UNSPSC 85415101: The diagnosis of abnormal glucose tolerance test

In this classification, this covers the group of activities that yields a diagnosis of abnormal glucose tolerance test. Abnormal glucose tolerance test is listed In The World Health Organization's International Statistical Classification of Diseases and Related Health Problems (ICD-10) as a four-character category under code R73.0.

UNSPSC 85415101 Diagnosis of abnormal glucose tolerance test is a Commodity code that shows limited alignment between its nominal scope and recent state and local procurement activity. The three most recent opportunities posted under this code do not solicit glucose tolerance testing, diagnostic lab services, or endocrinology evaluations. Instead, they span diagnostic imaging systems, special education contracted services, and temporary health professional staffing — none of which are classifiable as glucose tolerance diagnostics.

San Mateo Medical Center (a county-operated public hospital system) issued an RFP on October 7, 2026 for Diagnostic Imaging PACS (Picture Archiving and Communication System) implementation, due October 21, 2026, covering enterprise radiology, cardiology, and dental imaging with EPIC integration; Birdville Independent School District in Texas posted a competitive sealed proposal on September 18, 2026 for special education contracted services (audiologist, speech language therapist, occupational therapist, and 20 other line items), due October 21, 2026; and the County of Santa Clara Health System issued a non-exclusive multiple-award qualification RFQ on July 7, 2026 for temporary health professional staffing across ancillary clinical categories including audiology, cardiology, diagnostic imaging, and therapy services, due October 7, 2026. The code shows no recent activity in glucose tolerance testing or related diagnostic laboratory services. Buyers evaluating pursuit under this code should verify the correct UNSPSC classification before bid planning, as the current taxonomy assignment appears to be a data-entry or classification error.