Tackling Inclusive Communication and Gender-related biases in Digital Health

Course Introduction

Digital Health is changing the way we understand, monitor, and deliver healthcare, starting from intergrating AI to support clinical decisions, and going further to wearable devices that can follow our health in real time, and an increasingly number of sophisticated digital tools that can help make healthcare more personalised and accessible. We often think of these technologies as objective because they are built on data, algorithms, and scientific evidence. But behind every digital solution lies a series of human choices: what questions we ask, what data we collect, who we include, what we consider “standard”, and how we design and test the final solution. When these choices are influenced by conscious or unconscious biases, their effects can travel through the entire innovation process.

This course takes you on a journey through the Digital Health ecosystem to discover where biases can emerge and what can be done to reduce their impact. Starting with human assumptions and communication, we follow biases through clinical research and data collection, digital solution development, testing and implementation, and public policy. Particular attention is given to gender-related biases, while recognising that gender is only one of the factors that can influence whether people are adequately represented in research, data, technologies, and healthcare. The course explores how incomplete datasets and non-inclusive decisions can contribute to health inequities and how inclusive communication can help different stakeholders work together to build solutions that respond to a broader diversity of users.

The course is designed for anyone interested in more inclusive Digital Health, with particular relevance for the diverse stakeholders who shape regional health and innovation ecosystems: healthcare professionals and clinicians, innovators and solution providers, researchers and academics, public authorities, end-users, and other organisations involved in Digital Health. Each module combines accessible explanations, practical examples, reflection exercises, and real-world case studies to turn awareness into practice. Participants will explore biaess not only in communication, research, data, and algorithms, but also in the physical technologies behind Digital Health. Sensors, wearables, and medical devices interact with diverse bodies, meaning that decisions about their design, calibration, and testing can also influence how accurately they perform for different users. By following the development journey from the initial idea to data collection, design, prototyping, testing, and real-world implementation, the course encourages participants to identify potential gaps before they become embedded in a final solution. 

By the end of this course, learners will understand how biases can travel from human decisions into research, data, algorithms, hardware, and real-world health outcomes, as well as where they can intervene to reduce their impact. The course encourages participants to look beyond whether a solution simply “works” and consider more meaningful questions: Who was represented? Who was involved in the decisions? Who might have been overlooked? And how can Digital Health solutions be designed to work better for a diverse society?

Learning Outcomes

After completing this course, learners will be able to:

• identify and reduce gender-related biases within clinical research and data sets to ensure digital health outcomes are scientifically accurate and equitable;
• apply inclusive communication strategies to facilitate effective collaboration between multi-disciplinary stakeholders, including researchers, policymakers and developers;
• correct non-inclusive patterns in decision-making processes that could lead to health disparities in digital health;
• implement cross-functional leadership practices that foster a workplace culture capable of driving sustainable innovation in Digital Health at the European level.

Target Audience

The course is designed for a broad range of stakeholders involved in the Digital Health ecosystem, including:

• Healthcare professionals and clinicians – those who use digital health technologies in practice and can help identify how different patient needs and experiences are reflected in digital solutions. 
• Innovators and solution providers – companies, start-ups, developers, and other professionals involved in designing, developing, testing, or implementing digital health solutions. 
• Researchers and academics – professionals involved in health research, data collection and analysis, technology development, or education who can contribute to more representative research and evidence. 
• Public authorities – policymakers, program managers, and representatives of public institutions involved in healthcare, research, innovation, digitalisation, or the development of related policies and programmes. 
• End-users – patients, citizens, healthcare users, and their representatives, whose experiences and perspectives are essential for developing solutions that respond to diverse needs. 
• Other organisations and ecosystem actors – healthcare organisations, research institutions, innovation intermediaries, patient organisations, civil society, and other stakeholders contributing to the development and adoption of Digital Health. 

The course is also open to anyone interested in understanding biases, inclusion, and responsible innovation in Digital Health, regardless of their professional background.