Clinical Data Digitization in Traditional Medicine

Abstract

Clinical data digitisation in traditional medicine has been lagging the conventional-healthcare equivalent by roughly a decade, and the gap has real consequences for research, safety surveillance, and routine clinical integration. The two most-cited reasons for the lag - concept-to-code mapping difficulties and the heterogeneity of the traditional-medicine provider base - are genuine challenges but not insurmountable ones. The WHO International Classification of Traditional Medicine (ICTM) addition to ICD-11, the growth of AYUSH-specific terminology infrastructure, and the general maturation of HL7 FHIR-based interoperability have collectively changed what is feasible. We consolidated 62 peer-reviewed studies published between 2014 and 2024 describing or evaluating clinical-data digitisation in traditional-medicine contexts. Studies were grouped into five frames: CD1 (EHR systems for traditional medicine), CD2 (standard terminology and ontologies), CD3 (digitised patient-reported outcomes), CD4 (conventional-EHR interoperability), and CD5 (data governance and privacy). Each frame was scored on the Traditional Medicine Digitisation Index (TMDI), a weighted composite across standard compliance, interoperability, clinician usability, data quality, and regulatory compliance. Standard terminology (CD2) and interoperability (CD4) scored highest at TMDI 0.832 and 0.816. Our reading is that traditional-medicine digitisation has reached the point where systemic uptake is more constrained by implementation resources than by methodological readiness.