1. Initial Prototype for Clinical Data Normalization and High Throughput Phenotyping SHARPn F2F June 30,2011

2. Purpose  Demonstrate a proof of concept solution, based on new tools, technology, models and methods.  The prototype demonstrates: –The ability to push unsolicited data using NwHIN exchange protocols –Conversion and normalization of HL7 2.x lab messages to XML clinical element model (CEM) instances –Conversion and normalization of HL7 2.x medication orders to CEMs. –Extraction of medication CEM instances from narrative clinical documents using NLP processing –Persistence of CEM instances in a light weight SQL database –Phenotype processing across the CEM database utilizing the Drools rules engine

3. High Level Architecture Diagram 1. Use Data from IHC (De-Identified) HL7 2.x messages 2. Send data into Mirth Connect on the IHC side 3. Create NwHIN Document Submission (XDR) message using HL7 2.x message as payload 4. Send Document Submission (XDR) message from Mirth to IHC NwHIN Aurion Gateway 5. Send XDR message from IHC Aurion Gateway to SHARP NwHIN Aurion Gateway 6. Send XDR message from SHARP NwHIN Aurion to Mirth Connect 6a. Send Mayo HL7 2.x Lab Messages & Clinical Documents to Mirth Connect 7. Process HL7 2.x messages and/or clinical documents in the UIMA Pipelines, to normalize and transform into Clinical Element Model (CEM) instances 8. Send the resulting XML instance of Clinical Element Model (CEM) to Mirth Connect 9. Persist Clinical Element Model (CEM) instances to MySql database. 10. Perform phenotype processing on the CEM instance database. SHARP Processing Sequence 10 IHC (Backend CDR Systems) Mirth Connect IHC NwHIN Aurion Gateway SHARP NwHIN Aurion Gateway Mirth Connect UIMA Pipeline CEM Instance Database 1 2 3 4 5 6 7 8 9 6a Mayo EDT System

4. Mirth Connect  Enables information flow and transformation  Mirth channel receives message from some source, transforms it, and routes it to one or more destinations  Product is open source  NwHIN with Aurion/CONNECT can be source or destination of a channel  Used to store CEM Instances to the database  Can be used to route data to other locations or databases

5. Mayo EDT cTAKES CDA for Meds HL7 for labs Mirth CEM SharpDb High level flow - Mayo cTAKES (NLP) cTAKES (NLP) Mayo EDT AdminDiagnosis processor Tabular data CEM Custom UIMA pipeline Configurable UIMA pipeline Configurable UIMA pipeline CEM

6. Medication to CEM - Mayo data CDA CDA- Initializer Sentence Annotator Sentence Annotator Tokenizer Annotator Tokenizer Annotator LVG Context Dependent Tokenizer Context Dependent Tokenizer POS Tagger POS Tagger Chunker Dictionary Lookup Annotator Dictionary Lookup Annotator Drug Mention Annotator Drug Mention Annotator Drug CEM CAS Consumer Drug CEM CAS Consumer Mirth SharpDb cTAKES UIMA Annotators (NLP) Patient count – 10000 CDA document count - 360452 CEM count for medication – 3442000

7. IHC-Medication, Mayo, IHC LAB to CEM HL7 Meds HL7 Meds HL7 Initializer HL7 Initializer IHC-GCN TO- RXNORM Annotator IHC-GCN TO- RXNORM Annotator Drug CEM CAS Consumer Drug CEM CAS Consumer Mirth SharpDb HL7 Labs HL7 Labs HL7 Initializer HL7 Initializer Generic- LAB- Annotator Generic- LAB- Annotator LAB CEM CAS Consumer LAB CEM CAS Consumer Mayo LOINC resource Mayo LOINC resource IHC LOINC resource IHC LOINC resource IHC RXNORM resource IHC RXNORM resource New UIMA Process Nodes

8. SharpDB a CEM Instance Database

9. Phenotyping (Drools) Business Logic Clinical Element Database List of Diabetic Patients Data Access Layer Transformation Layer Inference/ workflow Engine (Drools) Service for Creating Output (File, Database, etc) Transform physical representation  Normalized logical representation (Fact Model)

10. Completed Work  Installation of informatics “SHARP” Cloud system at Mayo  Installation and configuration of tools on IHC side and SHARP Cloud  “Tracer Message” processing –Used to test communication throughout system –Successful transfer using NwHIN/Aurion of test message between IHC & Mayo  30 de-id IHC patients through pipeline/Drools end-to-end –134 Thousand CEMS generated  Extraction and message generation for 10,000 patients  Processing of 10,000 patients Meds, Labs, Billing data –15 Million CEMS generated  Conversion to selected CEM models via UIMA framework  Persisted from CEM to MySQL

11. Completed Work (Cont.)  Produced New XML Schemas for CEM Models –Standard lab panel –Ambulatory medication order –Administrative diagnosis These three models were used for the prototype experiment. Excerpt of Lab CEM instance CEM Search Tool: http://intermountainhealthcare.org/cem

12. Mirth Enhancements –Implemented NwHIN XDR connector capability –Implemented UIMA connector capability –Created NwHIN Aurion XDR adapter Channels Created Completed Work (Cont.) Sample XDR ChannelChannel that receives HL7 2.x message, places the message as the payload of an XDR message and sends it to a remote NwHIN gateway ReceiveXDRMessageReceives an XDR message from Mirth and extracts the HL7 2.x message CemAdminDxtoDatabaseReceives an XML instance of the administrative diagnosis CEM and persists it to the database CemLabToDatabaseReceives an XML instance of a standard lab panel CEM and persists it to the database CemMedicationToDatabas e Receives an XML instance of a medication CEM and persists it to the database

13. Dual Security Certificate Exchange SHARP/Mayo Cloud Intermountain Healthcare MirthAurion Gateway SHARP Aurion Gateway IHC Proxy SHARP Proxy Mirth Internet

14. Thank You! Calvin Beebe Christopher Chute Craig Parker Cui Tao Cyndalynn Tilley David Mead Dingcheng Li Donna Ihrke Gerald Bortis Guergana Savova James Masanz Jeff Ferraro John Holman Jon Teichrow Kevin Bruce Kyle Marchant Les Westberg Margarita Sordo Mat Bockol Michael Turk Mitch Dempsey Nathan Davis Pei Chen Sean Murphy Sridhar Dwarkanath Stan Huff Susan Welch Tim Peters Tom Oniki Vinod Kaggal