There is no arguing that digital transformation has helped revolutionise many industries, including healthcare. In my role as a data scientist, I have seen our field’s solutions and strategies change as technology advanced and patients’ expectations evolved. Now that I work as a data and Artificial Intelligence (AI) partnerships lead, I strive for a multi-disciplinary approach to help pave the way for digital transformation to increase the adoption of digital technologies. To achieve the full potential of digital transformation in healthcare, we have to overcome technological as well as regulatory hurdles, which can only happen if we work together.
Establishing a core of collaboration
Working in a collaborative ecosystem is critical for healthcare innovation. When bringing together industry technicians, academics, and clinicians, we focus our efforts on clinical problems to solve with digital technology. This is key: technology is the tool we use to address clinical problems, not the driver of our developments. This multi-disciplinary approach works very well, and when missing, it is often the reason for a slower development and adoption of new technologies such as AI.
Layering on data complexity
The efficient, effective and secure use of data is foundational for digital transformation. Pre-COVID-19 findings (the most recent available on the RBCCM platform attribute up to one-third of the world’s data to the healthcare industry. Nearly two-thirds (65%) of healthcare leaders believe that the value data brings in areas such as digital health records, patient monitoring, and medical devices makes the time and resource investments required worthwhile. But for now, 46% of leaders view data as more of a burden than an asset. Among the barriers they named: limited technology infrastructure, staff reluctance or data illiteracy, and concerns about privacy and security.
As we consider how to leverage data and AI for insights and decision-making in healthcare, it’s important to emphasise that the value of data lies in its use and re-use. There we face a challenge related to the interpretation of Europe’s General Data Protection Regulation (GDPR). It can be a delicate operation to remain in compliance with GDPR while expanding the use of data. Examples include unlocking data siloes to derive fresh insights, or aligning definitions for key terms, such as ‘personal data’ and ‘anonymisation.’ Divergent and excessively strict interpretation across Member States of what constitutes anonymous or anonymised data hinders lawful data processing. This makes it extremely difficult and impractical for organisations to agree on whether and how to use data in the context of (scientific) research. This has a downstream impact on the development of AI models that could address such challenges as bias in digital health technologies.
A common and supportive regulatory framework is essential to make Europe more competitive in the global digital health market. The European Health Data Space is a welcome initiative to set out a suitable data-sharing framework for the benefit of patients, healthcare providers, and health systems. When it aligns with other regulatory frameworks, such as GDPR, the EU Data Act, and the proposed EU AI Act, we get closer to legal certainty about the use and re-use of health data – and we incentivise the industry to invest in innovation in a meaningful way.
Completing the story
There is great momentum to unlock meaningful insights from data, but this effort will only be a success when working together within a clear, consistent and harmonised EU framework.