2 Services
Healthcare is a complex domain requiring deep domain knowledge, a methodical approach to understand and solve problems, and a principled multi-faceted strategy involving an umbrella of traditional and AI approaches (ML/DL, NLP, LLMs, Expert systems, rule engines, etc.) on a foundation of semantic data management and ontological thinking!
2.1 Clinical Data Management
Helping organizations make better decisions using ontological thinging.
Strategy
How does one create a resilient data strategy when no one really knows fully how the data being generated now will be used in the future? Ontological thinking holds the key to approaching uncertainity and complexity in a systematic manner to establish governance, processes, standards and frameworks for semantically normalized and unambigous data.
Good data is better than more data.
2.2 Terminology/Ontology
Operationalize terminology standards
Code systems, Value sets and Cross maps provide the means to creating semantically normalized and interoperable data (i.e. good data). These include standards such as SNOMED CT, LOINC, ICD, CPT, RXNORM, etc. as well value sets from VSAC and HL7. Cross maps are used to translate custom models to standard codes.
Terminology server/service does the hard work, so everyone doesn't have to.
Triad of terminology.
2.3 Clinical Decision Support (CDS)
Terminology at the core of Clinical Decision Support
CPG-on-FHIR
Clinical guidelines are the highest form medical knowledge! The CPG-on-FHIR framework brings a knowledge first approach to translating textual guidelines into actionable insights and plans at the point-of-care in real-time and individualized to each patient.
CDS is AI in action.
2.4 AI
Pragmatic AI with Ontologies
The field of AI is evolving at breakneck speed but still issues of reliability and accuracy remain. Trying to use quantity to overcome quality issues with data is not going to work.
A principled approach to data management with ontologies, open data standards and knowledge graphs provides for an evolutionary architecture and a resilient infrastructure for iterative model training and real-world testing for clinical validation.
Computable knowledge in the era of Generative AI
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Combining medical knowledge, terminology expertise and informatics for clinical data integration and interoperability, improving patient care and accelerating clinical research!