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A Scenario to Conceptually Illustrate

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Presentation on theme: "A Scenario to Conceptually Illustrate"— Presentation transcript:

1 A Scenario to Conceptually Illustrate
The Utility of the Public Health Grid Public Health Grid Atlanta BCD, Session B1 25 August 2008 Zaruhi Mnatsakanyan1, Nedra Garrett2 Joe Lombardo1, John Stinn3 1Johns Hopkins University Applied Physics Laboratory 2Centers for Disease Control and Prevention 3Bearing Point Management and Technology Consultants This presentation was supported by Grant Number P01 HK from the Centers for Disease Control and Prevention. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of CDC.

2 Scenario Introduction
Increasing evidence of a national outbreak of salmonella thought to be linked to the production and distribution of tomatoes. What existing surveillance capabilities support a rapid determination of the source and extent of the outbreak? How does Decision Support Technologies support a rapid understanding of an emerging PH threat? What additional capabilities could services on a PHGrid provide to resolve the problem?

3 Evolution of the Public Health Electronic Surveillance
Syndromic Surveillance Disease Surveillance Situational Awareness & Response Data Counts Classified by Syndromes: • Respiratory • Neurological • GI • etc. Anomaly Detection Lab Requests Radiology Requests ICD9 Rx Data Fusion on Individual Level EMR Population Level Non-health information Such as product supply. Lab Requests Requests Radiology ICD9 Rx Non-Linked Data Table of the Records with Severity Score (Probability of the Disease of Interest) Data Fusion on Individual Level Data Anomaly Detection by Age Groups EMR Population Level Detects outbreaks with large number of visits. Detects targeted diseases with relatively small number of cases. Identify reasons causing outbreaks.

4 Existing Surveillance Technologies
Syndromic Surveillance Disease Surveillance Situational Awareness & Response Anomaly Detection Syndromic Surveillance Data Counts Classified by Syndromes: • Respiratory • Neurological • GI • etc. Anomaly Detection Lab Requests Radiology Requests ICD9 Rx Data Fusion on Individual Level EMR Population Level Non-health information Such as product supply. Data Counts Classified by Syndromes: • Respiratory • Neurological • GI • etc. Lab Requests Requests Radiology ICD9 Rx Non-Linked Data Table of the Records with Severity Score (Probability of the Disease of Interest) Data Fusion on Individual Level Data Anomaly Detection by Age Groups EMR Population Level Detects outbreaks with large number of visits. Detects targeted diseases with relatively small number of cases. Identify reasons causing outbreaks. Detects outbreaks with large number of visits.

5 (Probability of the Disease of Interest) Data Anomaly Detection
Probabilistic Reasoning, Fusion, and the Electronic Medical Record (Highly Structured Formats) Syndromic Surveillance Disease Surveillance Situational Awareness & Response Disease Surveillance Data Counts Classified by Syndromes: • Respiratory • Neurological • GI • etc. EMR Anomaly Detection Lab Requests ICD9 Lab Requests Radiology Requests ICD9 Rx Data Fusion on Individual Level EMR Population Level Non-health information Such as product supply. Lab Requests Requests Radiology ICD9 Rx Non-Linked Data Table of the Records with Severity Score (Probability of the Disease of Interest) Data Fusion on Individual Level Data Anomaly Detection by Age Groups EMR Population Level Non-Linked Data Radiology Requests Rx Table of the Records with Severity Score (Probability of the Disease of Interest) Data Fusion on Individual Level Data Fusion on Population Level Data Anomaly Detection by Age Groups Detects outbreaks with large number of visits. Detects targeted diseases with relatively small number of cases. Identify reasons causing outbreaks. Detects targeted diseases with relatively small number of cases.

6 Knowledge Discovery (Textural Formats)
Syndromic Surveillance Disease Surveillance Situational Awareness & Response Situational Awareness & Response Data Counts Classified by Syndromes: • Respiratory • Neurological • GI • etc. Lab Requests Radiology Requests ICD9 Rx Data Fusion on Individual Level EMR Population Level Non-health information Such as product supply. Anomaly Detection Lab Requests Radiology Requests ICD9 Rx Data Fusion on Individual Level EMR Population Level Non-health information Such as product supply. Lab Requests Requests Radiology ICD9 Rx Non-Linked Data Table of the Records with Severity Score (Probability of the Disease of Interest) Data Fusion on Individual Level Data Anomaly Detection by Age Groups EMR Population Level Detects outbreaks with large number of visits. Detects targeted diseases with relatively small number of cases. Identify reasons causing outbreaks. Identify reasons causing outbreaks.

7 Potential Resource, a Public Health Grid

8 Objectives of This Presentation
Utilization of knowledge gathering service to enhance public health surveillance in a grid environment. Use information fusion techniques to integrate heterogeneous data and information in a grid environment.

