Bioterrorism remains a significant threat to our public health. Early identification of an increased rate of occurance of patient presentations consistent with an exposure to an agent of bioterrorism is one important method to contain bioterror attacks and effect more rapid treatment of exposed individuals. Often presentations consistent with an exposure to an agent of bioterrorism occur in significant numbers prior to the recognition that a bioterrorism related exposure has occurred. This presents an opportunity to capture and analyze signals from patient records. We propose to provide an abstraction of automated clinical information from the clinical record (section by section) that will be coded using SNOMED-CT which can serve as the substrate for surveillance data. We believe that this data (sets of codes by section of the clinical record) which holds the important and salient medical facts (codes) regarding the patients' presentation, findings, medications, allergies and co-morbidities could be abstracted from clinical records. In this study, we will analyze SNOMED-CT's ability to provide adequate content coverage for constellations of symptoms associated with exposures to agents of bioterrorism (i.e. Anthrax, Small Pox, Ricin, and Radiation exposure). Our method builds on the considerable body of research already available within our laboratory. We have been researching methods for codifying medical content using controlled medical vocabularies since 1987. For this study, we will employ the Mayo Vocabulary Server (MVS) developed in the Mayo Laboratory of Biomedical Informatics and has been used at Mayo, Johns Hopkins University and the VA medical centers all with great success. Our performanc2 of the MVS toolkit has been validated for diagnoses where we showed a sensitivity of 99.7% and a specificity of 97.9%. This proposal deals with practical issues that lead the way toward interoperable data. The fruits of this research will assist our national initiatives to pave the way toward a safe and effective BioSecure biosurveillance solution.

Agency
National Institute of Health (NIH)
Institute
Public Health Practice Program Office (PHPPO)
Type
Research Project (R01)
Project #
1R01PH000022-01
Application #
7098649
Study Section
Special Emphasis Panel (ZPH1-SRC (99))
Program Officer
Cyril, Juliana K
Project Start
2005-09-30
Project End
2008-09-29
Budget Start
2005-09-30
Budget End
2006-09-29
Support Year
1
Fiscal Year
2005
Total Cost
$514,616
Indirect Cost
Name
Mayo Clinic, Rochester
Department
Type
DUNS #
006471700
City
Rochester
State
MN
Country
United States
Zip Code
55905
Elkin, Peter L; Froehling, David A; Wahner-Roedler, Dietlind L et al. (2012) Comparison of natural language processing biosurveillance methods for identifying influenza from encounter notes. Ann Intern Med 156:11-8
Matheny, Michael E; Fitzhenry, Fern; Speroff, Theodore et al. (2012) Detection of infectious symptoms from VA emergency department and primary care clinical documentation. Int J Med Inform 81:143-56
Elkin, Peter L; Frankel, Andrew; Liebow-Liebling, Ester H et al. (2012) Bioprospecting the Bibleome: Adding Evidence to Support the Inflammatory Basis of Cancer. Metabolomics (Los Angel) 2:
Montella, Diane; Brown, Steven H; Elkin, Peter L et al. (2011) Comparison of SNOMED CT versus Medcin terminology concept coverage for mild Traumatic Brain Injury. AMIA Annu Symp Proc 2011:969-78
Trusko, Brett; Rosenbloom, S Trent; Montella, Diane et al. (2010) Are posttraumatic stress disorder mental health terms found in SNOMED-CT medical terminology. J Trauma Stress 23:794-801
Elkin, Peter L; Froehling, David; Wahner-Roedler, Dietlind et al. (2010) The Health Archetype Language (HAL-42): interface considerations. Int J Med Inform 79:e71-5
Elkin, Peter L; Liebow, Mark; Bauer, Brent A et al. (2010) The introduction of a diagnostic decision support system (DXplain™) into the workflow of a teaching hospital service can decrease the cost of service for diagnostically challenging Diagnostic Related Groups (DRGs). Int J Med Inform 79:772-7
Rosenbloom, S Trent; Brown, Steven H; Froehling, David et al. (2009) Using SNOMED CT to represent two interface terminologies. J Am Med Inform Assoc 16:81-8
Elkin, Peter L; Tuttle, Mark S; Trusko, Brett E et al. (2009) BioProspecting: novel marker discovery obtained by mining the bibleome. BMC Bioinformatics 10 Suppl 2:S9
Matheny, Michael E; Fitzhenry, Fern; Speroff, Theodore et al. (2009) Detection of blood culture bacterial contamination using natural language processing. AMIA Annu Symp Proc 2009:411-5

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