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. 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 a fundamental requirement, not a technical footnote.

From rules to statistics to deep learning

The earliest NLP systems worked on rules: developers defined instructions and machines responded accordingly. ELIZA, developed in the 1960s, could produce responses resembling human conversation by pattern matching, without understanding a word it processed.

In the late 1980s the field shifted towards statistical NLP. Systems began learning from data, identifying patterns across large volumes of text, opening up machine translation, text classification and speech recognition as practical applications. The explosion of digital text from the early 2000s, combined with deep learning and neural networks, produced a step change: systems could now interpret meaning, handle ambiguity and interact in ways that feel genuinely human.

What modern NLP can do

Today NLP is already embedded in everyday tools: machine translation, automatic text classification, sentiment analysis and automatic summarisation. For healthcare the most significant capability is entity extraction: pulling names, dates, diagnoses and organisations directly from unstructured text without anyone having to read and tag it manually.

Why standardised data is the critical factor

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

A feasibility study in the Journal of Medical Internet Research (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. Unstructured notes consistently contain meaningful clinical information not captured in coded fields.

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

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: better accuracy in automated coding and audit, better compatibility across platforms, and lower development costs.

Where this is heading

The global NLP in healthcare and life sciences market is projected to reach $12.09 billion by 2026, growing at 20.5% a year. The systems now being deployed in clinical environments, including large language models that read, summarise and structure clinical correspondence at scale, depend entirely on the quality of the underlying data.

For GP practices this means how clinical information is recorded today determines what these tools can reliably deliver tomorrow. Practices with consistent, well-coded records will benefit from NLP-assisted workflows as they arrive across NHS systems; those without that foundation will find the gap between promise and delivery wider than expected.

How NovaDoc supports GP practices

Nova Healthcare Solutions is a CQC-registered 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 NLP tools depend on.