> For the complete documentation index, see [llms.txt](https://docs.snomed.org/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.snomed.org/snomed-ct-practical-guides/snomed-ct-data-analytics-guide/appendix/12.2-vendor-case-studies/12.2.12-intelligent-medical-objects.md).
# Intelligent Medical Objects
IMO produces a medical terminology service for healthcare solutions, allowing over 2,500 hospitals and 350,000 clinicians to focus on patient care. IMO bridges the information gap between clinicians, coders, and patients in the US and internationally. IMO enable and support the accurate capture and preservation of clinical intent for clinical documentation, decision support, reimbursement, reporting, data analysis, research, and health education.'
IMO's clinical interface terminology is designed pragmatically to capture clinical intent at point of care. However it is also intended to enable and simplify the adoption of standard ontologies by vendor partners.
By choice, the editorial process requires all IMO interface terms to have one or many qualified maps to SNOMED CT. Clients can then use SNOMED CT to drive reporting, analytics, clinical decision support, and research.
The following examples demonstrate how IMO uses SNOMED CT for analytical purposes:
1. Helping patients find health professionals who have expertise or interest in specific areas of medicine. These areas include disorders, procedures, devices, medications, patient demographics, and medical specialties. These areas of expertise or interest include those that are self-reported by clinicians and those documented in clinical encounters. The search algorithms use hierarchies in SNOMED CT to retrieve and rank search results.
2. Helping clinicians use patient diagnoses and procedures documented at varying levels of granularity to find appropriate patient education materials using SNOMED CT is-a hierarchies.
3. Grouping together related clinical concepts in patient records for creating focused patient reports and driving clinical workflows.
4. Forming subsumption queries for cohort selection within patient data repositories and document libraries.
5. IMO uses natural language processing (NLP) to extract information coded in SNOMED CT from clinical narratives.
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