Unambiguous Semantic Clinical Data for Information Exchange and Enhanced Understanding
Clinical Documentation and Decision Support
The CSN (Clinical Semantic Network):
- Enables the medical enrichment of structured clinical data at the point of input or from interrogating unstructured documents.
- Provides a built-in clinical data model that enables the aggregated mapping of concepts across multiple data sources.
- Supports decisions, create cohorts or phenotypes, etc. without the target user or systems (EHR, HIE, Population Health, etc) needing to understand the metadata linkages.
- Findings, symptoms and observations are captured using over 3 million concepts embedded in the CSN.
- Takes precision and personalized medicine beyond genomics by applying family history, social history, and past medical history as semantic information.
One of the founders of Johns Hopkins Hospital, Sir William Osler, famously said: “Listen to your patient, he is telling you the diagnosis,” which emphasizes the importance of an individual’s medical history, which includes past medical, family and social history. We agree, but unless this information can be captured as unambiguous, semantically exchangeable data it loses meaning once exchanged. The CSN changes all of this and represents a new alternative to traditional systems.
It is no longer enough for providers, value-based organizations and health plans to focus only on the relatively few patients or members who drive the majority of expenses. Improving clinical and financial outcomes will increasingly depend on a higher volume of well-formed data to address the clinical and lifestyle management needs of an entire population. Health assessments powered by the CSN provide deep, embedded medical knowledge and decision support at the point of engagement regardless of the engagement type or the engaging resource’s medical knowledge.
Natural Language Processing/Text Analytics
The CSN not only can enrich the volume of well-formed clinical data from “input”( i.e. member/patient engagement) but can also be paired with Natural Language Processing and Text Analytics. The result is a disruptive new way to clinically interrogate unstructured data and score the resulting cohorts. Resulting in more effective discovery and resource allocation to engage a population.