The Language of Pain: How Clinicians and Builders Can Learn from Metaphor

A practical and bilingual guide on bridging lived experience, clinical communication, and NLP implementation

Dr. Stella Bullo

Overview

Metaphor is not decoration. It is a tool patients reach for when literal words fail. In clinical contexts, metaphors transform private sensations into shareable narratives. Preserving them helps clinicians hear what patients mean, and helps builders design tools that turn figurative expressions into actionable insights. Ignoring metaphor means missing signals about intensity, urgency, and lived experience.

Why this matters

Clinicians gain empathy and accuracy when they document metaphors alongside standard notes. Product teams in digital health create better triage and dashboards when they integrate figurative language. Researchers find richer datasets when they treat metaphors as structured information rather than noise.

From methodology to practice

Step one: collect

Gather authentic patient phrases through interviews, forums, or intake forms. Record them verbatim.

Step two: categorise

Group expressions into families: fire, pressure, intrusion, tearing, weight. Patterns emerge across narratives.

Step three: reframe

Translate into clinical equivalents while retaining intensity. “Like broken glass in the pelvis” becomes “sharp, cutting pelvic pain”.

Step four: flag

Mark metaphors that suggest red-flag conditions. “An elephant on my chest” should trigger cardiac triage.

Clinical examples

  • “It feels like glass” → sharp, localised pelvic pain. Guidance: check cyclical patterns.
  • “Like a fire in my womb” → burning pelvic pain. Guidance: assess inflammation.
  • “An elephant on my chest” → crushing chest pressure. Guidance: triage urgently.

Technical implementation

A prototype pipeline shows how linguistics guides NLP:

  • Text normalised and segmented.
  • Regex and lexicons detect metaphor candidates.
  • Taxonomy stored in JSON for maintainability.
  • Lightweight classifiers handle context and ambiguity.
  • Dual outputs: patient-friendly summary and clinician-friendly notes aligned with ICD/SNOMED.

The stack combines Python, spaCy, curated regex, JSON taxonomies, Flask APIs, and small transformer modules where extended context is needed. Rules ensure interpretability; classifiers resolve edge cases; hybrid design balances precision, recall, and usability. Governance requires GDPR/HIPAA compliance, informed consent, and anonymised storage.

Pitfalls and safeguards

Avoid over-interpreting a single metaphor. Avoid reducing narratives to sterile shorthand. Account for emotional, cultural, and social dimensions. Do not assume metaphor categories are universal. The safeguard is simple: listen closely, triangulate, and document.

Practical tips

  • Allow free-text alongside structured symptom capture.
  • Show metaphors next to clinical paraphrases in dashboards.
  • Use colour or icons to signal figurative intensity.
  • Track metaphor frequency to support clinical research.
  • Maintain a living taxonomy that grows with patient contributions.

Checklist for clinicians

  • Record at least one metaphor verbatim.
  • Provide a neutral paraphrase for clarity.
  • Treat figurative intensity as a possible severity marker.
  • Use metaphors to open conversation about impact on life.
  • Share anonymised examples for training and research.

Conclusion

Metaphor is not noise; it is signal. Embedding it into healthcare and digital systems makes care more accurate and humane. For clinicians, it sharpens listening. For builders, it sharpens tools. Together, they ensure that when patients reach for metaphor, their voices are not lost in translation.