Advertisement

Using Emergency Department Data For Biosurveillance: The North Carolina Experience

  • Anna E. Waller
  • Matthew Scholer
  • Amy I. Ising
  • Debbie A. Travers
Chapter
Part of the Integrated Series in Information Systems book series (ISIS, volume 27)

Chapter Overview

Biosurveillance is an emerging field that provides early detection of disease outbreaks by collecting and interpreting data on a variety of public health threats. The public health system and medical care community in the United States have wrestled with developing new and more accurate methods for earlier detection of threats to the health of the public. The benefits and challenges of using Emergency Department data for surveillance are described in this chapter through examples from one biosurveillance system, the North Carolina Disease Event Tracking and Epidemiologic Collection Tool (NC DETECT). ED data are a proven tool for biosurveillance, and the ED data in NC DETECT have proved to be effective for a variety of public health uses, including surveillance, monitoring and investigation. A distinctive feature of ED data for surveillance is their timeliness. With electronic health information systems, these data are available in near real-time, making them particularly useful for surveillance and situational awareness in rapidly developing public health outbreaks or disasters. Challenges to using ED data for biosurveillance include the reliance on free text data (often in chief complaints). Problems with textual data are addressed in a variety of ways, including preprocessing data to clean the text entries and address negation.

The use of ED data for public health surveillance can significantly increase the speed of detecting, monitoring and investigating public health events. Biosurveillance systems that are incorporated into hospital and public health practitioner daily work flows are more effective and easily used during a public health emergency. The flexibility of a system such as NC DETECT helps it meet this level of functionality.

