TCIM Research Navigator

Demonstration prototype prepared for proposal WHO-SHQ-GTMC-RFP-26-2035 by IRA Research and AI Innovation Pvt. Ltd. with IRA Consultancy and Research Organisation.

Demonstration prototype for proposal evaluation only. Content is draft and has not been independently reviewed or approved by WHO. Rules are fixed and transparent; no artificial intelligence is used.

TCIM Research Navigator

Research methods and evidence-assessment guidance for traditional, complementary and integrative medicine
Load a worked pathway:

1. Describe the study

State the research question and the system and intervention it concerns.

2. Characterise the intervention and setting

These features decide which designs can study the intervention as it is really practised.

3. Potentially appropriate methods

Options follow from the approved rules. Each shows its rule, strength label and sources. The navigator gives methodological guidance only; it does not grade evidence or recommend treatments.

4. Method Appropriateness Statement

Generated from the matched rules. Edit any field; edits are marked. Confirm to lock this version, then export.

5. Evidence-assessment route

How evidence produced by the chosen methods should be assessed. The navigator directs to established approaches; it does not carry out the assessment.

Feedback on this pathway

Source register

Every rule cites entries in this register. In the full product each entry also records the version and the section relied on.

Gap and uncertainty register

Recorded where no source supports a method or assessment approach, or where available tools are not validated for the use. Shown to users beside the outputs concerned.

Glossary

About this prototype

This prototype shows the user journey proposed for the TCIM Research Navigator early alpha: describe, characterise, review method options, draft the Method Appropriateness Statement and see the evidence-assessment route.

How it works

  • Rules are held as data, each with an identifier, conditions, a strength label, an uncertainty note and linked sources.
  • The same inputs always give the same outputs. No artificial intelligence or machine learning is used.
  • Gaps are recorded and shown, not filled by assumption.
  • Strength labels describe methodological support for a rule. They are not grades of clinical evidence and make no statement about the effectiveness or safety of any intervention.

Scope of this demonstration

Two worked pathways from the technical proposal are fully implemented: (A) Ayurveda, individualised care for type 2 diabetes, effectiveness; (B) Siddha, Varmam therapy for chronic low back pain, lived experience. Other combinations return general guidance from a small rule set. The early alpha will contain the full content approved by WHO at D2.

Content version 0.3-demo · draft · not independently reviewed · prepared October 2026

IRA Research and AI Innovation Pvt. Ltd. with IRA Consultancy and Research Organisation, Pune, India · Demonstration prototype · No personal data is stored