Is there a role for AI in managing healthcare data?
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Is there a role for AI in managing healthcare data?

Is there a role for AI in managing healthcare data?

Following the release of the UK Government’s health data strategy in June 2022, there has been sector-wide discussion about the potential of the NHS’s vast healthcare database as a ‘world-class resource’.  


There are two key issues under debate. Unlocking the potential of healthcare data could provide golden opportunities for research and sharing between services, and the improvement of efficiency within the NHS. But the sharing of health data also comes with enormous risks for patient confidentiality and privacy, where the misuse of sensitive information can have serious consequences.  


In the midst of all this is a question over how technological advancements will fit into the government’s plans for health data management. Specifically, is there a role for AI and machine learning within healthcare?  


AI in healthcare: the benefits 


AI has the potential to become a vital tool for supporting human decision-making within organisations like the NHS. It can help healthcare professionals to better understand the data they have, identifying day-to-day patterns and needs of the people they care for.  


AI can be used to detect disease and power wearable healthcare devices for the remote monitoring of conditions. It can even solve major global health problems, by tackling and analysing masses of big health data.  


There are numerous ongoing projects which could reshape the sector as we know it. One example is Google’s DeepMind Health, which is working with clinicians, researchers and real-life patients. Its technology combines systems neuroscience and machine learning to develop powerful learning algorithms and neural networks that can mimic the human brain itself.  


The importance of trust in the technology 


Many experts agree that for AI to have a significant place in the future of global healthcare, it needs to be trusted by professionals and patients. Transparency and explainability are key, so that AI-backed decisions and recommendations can be understood.  


In a recent DIGIT article, Lenus Health CERO Paul McGinness explained: 

“Healthcare is a very high risk setting for deploying AI. It’s important that any data analysis and AI work is fair and ethical.” 


“We need to be as transparent as possible. That means having interpretable, explainable models much more appealing than the black box approach you often get with neural networks.” 

In the UK, the NHS enjoys a high level of trust, which could go some way to mitigating the reputational risks of deploying AI technology. 


A report in December 2021 found that 84% of people trust the NHS to use their personal data in their best interests. However, around half of the same respondents admitted that they didn’t know how their data was collected or used.  


But of course, there are some other major pitfalls to be avoided. One is the fight against bias, which comes up again and again in scenarios where AI technology is deployed. It’s all too easy for bias to slip through AI algorithms, often due to a lack of oversight or information. This could be catastrophic in healthcare environments, and could negatively impact care for certain groups of patients. 


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