Keywords

Emergency Department Visit Chief Complaint Syndromic Surveillance Public Health Surveillance Infectious Disease Outbreak 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Aylin P, Bottle A, Majeed A. Use of Administrative data or clinical databases as predictors of risk of death in hospital: comparison of models. BMJ 2007; 334:1044. Originally published online 23 Apr 2007.PubMedCrossRefGoogle Scholar
  2. Baer A, Jackson M, Duchin JS. What is the value of a positive syndromic surveillance signal? Advances in Disease Surveillance 2007; 2:192.Google Scholar
  3. Barnett C, Deyneka L, Waller AE. Post-Katrina situational awareness in North Carolina. Advances in Disease Surveillance 2006; 2:142.Google Scholar
  4. Beitel AJ, Olson KL, Reis BY, Mandl KD. Use of emergency department chief complaint and diagnostic codes for identifying respiratory illness in a pediatric population. Pediatric Emergency Care 2004; 20(6):355–360.PubMedCrossRefGoogle Scholar
  5. Buehler JW, Hopkins RS, Overhage JM, Sosin DM, Tong V. Framework for evaluating public health surveillance systems for early detection of outbreaks. MMWR. Recommendations and Reports 2004; 53(RR05):1–11.Google Scholar
  6. Centers for Disease Control and Prevention. Morbidity and mortality associated with Hurricane Floyd — North Carolina, September–October 1999. MMWR. Morbidity and Mortality Weekly Report 2000; 49(17):369–372.Google Scholar
  7. Centers for Disease Control and Prevention (October 23, 2003). Syndrome definitions for diseases associated with critical bioterrorism-associated agents. Available at http://www.bt.cdc.gov/surveillance/syndromedef/index.asp. Accessed June 24, 2005.***
  8. Chapman WW, Bridewell W, Hanbury P, Cooper GF, Buchanan BG. A simple algorithm for identifying negated findings and diseases in discharge summaries. Journal of Biomedical Informatics 2001; 34(5):301–310.PubMedCrossRefGoogle Scholar
  9. Chapman WW and Dowling JN. Consultative meeting on chief complaint classifiers and standardized syndromic definitions. Advances in Disease Surveillance 2007; 4.Google Scholar
  10. Dara J, Dowling JN, Travers D, Cooper GF, Chapman WW. Chief complaint preprocessing evaluated on statistical and non-statistical classifiers. Advances in Disease Surveillance 2007; 2:4.Google Scholar
  11. Davis MV, MacDonald PDM, Cline JS, Baker EL. Evaluation of public health response to hurricanes finds North Carolina better prepared for public health emergencies. Public Health Reports 2007; 122:17–26.PubMedGoogle Scholar
  12. Davis SF, Strebel PM, Atkinson WL, Markowitz LE, Sutter RW, Scanlon KS, Friedman S, Hadler SC. Reporting efficiency during a measles outbreak in New York City, 1991. American Journal of Public Health 1993; 83(7):1011–1015.PubMedCrossRefGoogle Scholar
  13. Espino JU, Dowling JN, Levander J, Sutovsky P, Wagner MM, Cooper GF. SyCo: A probabilistic machine learning method for classifying chief complaints into symptom and syndrome categories. Advances in Disease Surveillance 2007; 2:5.Google Scholar
  14. Fleischauer AT, Silk BJ, Schumacher M, Komatsu K, Santana S, Vaz V, et al. The validity of chief complaint and discharge diagnosis in emergency-based syndromic surveillance. Academic Emergency Medicine 2004; 11(12):1262–1267.Google Scholar
  15. Forbach C, Scholer MJ, Falls D, Ising A, Waller A. Improving system ability to identify symptom complexes in free-text data. Advances in Disease Surveillance 2007; 2:7.Google Scholar
  16. Hakenewerth A, Waller A, Ising A, Tinitinalli J. NC DETECT and NHAMCS: Comparison of emergency department data. (Submitted for publication June 2008).Google Scholar
  17. Heffernan R, Mostashari F, Das D, Besculides M, Rodriguez C, Greenko J, et al. New York City syndromic surveillance systems. MMWR. Morbidity and Mortality Weekly Report Supplement 2004; 24(53):23–27.Google Scholar
  18. Heffernan R, Mostashari F, Das D, Karpati A, Kulldorff M, Weiss D. Syndromic surveillance in public health practice, New York City. Emerg Infect Dis [serial on the Internet]. 2004 May http://www.cdc.gov/ncidod/EID/vol10no5/03-0646.htm. Accessed July 20, 2007.
  19. Hirshon JM. For the SAEM Public Health Task Force Preventive Care project. The rational for developing public health surveillance systems based on emergency department data. Academic Emergency Medicine 2000; 7:1428–1432.PubMedCrossRefGoogle Scholar
  20. Hripscak G, Bamberger A, Friedman C. Fever detection in clinic visit notes using a general purpose processor. Advances in Disease Surveillance 2007; 2:14.Google Scholar
  21. Hutwagner L, Thompson W, Seeman GM, Treadwell T. The bioterrorism preparedness and response Early Aberration Reporting System. Journal of Urban Health. 2003; 80 (2 Suppl 1):i89–i96.PubMedGoogle Scholar
  22. Ising A, Li M, Deyneka L, Barnett C, Scholer M, Waller A. Situational awareness using web-based annotation and custom reporting. Advances in Disease Surveillance 2007; 4:167.Google Scholar
  23. Ising A, Travers D, Crouch J, Waller A. Improving negation processing in triage notes. Advances in Disease Surveillance 2007; 4:50.Google Scholar
  24. Ising AI, Travers DA, MacFarquhar J, Kipp A, Waller A. Triage note in emergency department-based syndromic surveillance. Advances in Disease Surveillance 2006; 1:34.Google Scholar
  25. Kaufman AF, Meltzer MI, Schmid GF. Economic impact of a bioterrorist attack: are prevention and postattack intervention programs justifiable? Emerging Infectious Diseases 1997; 3:83–94.CrossRefGoogle Scholar
  26. Komatsu K, Trujillo L, Lu HM, Zeng D, Chen H. Ontology-based automatic chief complaints classification for syndromic surveillance. Advances in Disease Surveillance 2007; 2:17.Google Scholar
  27. Lee LE, Fonseca V, Brett KM, Sanchez J, Mullen RC, Quenemoen LE, Groseclose SL, Hopkins RS. Active morbidity surveillance after Hurricane Andrew--Florida, 1992. JAMA 1993; 270(5):591–594. Erratum in: JAMA 1993; 270(19):2302.PubMedCrossRefGoogle Scholar
