How can natural language processing benefit post-pandemic mental healthcare services?

It’s no surprise that the ongoing COVID-19 pandemic has shed light on existing gaps in mental healthcare services, highlighting issues with current approaches to treatment alongside areas for improvement – but how can data analytics fix this? In this blog post, we’ll explore the ways in which healthcare providers can harness the power of artificial intelligence and natural language processing to shake up existing systems, improving access to quality care and developing new forms of support in line with post-pandemic needs and expectations.

Over the course of the past two years, it’s become quite clear that not everyone has been able to access the mental health services they need. Many reports suggest that rates of relatively common conditions - such as anxiety disorders and depressive episodes - have increased dramatically, and a growing body of evidence gathered throughout the pandemic provides a solid argument for improving help for those at risk, with a proactive approach needed in order to address the root causes of these problems.

Natural language processing – a complex subfield of linguistics, AI and computer science - can be used in order to acknowledge specific trends across the mental health spectrum. By automatically manipulating and organising the information presented in a public forum – such as comments made across a variety of social media channels, for example – data analysts are able to better understand the contents and linguistics nuances of certain statements, creating an accurate representation of the status quo and establishing existing opinions surrounding mental healthcare services.

Using this invaluable information as a solid foundation, statistics-based predictive analytics models can then be used to highlight at-risk demographics or individuals through these insights, accurately assessing and determining those more inclined to experience a number of mental health conditions. This, in turn, can be used in many different ways depending on the nature of the information used – perhaps in the form of increased investment in outreach services, or to tweak current programmes in order to improve accessibility and success rates.

Of course, with something as sensitive as medical data, it’s hugely important to consider the ethics of natural language processing. It’s completely understandable that some individuals might not be comfortable sharing such personal details, preferring instead to keep this information private. As a result, data analysts have an obligation to make sure that this data is used responsibly and only with extensive privacy protocols in place, ensuring the safety and security of this information.

Creating a more personalized approach to medical treatment is something Permea stands very much behind, and no aspect of the healthcare sector is set to benefit from this renewed outlook than mental health services. It can be quite challenging for healthcare providers to establish exactly which services individuals need (and to work out what they really think about existing ones!). By harnessing the power of natural language processing and social listening, tailored solutions to these problems can be implemented, benefitting both the individual and society at large and creating a better outlook for mental health services in a post-pandemic world.

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