Using RDF, Datalog, OWL-RL and a "RIM lite ERPA Ontology" to calculate HEDIS Quality measures

At KP, and working with Ian Horrock's group at Oxford, we have been 
experimenting with their new RDF, Datalog, OWL-RL triple store called 
"RDFox". 

We have calculated the HEDIS Diabetes quality measure on a population of 
over 400,000 patients real data.

We still have to compare our numerators and denominators to results 
calculated with SQL and traditional DB tables. 

I will be presenting a very simple version of this at HL7 at the AID work 
group in Atlanta on Monday Q3.

I believe this is the first time a complex HEDIS quality measure has been 
calculated with RDF, OWL and Datalog and SPARQL on a large population of 
real patients.

I will not be presenting the complete complex HEDIS measure (which would 
take days), but a smaller example to explain how it all works. 

We used SNOMED subsumption to generate a small value set of SNOMED codes 
that are "kinds of Diabetes".  Using that SNOMED VS, we found all the 
patients who had a visit coded for Diabetes.  Then we searched all of 
their HgBA1C values and then found the "last value".  We could then look 
at the numerical results of the HgBA1C and find how many of them were 
below 7% (good control).

In order to do this we had previously created an OWL ontology based on 
Entities in Roles that Participate in Acts.  It is not the full HL7 V3 
RIM, but only what was needed for this exercise. 
This "KCOM" model is what we presented before at HL7 AID meetings.
This entire project would not have been possible to do without first 
mapping the raw clinical data to this ERPA OWL backbone ontology.  All of 
our queries were based on this ERPA (Entities in Roles Participating in 
Acts). 

RDFox is multi threaded and we were able to run the data materialization 
on 8 threads on an 8 core machine with 64 Gig RAM.  It ran in only a few 
hours and we have already found ways to speed it up further.

Hope to see you at HL7 Atlanta. 





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