Generally speaking, EMA focuses on innovation across the product development cycle, looking to engage with stakeholders early and remain engaged throughout; early engagement is considered critical, if Digital is going to be extensively used and ultimately linked to meaningful clinical benefits.
Challenges and opportunities for Digital Health in EU
It starts with (Big) Data
- There is a consensus view across EU that Digital has the potential to improve data collection, analysis and downstream use, by simplifying such processes and reducing associated costs for insight generation. However, data handling and processing are expected to still remain a major challenge as they are frequently linked to location, method of storage and data integrity and security
- Collected digital data also need to be structured in a way that allows multi-layer applicability (i.e. can be used by multiple and various stakeholders), however without increased cost or processing burden, even if this is taking place at the local level. This is particularly important when payers and HTAs seek to use digital data in order to make decisions for digital therapeutics or digital health solutions in general
- Systems also need to be in place for data collection and processing. Currently, such systems and approaches are implemented via the EMA or via the relevant national authorities whereas GDPR is also providing another layer of data acquisition and processing that allows for streamlining of information flow and storage
- Data privacy, data security and data transparency remain key priorities, as large amounts of healthcare data are being generated at high rates. This necessitates organized efforts to generate structured and accessible datasets that seek to answer pertinent and specific healthcare questions, thereby reducing data noise levels. Machine learning and AI remain pivotal in this process, enabling to connect disparate datasets and subsequently extract meaningful insights from them. Eventually, frameworks supporting data storage, sharing and access (including by citizens) across EU countries will need to be developed
- Finally, when discussing pure innovation, experts agree that the use of big data has unlocked the potential for increased innovation during drug discovery and development. This is a trend which is likely to continue, however democratization of access to data and tools for their analyses is considered a key point going forward, in order to reduce any disparities among pharmaceuticals’ developers
Outcomes and endpoints in the Digital era
- Opportunities always exist for Digital to support improvement on how clinical trials are being conducted or to enhance the generated trial datasets with additional data points, either as part of the clinical outcomes themselves or as PROs
- Digital-influenced or “pure” Digital clinical outcomes are still challenging to develop, use effectively in clinical trials and ultimately maximize their value and that of associated datasets. Companies who operate in this space, therefore, seek to initially go down the road of patient-reported outcomes, which could provide early evidence, supporting further / expanded use of a Digital solution
- Finally, compliance always remains an important discussion topic when it comes to data generated or collected using Digital means as legal requirements might differ from country to country
Education around Digital and stakeholder positions on the topic
- With Digital also impacting authorization and post-marketing surveillance processes, clarity of information around it in general and Big Data / AI more specifically, as well as information on the views and positions of all the stakeholders involved needs to be in place, in order for more efficient and effective communications to exist
- There is an increased need for the establishment of best practices and tools accessible to everyone working with large datasets in healthcare, particularly when these data are being used to generate evidence on drug safety, efficacy and ultimately reimbursement (real-world data are key here)
Digital and outcomes-based reimbursement
- Digital and big data also have the potential to revolutionize Regulatory and how this discipline conducts its activities going forward. New digital tools can support innovative data generation and analysis, further advancing Regulatory professionals’ thinking toward new methodologies that can unlock the value this evidence brings to the table. This becomes particularly important as big data will also impact health technology assessments and will likely lead to a merging of the regulatory / HTA assessment process, or at least create a more seamless overall process
- Digital evidence validation and its subsequent use in regulatory- / payor- and ultimately HCP-related decisions remain challenging. The industry however remains hopeful that new approaches, stemming from an increasing focus on outcome-based reimbursement, will enable the development of datasets which can support such uses
Artificial Intelligence
- AI is closely linked to big data applications and as such, investment in this space will need to increase (both in healthcare and in other sectors). Early signals of the impact of AI on jobs, communications etc. also point toward broader changes in the socio-economic space. EU is therefore seeking to democratize AI by making both data access and data-sharing easier for everyone. Coupled with these efforts, increased funding toward training and education on AI (and other digital skills) is likely to emerge; such education/training will deal both with the technical aspects of the discipline, as well as the ethical ones, potentially through the development of new frameworks
Getting closer to the patient: apps and devices
- MHealth apps are at the forefront of the implementation of Digital in healthcare, however, it will be imperative they provide clear information to users about how their data are stored, shared and ultimately used
o This will lead to increased confidence and trust by patients on using these apps whilst being reassured that their data are protected and shared on a need-to-know basis with any authorized third parties
o Concurrently, new efforts around improving patient digital health literacy should be in place
Biggest obstacles for Digital
- Despite current efforts by EMA, major obstacles still remain for Digital to be widely adopted
o Privacy and associated risks (including cybersecurity risks)
o Lack of wide-scale infrastructure for efficient implementation and adoption of Digital solutions
o Ethics
Some examples from EU countries
- With EU going toward standardization of processes around Digital, let’s look at some examples from some of the EU5 countries
o France
- HAS provides guidance, promotes and aims to increase the confidence of the end-user when it comes to Digital applications, including providing good practice guidelines for manufacturers/users when it comes to mHealth apps and smart devices (which are not classified as medical devices)
o Germany
- The approval of the Act to Improve Healthcare Provision through Digitalisation and Innovation (Nov 2019) means that patients will be able to get their hands on new digital solutions faster and under prescription / national reimbursement
- This also means that Germany will be leading the way in terms of establishing broad-scale digital networks in the healthcare space that are patient- and value-based oriented
- Similarly, the physician-patient interactions will be heavily influenced by telehealth and we are likely to witness rapidly increasing rates of video-consultations
o Spain
- Spain is on the way to provide more guidance on mHealth apps and digital health solutions, focusing on privacy and security and also on the quality of service and applicability for the target demographics and patient groups
o UK
- UK is focused on ensuring mHealth apps comply with medical device regulations and are safe/able to receive a CE mark
Some examples of company activity and developing positions on Digital
- Besides activity within countries, let’s also have a look at some of the companies who are venturing into Digital and what their approach and position is
o Bayer
- Openly discussing about Big Data and how it can affect healthcare by empowering patients, including increasing their involvement in innovative medicine development, mostly through new sensor tech
- Focusing on AI and how it can help with Big Data management and support new tech implementation in clinical trials and real-world studies
o Pfizer
- Pfizer’s focus is data science, including looking into wearables and smart devices (and how these can be used to improve clinical trials) and digital treatments / mHealth apps and the benefit they can provide to patients
o Novartis
- Extremely strong focus on AI and data science, looking to develop cost-effective but broad solutions for treatment, including real-time monitoring and allocation of resources for a better healthcare delivery and clinical trial conduct
- Novartis also aims to develop algorithms (based on the collected datasets) which will (hopefully) enable prediction of risk of disease for individuals
o GSK
- Positive view and position on Digital, focusing on wearables, mHealth apps, sensor tech, AI / machine learning as well as algorithm development
- GSK appears to be making a “push” to increase its partnerships with innovative start-ups in the space
o Sanofi
- Patient-generated data (either within the clinical trial setting or in the real world) and insights are key focus for Sanofi with the company also aiming to understand and develop digital biomarkers
- Increased focus also on AI and machine learning applications, especially for real world evidence generation, in order to improve personalized treatment approaches and offerings in the future
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