for creating and managing clinical digital content and its use with privacy requirements, clinical solutions using Artificial Intelligence to diagnose and support decisions, the tools to develop and evaluate clinical digital designs, and how to make the economic analysis of clinical processes.

This post graduate diploma in medicine is organized in 4 modules:

  • Introduction to the digital transformation in healthcare and basic digital skills
  • Machine learning
  • User-centered design and user experience
  • Economics in digital healthcare

This postgraduate medical diploma is funded by EIT Health, a network of best-in-class health innovators backed by the European Union. The content has been developed by three partner organizations: Universitat de Barcelona, listed as the best university in Spain in the QS World University Rankings of 2021; Fraunhofer, Europe’s leading application-oriented research organization and TheHill, the digital innovation team of Oxford University Hospitals NHS Trust, one of the UK’s largest teaching hospitals and one of the largest hospitals in Europe.

This year’s pilot postgraduate health course is offered at a very competitive price since it is partially funded by EIT Health. Take advantage of this unique opportunity.


Learning Path

Digital technologies have a significant impact on the healthcare system. Active involvement of healthcare professionals is of high relevance in order to effectively use the emerging opportunities these technologies offer in healthcare. This requires essential digital knowledge and skills, facilitating demand-oriented and patient-centred care, supporting communication in the changing doctor-patient-relationship, enabling interdisciplinary collaboration and improved hospital routines and facilitating deliberate professional self-development by successfully applying digital health technologies.

Developing essential digital skills by means of concrete digital technologies to improve hospital routines and patient care.

Today, hospitals manage a huge amount of clinical data which can be implemented into Machine Learning/Artificial Intelligence tools to perform real digital transformation in healthcare. Teaching machines to learn from data is the key to develop intelligent assistants for many tasks in the clinical institutions. Today, machine learning tools allow us to process large amounts of clinical data and elaborate highly precise diagnostic methods to help experts to get high precision medicine and significantly better care quality.

Train how machine learning can help to improve diagnosis, learn to assess machine learning tools for clinical care (e.g. computer-aided diagnosis), and streamline administrative processes in hospitals.

The value of a medical system for its users (both patients and healthcare professionals) depends largely on the ease of use of the system and its accessibility. Learning the principles of design and evaluation will allow users to judge the quality of medical devices, provide their feedback and contribute to the improvement of such systems.

Training about the principles of user interface design and evaluation in the context of medical devices and eHealth applications to improve their performance, users acceptance and satisfaction.

Connected health according to eHealth data standards allows the development of state of the art eHealth tools that provide every stakeholder easy access to the data required for his/her work. This ensures personalised care plans for every patient avoiding duplicate examinations, contradictory medications and optimised cost structures. Connected health eliminates proprietary data formats, that lead to isolated data silos and stand alone systems. Isolated systems do not enfold full potential benefits to their users and cause high deployment and maintenance costs.

Understand why health economics is important, being able to define a problem, understand different types of analysis commission/co-design and interpret the outcome of a health economic evaluation.



Alexandrina Stoyanova

Associate professor in Economics at the University of Barcelona, researcher at the Center for Economics Analysis and Social Policy, member of Barcelona Economic Analysis Team, EU Commission expert in the evaluation of Horizon 2020 Framework proposals and projects. Her research focuses on health economics, a subject she teaches at post-graduate level.

Petia Radeva

Petia Radeva

Full professor at the Department of Mathematics and Computer Science, Universitat de Barcelona, Senior Researcher at Computer Vision Center (CVC), Head of “Computer Vision and Machine Learning at the University of Barcelona” consolidated research group (www.ub.edu/cvub) SGR 1742, Collaborator of the State Agency of Research (Agencia Estatal de Investigación) area TIC (INF), División de Coordinación, Evaluación y Seguimiento Científico Técnico, November 2019.

Oliver Diaz

Oliver Diaz

Lecturer in Computer Science at the University of Barcelona (Spain). PhD in electronic engineering from the University of Surrey (UK). Postgraduate in medical computing from the University of Alicante (Spain). 10+ years experience as researcher in artificial intelligence applied to medical imaging and medical physics.

Mireia Ribera

Mireia Ribera

Lecturer in Computer Science at the University of Barcelona. PhD in Digital Information from the University of Barcelona. 15+ years experience as researcher in accessibility and user centered design. 5+ years experience in teaching and disseminating Information Visualization.

Jordi Vitria

Jordi Vitria

Full Professor in Computer Science at the University of Barcelona. PhD in Computer Science from the Universitat Autònoma de Barcelona. 25+ years experience as researcher in data science and machine learning.

Antonio Monleon

Antonio Monleon

Lecturer in Statistics, Data-Science and Bioinformatics at the University of Barcelona. PhD in Probability and Statistics from the University of Barcelona (Spain), Master in Applied statistics from the UNED (Spain), postgraduate in Geostatistics and Open-Source Statistical Computing (University of Twente; Netherland) and in Big-Data and Data-Science from the University of Barcelona (Spain) and agricultural engineer. 10+ years experience as researcher in statistics applied to biosciences and clinical research.

Javi Ródenas

Javier Ródenas

Deep Learning researcher and associate professor at the University of Barcelona. Master in Artificial Intelligence from the International University of La Rioja. +2 years of experience in Artificial Intelligence solutions.

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Universitat de Barcelona
Gran Via de les Corts Catalanes 585
08007, Barcelona

Phone: +34 934 02 11 00