TM HCLS Semantic Web in Healthcare A view of where we are and where we need to go in health care semantics Cecil O. Lynch, MD, MS

TM Validating Content for Incoming Case Notifications: CDC’s Tuberculosis Reasoner Tool Authors Craig Cunningham, OntoReason LLC John P. Abellera, MPH DISSS Sandy Price, PMP Northrop Grumman GB Kesarinath, MS DISS Participating Organizations Division of Information Shared Services Division of Integrated Surveillance System and Services Division of Tuberculosis Elimination Northrop Grumman OntoReason LLC Avenida Information Technology LLC

TM Project Charter PHIN message guide, in the form of HL7 v2.5 for Tuberculosis (TB) report of Verified Case of TB (RVCT) to CDC. Several states plan to use their own NEDSS compliant state-based disease surveillance system to capture and send case information of TB. However, these systems may not include all the validations to minimize the amount content error, and in most cases, do not include functionality to alert for drug resistant cases. Project to develop a message content validation application to implement the business rules provided by the retired Tuberculosis Information Management System (TIMS) Over 300 business rules, which ensure data collection and reporting quality. Tag messages with content validation errors Alert messages to accelerate program notification of MDR/XDR case reports

TM Message Processing Integration The message processor could be integrated in a number of alternative ways In line with the message processing system Post processing message components from a database As a Web Service remote procedure call

TM Deployment Architecture Figure 4. Data flow diagram of incoming TB case notification message within CDC’s DMB.

TM OTR Reasoning Framework Message Parsing Reasoner Message Parsing Reasoner Inbound MSG Queue Inbound MSG Queue Message Validation Reasoner Message Validation Reasoner Alert Reasoner Knowledge Registry JMS Interface Outbound Results Queue Outbound Results Queue Semantic Knowledge Store DMB Message Content Validation Architecture Visualization Tools Public Health Ontology Extraction for TB Msg.

TM Types of problems that could be solved by extending the TB framework The application component has great flexibility, and can be used in a number of ways Processing of case reports for consistency and accuracy Identification of trends in reporting Review of historical data against existing standards Feedback to situational awareness processes

TM The use of an OWL ontology The TB Ontology represents a machine processable version of the implementation guide Contains message element structures with requirements as it relates to data types, value set usage, field requirements Definitions of validation rules Contains value set extraction from VADS data Correlation between PHIN field identity and HL/7 v2 message elements.

TM HL7 Message Artifact Taxonomy Structured taxonomy Related to codeSystem Related to TB Question Carries the attribute of code

TM Question Detail Divided into semantic type Related to: HL7 Segment Errors that can be associated TIMS question it replaces HL7 data type of the answer CDC program usage (optional or required)

TM Rule Processing Because of the nature of data validation, a means for expressing the validation requirements which can generate the desired error messages is required There are a number of different types of rules defined Static rules which are not based on the ontology which enforce certain consistencies or perform logically complex operations. Code validation rules which are generated from the ontology by looking at the requirements defined for elements and the associated value set Data validation rules which are defined within the ontology and expressed through the use of rule templates

TM Message Content Validation Rule Implementation During knowledge engineering process we were able to implement a more efficient rule set TIMS Validation Rules DispositionCount Deleted - Message Structure19 Deleted - TIMS Specific Validation91 Deleted - Not a single message check3 Deleted - Obsolete2 Deleted - Reciprocal62 CVR Rule125 Vocabulary56 Grand Total358

TM Message Content Validation Rules Rules executed in Java Expert System Shell Efficiency of reasoning model Dynamic configuration from ontology Enables complex rule definition Ability to deal with HL/7 representations of data, and time based comparisons things such as the differences between dates Message components are broken down into defined facts and asserted into the engine Rules infer additional facts based on content such as the existence of a data validation error or alert situation Ontology extractions Message definition Vocabulary standards Rule definitions Error and alert result details

TM Message Content Validation Results View Human readable TB message processed with validation error TB message processed with MDR Alert results. Above is the xml representation, to the left, is the text representation of the same result. The results from the message content validation processing are represented in human and machine processable formats and added to the TB message for communication and storage. The results are then used to alert program staff of issues via .

TM Processing Results Average time to process a message -> 3.5 sec Capacity to process 300,000 messages a day No.# MessagesProcessing (Rhapdy In Q to CVR INBOUND Q) Processing (CVR INBOUND Q to CVR OUTBOUND Q) Sec7 Min 56 Sec Sec4 Min 14 Sec Sec2 Min 49 Sec

TM Conclusions The content validation tool is intended for use by the CDC in its TB program The baseline for functionality that will be made available to state and local TB programs Ultimately expansion of the message validation methodology to other CDC Program Areas. The functionality to generate alerts show promise for epidemiologists and can help profile for MDR or XDR drug treatment. The results from the message validation/testing and integration within DMB are indicative of the effectiveness of the alerting functions and the potential for expansion of validation tool to provide additional feedback to the reporting jurisdictions in a timely manner. Overall, the TB message validation tool was effective in identifying all tested errors and drug resistance, and creating alerts with little impact to the overall message processing Successful completion of this project will be dependent on full integration within CDC’s data warehouse scheduled for 3 quarter 2008