Surface Anatomy FHIR IG (PSS-2769)

Accurate, consistent, and computable representation of anatomic location is foundational to clinical care, documentation, research, quality measurement, utilization review, patient engagement, and interoperability. However, externally visible surface anatomy remains inadequately standardized in current interoperability frameworks. Clinicians and patients frequently describe surface-anatomic locations using free text or broad anatomic terms that lack the granularity, reproducibility, and visual grounding needed for precise localization.

This gap is particularly important in dermatology, wound care, podiatry, rheumatology, dentistry, oncology, and other domains where externally visible anatomy is central to documenting findings, diagnoses, procedures, observations, symptoms, orders, reports, imaging-related workflows, and longitudinal changes over time. Inconsistent representation of surface anatomy makes it difficult to align clinical data across systems, track findings over time, support patient-generated health data, and enable reliable secondary uses such as research, analytics, quality measurement, utilization review, and artificial intelligence.

Existing terminologies such as SNOMED CT provide broad anatomic coverage, but overlapping, variably granular, or non-canonical surface-anatomic concepts can create ambiguity for reproducible surface localization. Existing terminologies also do not consistently provide the precise, visually anchored surface-anatomy concepts needed to represent externally visible anatomy in a computable and interoperable manner. FHIR provides relevant structures, particularly BodyStructure, but additional implementation guidance is needed to support consistent use of BodyStructure for high-resolution and interoperable surface-anatomy representation.

This project addresses that need by defining a standardized FHIR approach for representing externally visible surface anatomy using BodyStructure and by binding relevant elements to Anatomy Mapper terminology. Anatomy Mapper identifiers, including persistent non-lateralized AMID concepts and composable AMcode expressions that may include laterality and additional modifiers, support more precise and computable localization of surface-anatomic concepts. The resulting IG will improve semantic interoperability, reproducibility, and longitudinal tracking of surface-anatomic data across clinical, research, operational, and patient-facing systems.