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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 , MSCecil.o.lynch@accenture.co

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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

www.ontoreason.com

ccunningham@avenidait.com

www.avenidait.com

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

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Project CharterPHIN 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

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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

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Deployment Architecture

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

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OTR Reasoning FrameworkOTR Reasoning Framework

Message ParsingReasoner

Message ParsingReasonerInbound MSG

Queue

Inbound MSG Queue

Message ValidationReasoner

Message ValidationReasoner

Alert ReasonerAlert Reasoner

Knowledge RegistryKnowledge Registry

JMS

Inte

rfac

eJM

S In

terf

ace

Outbound ResultsQueue

Outbound ResultsQueue

Semantic Knowledge StoreSemantic Knowledge Store

DM

BD

MB

Message Content Validation Architecture

Visualization Tools

Public Health Ontology Extraction for TB Msg.

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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

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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.

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HL7 Message Artifact Taxonomy

• Structured taxonomy• Related to codeSystem• Related to TB Question• Carries the attribute of code

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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)

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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

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Message Content Validation Rule Implementation

• During knowledge engineering process we were able to implement a more efficient rule set

TIMS Validation Rules Disposition Count

Deleted - Message Structure 19Deleted - TIMS Specific Validation 91Deleted - Not a single message check 3

Deleted - Obsolete 2

Deleted - Reciprocal 62

CVR Rule 125

Vocabulary 56

Grand Total 358

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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

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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 email.

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Processing Results

• Average time to process a message -> 3.5 sec• Capacity to process 300,000 messages a day

No. # Messages Processing Time @Rhapsody

(Rhapdy In Q to CVR INBOUND Q)

Processing time @CVR (CVR

INBOUND Q to CVR OUTBOUND Q)

1 1670 37 Sec 7 Min 56 Sec2 820 16 Sec 4 Min 14 Sec3 620 10 Sec 2 Min 49 Sec

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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