Natural Language Processing: Why Standardised Data Matters

Natural Language Processing: Why Standardised Data Matters

Natural language processing has been part of computing for longer than most people realise. Alan Turing’s 1950 paper “Computing Machinery and Intelligence” posed the question of whether machines could think and in doing so laid the intellectual groundwork for everything that followed. The field has been evolving ever since.

Understanding where NLP has come from helps explain why it matters so much to healthcare today and why the quality of the data that underpins it is not a technical footnote but a fundamental requirement.

From rules to statistics to deep learning

The earliest NLP systems worked on rules. Developers defined a set of instructions and machines responded accordingly. ELIZA, developed in the 1960s, is the most well-known early example. It could produce responses that resembled human conversation by pattern matching against a predefined set of rules, without understanding a word of what it was processing.

In the late 1980s the field shifted towards statistical NLP. Rather than following rules, systems began learning from data, identifying patterns across large volumes of text and using those patterns to make predictions. This opened up machine translation, text classification and speech recognition as practical applications for the first time.

The internet accelerated everything. The explosion of digital text from the early 2000s onwards gave NLP researchers the data they had always lacked. Combined with advances in deep learning and neural networks, this produced a step change in capability. Systems could now not just match patterns but interpret meaning, handle ambiguity and interact in ways that felt genuinely human.

What modern NLP can do

Today NLP encompasses a broad range of capabilities that are already embedded in tools that organisations use every day.

Machine translation breaks down language barriers across international teams and documents. Text classification allows large volumes of written content to be automatically sorted and categorised. Sentiment analysis identifies the underlying tone and opinion within written feedback. Automatic summarisation condenses lengthy documents into concise outputs. Entity extraction pulls specific pieces of information including names, dates, diagnoses and organisations directly from unstructured text without anyone having to read and tag it manually.

In healthcare, that last capability is particularly significant.

Why standardised data is the critical factor

NLP models learn from data. The quality, consistency and structure of that data directly determines how accurately and reliably the model performs. When clinical information is recorded in inconsistent formats, using different terminology or abbreviations across clinicians and systems, NLP tools struggle to extract comparable and trustworthy information.

A feasibility study published in the Journal of Medical Internet Research in February 2025, analysing primary care records from 2.9 million patients, found that only a small proportion of clinical concepts extracted from free text had equivalent structured counterparts, demonstrating that unstructured notes consistently contain meaningful clinical information not captured in coded fields.

The practical consequences of this gap are significant. A study published in January 2026 analysing over 500,000 patients and 19 million electronic health records found that NLP applied to unstructured clinical notes identified 29.5 per cent more smokers and 19.3 per cent more obese patients than structured coded data alone. In a primary care setting, that scale of undercounting has direct implications for disease registers, QOF reporting and the commissioning decisions that follow from population health data.

Standardised data, meaning clinical information recorded using consistent conventions and agreed terminology such as SNOMED CT, gives NLP the foundation it needs to close that gap reliably. The benefits are well established: improved accuracy in automated coding and clinical audit, better compatibility across platforms and reduced development costs as less time is spent cleaning and reformatting inconsistent records before they can be used.

Where this is heading

Investment in NLP within healthcare is accelerating at a pace that makes this a present concern rather than a future one. The global NLP in healthcare and life sciences market is projected to reach $12.09 billion by 2026, growing at a compound annual rate of 20.5 per cent (KMS Technology, 2026). The systems now being deployed in clinical environments, including large language models capable of reading, summarising and structuring clinical correspondence at scale, depend entirely on the quality of the underlying data they work with.

For GP practices this means that how clinical information is recorded today determines what these tools can reliably deliver tomorrow. Practices with consistent, well-coded records will be able to benefit from NLP-assisted workflows as they become more widely available across NHS systems. Those without that foundation will find the gap between what these tools promise and what they can actually deliver in their specific setting is wider than expected.

How NovaDoc supports GP practices

Nova Healthcare Solutions is a CQC-registered healthcare services provider working with GP practices across 32 ICB areas in England. Our NovaDoc service sits at the intersection of clinical workflow and data quality, handling the correspondence and documentation processes that generate the structured, consistent records that NLP tools depend on to work effectively.

If your practice is looking at how technology can reduce administrative burden whilst improving the quality of its clinical data we would be happy to talk through what that looks like in practice.

Don Udara, Lead Technology Developer, Nova Healthcare Solutions.

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