  28. Lober WB, Karras BT, Wagner MM, et al. Roundtable on bioterrorism detection: information system-based surveillance. Journal of the American Medical Informatics Association 2002; 9(2):105–115.PubMedCrossRefGoogle Scholar
  29. Lombardo J. A systems overview of the Electronic Surveillance System for Early Notification of Community-based Epidemics (ESSENCE II). Journal of Urban Health 2003; 80(2 Suppl 1):i32–i42.Google Scholar
  30. Mikosz CA, Silva J, Black S, Gibbs G, Cardenas I. Comparison of two major emergency department-based free-text chief-complaint coding systems. MMWR. Morbidity and Mortality Weekly Report Supplement 2004; 53S:101–105.Google Scholar
  31. National Center for Injury Prevention and Control (US). Data elements for emergency department systems, release 1.0. Atlanta, GA: Centers for Disease Control; 1997.Google Scholar
  32. National Library of Medicine (2006). Specialist Lexicon and Lexical Tools Documentation, 2006AD. Retrieved May 11, 2007 from http://www.nlm.nih.gov/research/umls/meta4.html.
  33. National Library of Medicine (2007). Unified Medical Language System Documentation,2007AA. Bethesda, MD: National Library of Medicine. Retrieved May 11, 2007 from http://www.nlm.nih.gov/research/umls/meta2.html.
  34. Nawar EW, Niska RW, Xu J. (2007). National Hospital Ambulatory Medical Care Survey:2005 Emergency Department Summary. Advance data from vital and health statistics: no.386. Hyattsville, MD: National Center for Health Statistics.Google Scholar
  35. North Carolina General Statute 130A. http://www.ncleg.net/EnactedLegislation/Statutes/HTML/ByChapter/Chapter_130A.html Accessed January 17, 2008.
  36. Olson DR, Heffernan RT, Paladini M, Konty K, Weiss D, Mostashari F. Monitoring the impact of influenza by age: emergency department fever and respiratory complaint surveillance in New York City. PLoS Medicine. 2007; 4(8):e247.PubMedCrossRefGoogle Scholar
  37. Reis BY, Mandl KD. Syndromic surveillance: The effects of syndrome grouping on model accuracy and outbreak detection. Annals of Emergency Medicine 2004; 44:235–241.PubMedCrossRefGoogle Scholar
  38. Rodewald LE, Wrenn KD, Slovis CM. A method for developing and maintaining a powerful but inexpensive computer data base of clinical information about emergency department patients. Annals of Emergency Medicine 1992; 21:41–46.PubMedCrossRefGoogle Scholar
  39. Rydman RJ, Rumoro DP, Silva JC, Hogan TM, Kampe LM. The rate and risk of heat-related illness in hospital emergency departments during the 1995 Chicago heat disaster. Journal of Medical Systems 1999; 23(1):41–56.PubMedCrossRefGoogle Scholar
  40. Shapiro A. Taming variability in free text: Application to health surveillance. MMWR. Morbidity and Mortality Weekly Report 2004; 24(53S):95–100.Google Scholar
  41. Sosin DM, DeThomasis J. Evaluation challenges for syndromic surveillance — Making incremental progress. MMWR. Morbidity and Mortality Weekly Report 2004; 53S:125–129.Google Scholar
  42. Stern L, Lightfoot D. Automated outbreak detection: a quantitative retrospective analysis. Epidemiology & Infection 1999; 122:103–110.CrossRefGoogle Scholar
  43. Talan DA, Moran GJ, Mower WR, Newdow M, Ong S, Slutsker L, Jarvis WR, Conn LA, Pinner RW. EMERGEncy ID NET: an emergency department-based emerging infections sentinel network. The EMERGEncy ID NET Study Group. Annals of Emergency Medicine 1998; 32:703–711.PubMedCrossRefGoogle Scholar
  44. Teich JM, Wagner MM, Mackenzie CF, Schafer KO. The informatics response in disaster, terrorism and war. Journal of the American Medical Informatics Association 2002;9(2):97–104.PubMedCrossRefGoogle Scholar
  45. Travers DA. (2006). Emergency Medical Text Processor. Accessed on June 10, 2008 from http://nursing.unc.edu/research/current/emtp/.
  46. Travers DA, Barnett C, Ising A, Waller A. (2006). Timeliness of emergency department diagnoses for syndromic surveillance. Proceedings of the AMIA Symposium 2006, 769–773.Google Scholar
  47. Travers DA, Haas SW. Using nurses natural language entries to build a concept-oriented terminology for patients’ chief complaints in the emergency department. Journal of Biomedical Informatics 2003; 36:260–270.PubMedCrossRefGoogle Scholar
  48. Travers DA, Waller A, Haas S, Lober WB, Beard C. (2003). Emergency department data for bioterrorism surveillance: Electronic availability, timeliness, sources and standards. Proceedings of the AMIA Symposium 2003, 664–668.Google Scholar
  49. Tsui FC, Wagner MM, Dato V, Chang C. (2001) Value of ICD-9-coded chief complaints for detection of epidemics. Proceedings of the Fall Symposium of the American Medical Informatics Association 2001, 711–715.Google Scholar
  50. U.S. Department of Health and Human Services (USDHHS). International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM). 6th ed. Washington: Author;2006.Google Scholar
  51. Varney SM Hirshon JM. Update on public health surveillance in emergency departments. Emergency Medicine Clinics of North America 2006; 24:1035–1052.PubMedCrossRefGoogle Scholar
  52. Waller AE, Ising AI, Deneka L. North Carolina Biosurveillance System. Wiley Handbook for Science and Technology for Homeland Security. Book chapter, due to be published summer, 2008. Submitted January 2008, under review.Google Scholar
  53. Waller AE, Ising AI, Deyneka L. North Carolina Emergency Department visit data available for public health surveillance. North Carolina Medical Journal 2007; 68(4):289–291.PubMedGoogle Scholar
  54. Wagner MM, Espino J, Tsui FC, et al. Syndrome and outbreak detection using chief-complaint data — experience of the Real-Time Outbreak and Disease Surveillance project. MMWR. Morbidity and Mortality Weekly Report 2004; 24(53S):28–31.Google Scholar
  55. Weiss BP, Mascola L, Fannin SL. Public health at the 1984 Summer Olympics: the LosAngeles County experience. American Journal of Public Health 1988; s78(6):686–688.CrossRefGoogle Scholar

