IMO Precision Normalize is built on domain-specific NLP to extract, standardize, and enrich free text and structured clinical data.
NLP solutions must be trained in the complexity of clinical language to efficiently and effectively realize value from unstructured healthcare data. They need to be able to evaluate countless acronyms, abbreviations, and misspellings common in clinical parlance; understand the use of negation, specialty-dependent language, and time-based relationships; and identify relationships between words and phrases within a larger block of information.
Clinical patient data is a powerful resource in healthcare. But, as the healthcare IT ecosystem grows in complexity, data is more plentiful and problematic.
As patient data moves across various health information systems, it can become highly variable and lose key components. This leads to a drain on IT, analytics, and clinical resources, as staff must spend more time manually filling in the gaps.
IMO Precision Normalize is built on award-winning NLP to efficiently extract clinical concepts from free text data, seamlessly mapping clinical entities and structured data to standard codes for more accurate, complete, and usable data.
Extracts clinical concepts from unstructured free text using named entity recognition.
Identifies and understands the presence, absence, or possibility of the clinical concept.
Uncovers connections between clinical concepts throughout the text.
Extracts time-related details associated with the identified clinical concepts.
Identifies sections within clinical narrative to add context and organization to extracted entities.
Comprehensively maps clinical concepts to IMO Health terms and all relevant standard codes.
Deploy as SaaS or integrate into your local or cloud environments via API to help extract and standardize clinical data at scale.
(AWS, Azure, GCP)
ICD-10-CM (default), ICD-9-CM, SNOMED CT®
CPT® (default), HCPCS, ICD-10-PCS, SNOMED CT, LOINC
RxNorm® (default), NDC, CVX
LOINC (default), CPT, HCPCS, ICD-10-PCS, SNOMED CT
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