9 CDC’s Decision Augmentation Service
Decision Support Framework Services Get Clinical Related Terms Web Services UMLS Vocabulary Service Any internal application/search/search appliance can supply an interface to return results to the KGS .NET Web Service Internal Websites Internal Appliance or Other Search Appliances Knowledge Gathering Service (KGS) External Websites Indexed Content / Data Expand Terms (UMLS) Process Search Criteria Send Search Request Federate Results Return Results Your Application .NET Web Service Java, .NET, or any WS* compliant technology Search Appliance RSS Feeds Currently indexing many public health websites CHT Vocabulary Service Databases Get Public Health Related Terms SQL Server .NET Web Service Controlled Health Thesaurus All services are accessible by any application within the CDC File Systems

10 Intelligent Decision Support Service (NCPHI / COE Grid Service)
JHU/APL Demo Client Web Application / Portal Requests Interaction Data Management User Interfaces Future PHGrid Resources Computational

11 “Now, with the tomato industry in tatters
and the jalapeno pepper and cilantro growers facing a consumer uprising, it should be clear that traceability is both good public healthy policy and good business.” NY Times Editorial Board 7/8/08

12 Grid Service Data / Information Flow CDC Decision Augmentation Service
InfoShare* Service GLOBUS Grid Support Functions Grid Client Requests Interaction Data Management User Interfaces WWW EMERGE* Data Service IDSS* Service Knowledge Gathering Service GLOBUS Grid Support Functions CHT Service GLOBUS GLOBUS Grid Support Functions GLOBUS Grid Support Functions UMLS Service GLOBUS Data Bases CDC Decision Augmentation Service * On the JHU/APL / NCPHI Grid

13 Acquiring HIE Surveillance Data CDC Decision Augmentation Service
Exposed to the PHGrid InfoShare* Service GLOBUS Grid Support Functions Grid Client Requests Interaction Data Management User Interfaces WWW EMERGE* Data Service IDSS* Service Knowledge Gathering Service GLOBUS Grid Support Functions CHT Service GLOBUS GLOBUS Grid Support Functions GLOBUS Grid Support Functions Health Department identifies high levels of GI in local syndromic surveillance system HD Grid user requests EMR data from their HIE data grid service UMLS Service GLOBUS HIE Data Bases CDC Decision Augmentation Service * On the JHU/APL / NCPHI Grid

14

15 Obtaining a Preliminary Case Definition
Using Probabilistic Reasoning and Sharing Results with a Knowledge Gathering Service InfoShare* Service GLOBUS Grid Support Functions Grid Client Requests Interaction Data Management User Interfaces WWW EMERGE* Data Service IDSS* Service Knowledge Gathering Service GLOBUS Grid Support Functions CHT Service GLOBUS GLOBUS Grid Support Functions GLOBUS Grid Support Functions UMLS Service GLOBUS Data Bases CDC Decision Augmentation Service * On the JHU/APL / NCPHI Grid

16 Knowledge Gathering to Support Investigation and Situational Awareness
InfoShare* Service GLOBUS Grid Support Functions Grid Client Requests Interaction Data Management User Interfaces WWW EMERGE* Data Service IDSS* Service Knowledge Gathering Service GLOBUS Grid Support Functions CHT Service GLOBUS GLOBUS Grid Support Functions GLOBUS Grid Support Functions UMLS Service GLOBUS Data Bases CDC Decision Augmentation Service * On the JHU/APL / NCPHI Grid

17 Decision Augmentation Services
UMLS & CHT

18 Client View of Patient Severity from IDSS and
Preliminary Differential Diagnosis from DAS Fusion of EMR data suggests possible Salmonella, Obtains a possible initial case definition from IDSS and passed the information to DAS to get differe

19 Sharing and Obtaining Information in a
Federated Surveillance Environment InfoShare* Service GLOBUS Grid Support Functions Grid Client Requests Interaction Data Management User Interfaces WWW EMERGE* Data Service IDSS* Service Knowledge Gathering Service GLOBUS Grid Support Functions CHT Service GLOBUS GLOBUS Grid Support Functions GLOBUS Grid Support Functions Infoshare identifies similar activity in other surveillance systems. Requests auto analysis outputs from info share. by requesting automated surveillance UMLS Service GLOBUS Data Bases CDC Decision Augmentation Service * On the JHU/APL / NCPHI Grid

20 Creating a Federated Surveillance Environment

21 Knowledge Gathering to Support Investigation and Situational Awareness
InfoShare* Service GLOBUS Grid Support Functions Grid Client Requests Interaction Data Management User Interfaces WWW EMERGE* Data Service IDSS* Service Knowledge Gathering Service GLOBUS Grid Support Functions CHT Service GLOBUS GLOBUS Grid Support Functions GLOBUS Grid Support Functions UMLS Service GLOBUS Data Bases CDC Decision Augmentation Service * On the JHU/APL / NCPHI Grid

22 Sharing and Obtaining Information in a
Federated Surveillance Environment InfoShare* Service GLOBUS Grid Support Functions Grid Client Requests Interaction Data Management User Interfaces WWW EMERGE* Data Service IDSS* Service Knowledge Gathering Service GLOBUS Grid Support Functions CHT Service GLOBUS GLOBUS Grid Support Functions GLOBUS Grid Support Functions Infoshare identifies similar activity in other surveillance systems. Requests auto analysis outputs from info share. by requesting automated surveillance UMLS Service GLOBUS Data Bases CDC Decision Augmentation Service * On the JHU/APL / NCPHI Grid

23 Conclusions What we presented is a concept for how complementary decision support services could be used to assist in an investigation. The next step is to develop the linkages between the services, utilize the tools in an operational surveillance environment and evaluate the enhanced capability.

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