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Enhancing Health Literacy through Controlled Natural Languages

A summary of Federica Vezzani's talk at the IUNTC meeting from March 19, 2025. 

This talk explores the use of controlled natural languages and plain language techniques to enhance health literacy in the medical domain. The results highlight the synergy between technical communication and effective terminology management in advancing patient-centered healthcare.

Federica Vezzani, University of Padova (Italy), holds a PhD in terminology and is a tenure-track assistant professor at the Department of Linguistic and Literary Studies of the University of Padova, Italy. She is a member of the ISO/TC 37 "Language and Terminology". Her main research interests are terminology, specialized translation, and technical communication. In particular, she focuses on the management of multilingual terminology in the medical domain, and she has developed the FAIR terminology paradigm for the optimal organization of findable, accessible, interoperable, and reusable terminological data.

 

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In healthcare, understanding medical information can make the difference between life and death. Especially when patients have to manage acute or chronic illnesses themselves, understanding medical information is essential, as in the context of type 1 diabetes. This is precisely where the "Enhancing Health Literacy through Controlled Natural Languages" project comes in. The study asks whether texts can be reformulated using controlled natural languages in such a way that they are not only easier to understand, but also more structured and precise - a research project on language and comprehensibility that is relevant to terminology and technical editors alike.

What is health literacy - and why is it important?

The term "health literacy" was coined in the 1970s and has undergone considerable development since then. It describes the ability to understand, evaluate and apply health information. In the specialist literature, a distinction is made between functional, interactive and critical health literacy. While the functional level focuses on basic reading and understanding, critical health literacy describes the ability to classify information and make informed decisions. Health literacy is therefore not static, but develops with experience, education and access to information - a dynamic goal that communication can actively support.

Plain language as a means of improving health literacy

Plain language is a proven tool for promoting health literacy. It reduces language barriers and is aimed primarily at people with a low level of education or language barriers. Studies show that If medical information is formulated in a clear, structured and target group-oriented way, comprehension increases significantly - a clear added value for prevention, compliance and patient safety.

The use of plain language ranges from brochures to digital tools such as apps or websites. Official guidelines already exist in some countries, such as Canada and the USA. Internationally, the ISO standard ISO 24495-1 was adopted in 2023, which sets out four basic principles for plain language:

  • Comprehensibility: familiar terms, clear and short sentences
  • Findability: logical structure, helpful organization
  • Usability: action-oriented formulations
  • Relevance: Content is geared to the information needs of the target group

These principles form a practicable framework for technical communication, including in the medical field.

Controlled natural languages - more than just "simplifying"

Another approach is the use of controlled natural languages (CNL). These are based on a natural language, but follow a defined set of rules and a fixed vocabulary. Their aim is to avoid ambiguity,

create clarity and formally design texts in such a way that they can also be processed by machines.

In contrast to plain language, CNLs are language-specific, e.g. "Simplified Technical English" (STE) for English, "Français Rationalisé" for French or "Italiano Tecnico Semplificato". These languages were originally developed for technical documentation in industry and aviation. They are characterized by

  • a strict choice of words (no synonyms)
  • reduced record lengths
  • only permitted grammatical structures
  • prescriptive glossaries for specialized terms

Even if they originate from technical communication, CNLs have the potential to have the same effect in the healthcare sector: clear communication, targeted terminology, automated processing - and a new quality in patient communication.

Research questions: Do patients understand medical texts better with CNL?

The project presented here investigates whether controlled natural languages can improve the readability of medical texts - specifically in the context of type 1 diabetes. The focus is on a text on the so-called "15-15 rule", a standard procedure for hypoglycemia, i.e., a critical drop in blood glucose levels. As hypoglycemia can quickly become life-threatening, clear, rapid communication of instructions is essential for patients.

The central research questions are:

1. Can the comprehension of medical texts be improved through the use of controlled natural languages?

2. What are the possible limitations of this method, particularly in view of its origins in the field of technical documentation?

The rewriting process: three languages, one goal

Authentic patient information from national diabetes organizations in three languages was selected for the study: English, French and Italian. These original texts contain scientific jargon, complex sentence structures and partly implicit instructions. The aim was to reformulate these texts according to the rules of three controlled natural languages:

  • Simplified Technical English (STE) - originally developed for aviation
  • Français Rationalisé - an early French CNL, currently under revision
  • Italiano Tecnico Semplificato - developed by the Italian Association for Technical Communication (COM&TEC)

The rewriting process followed the typical CNL principles:

  • Reduction of sentence length and segmentation of complex structures
  • Use of permitted words from a defined vocabulary
  • Deletion or replacement of unauthorized terms
  • Clear syntactic structures with unambiguous use of parts of speech

Example: In the original it said something like: "Hypoglycemia is a serious condition that may result from taking certain medications." This passage was changed in STE to: "Hypoglycemia is a dangerous condition. Some medicines can cause it."

The terms serious and taking are not permitted in the STE dictionary and have been replaced by the permitted variants dangerous and can cause. Complex sentence constructions were also broken up and replaced by simple main clauses. Similar changes were made to the French and Italian texts.

In Italian, a visual restructuring was also carried out, through vertical lists, clear enumerations and explicit highlighting of important references - all measures that serve the principle of findability and comprehensibility.

Results: Measurable improvements in readability

The comprehensibility of the revised texts was analyzed using various readability tools:

  • French: With the help of the Scolarius platform, the complexity score dropped significantly - from a higher school level (niveau collège) to a basic level (niveau primaire).
  • English: The Gulpease index showed a one-point improvement in readability after paraphrasing.
  • Italian: An analysis with the Pisa tool Read-It showed that the number of subordinate clauses was reduced by almost 50%, reflecting a clear structural simplification.

In addition, a lexicometric analysis using the Anatext tool showed that the reformulated texts used fewer different word forms per sentence, which indicates reduced lexical complexity. At the same time, the number of sentences increased - a sign that long structures were broken down into shorter units.

Conclusions: Potential and next steps

The study is still being carried out. The results so far suggest that controlled natural languages can significantly improve the comprehensibility of medical texts - even in complex, high-risk scenarios such as self-care in hypoglycemia. They offer not only linguistic but also structural advantages. This opens up new possibilities for technical communication in the healthcare sector: more precise documentation, standardized terminology, machine processability and international applicability.

But there are still unanswered questions:

  • Is linguistic simplification sufficient to ensure actual understanding?
  • How do manually formulated CNL texts compare to automatically generated variants (e.g. with AI)?
  • And: How can existing CNL dictionaries be usefully expanded for medical content?

Questionnaires for type 1 diabetics are currently being developed to answer these questions. These are intended to measure both the perception of the texts and the actual understanding of the text - in comparison between the original text, the CNL version and, if applicable, the AI-generated version.

In the long term, controlled natural languages will also be adapted more broadly to the healthcare sector - through specific glossaries, updated regulations and integration into digital tools. After all, in the medical field in particular, only those who understand can act correctly.