Technology is changing at an ever-increasing pace, empowering medical professionals to perform new ways of testing, diagnose diseases earlier and treat and cure illnesses that were previously thought to be incurable. While technology has helped patients around the world, the medical innovation sector still needs to overcome some barriers to unlock the full potential of data and artificial intelligence (AI).
The role of AI in medical innovation
Innovation is the lifeblood of the health and life sciences industry. New innovations help improve patient outcomes, increase the speed to market for treatments and cures and lower costs while still fulfilling regulatory requirements. These reasons more than justify the billions invested in R&D every year, including the 27% reinvested in innovation by the Belgian biotech sector.
AI plays an important role in accelerating the innovation process. It can be used to:
- facilitate and accelerate a range of manual processes to review clinical data quickly and efficiently;
- assist in drug discovery, design and development;
- improve pathways in patient journeys; and
- streamline daily operations.
Barriers to using intelligent solutions
But how can organizations leverage AI and data in medical innovation? First, the organization needs to investigate the issue to fully understand the problem. Then, it will have to explore the available data sources before preparing, integrating, transforming and standardizing the data itself. Intelligent solutions such as AI, machine learning, algorithms or models then analyze the data and give insights based on the original problem.
Even though that sounds easy enough, organizations are discovering there are still a lot of barriers to overcome.
Data is a double-edged sword. On one hand, the right data used in the right way can reduce the time to market, increase treatment effectiveness and deliver new insights. But on the other hand, there are technical issues concerning data. Organizations need to be mindful of how to receive, store and manage incoming data, especially when this is coupled with the technical depth of the data.
The data within some organizations is hidden in different silos, with no transversal movement, reducing the likelihood that innovators have access to all the data they need when they need it.
In large hospitals, this often prevents one doctor from obtaining a global view of their patient if that patient has passed through several specializations.
A change management program, complete with workshops and enablement sessions, is often required to get silo owners to share their data and benefit the entire organization and its patients.
GDPR and privacy concerns are one reason that organizations maintain separated data silos. However, sharing anonymized data is possible and often recommended.
To give one example, when it comes to COVID-19 vaccinations, governments around the world are vaccinating at-risk citizens first. However, governments do not always have access to their citizens’ medical records, so are unable to identify which citizens should be vaccinated first.
Looking to the future of intelligent solutions
Intelligent solutions can deliver insights, analyze results and assist the innovation process. However, realizing the full potential of intelligent solutions within an organization requires the right infrastructure, including a comprehensive data platform, established data standardization policies and clear regulations on data privacy and security. Is your organization ready to benefit from intelligent solutions?