Suggested Reading

  1. Hirshon JM. For the SAEM Public Health Task Force Preventive Care project. The rational for developing public health surveillance systems based on emergency department data. Academic Emergency Medicine 2000; 7:1428–1432.PubMedCrossRefGoogle Scholar
  2. Varney SM, Hirshon JM. Update on public health surveillance in emergency departments. Emergency Medicine Clinics of North America 2006; 24:1035–1052.PubMedCrossRefGoogle Scholar
  3. Buehler JW, Hopkins RS, Overhage JM, Sosin DM Tong V. Framework for evaluating public health surveillance systems for early detection of outbreaks. MMWR. Recommendations and Reports 2004; 53(RR05):1–11.Google Scholar
  4. Fleischauer AT, Silk BJ, Schumacher M, Komatsu K, Santana S, Vaz V, et al. The validity of chief complaint and discharge diagnosis in emergency-based syndromic surveillance. Academic Emergency Medicine 2004; 11(12):1262–1267.PubMedGoogle Scholar
  5. Centers for Disease Control and Prevention (October 23, 2003). Syndrome definitions for diseases associated with critical bioterrorism-associated agents. Available at http://www.bt.cdc.gov/surveillance/syndromedef/index.asp. Accessed June 24, 2005.
  6. Nawar EW, Niska RW, Xu J. (2007). National Hospital Ambulatory Medical Care Survey:2005 Emergency Department Summary. Advance data from vital and health statistics: no.386. Hyattsville, MD: National Center for Health Statistics.Google Scholar

Online Resources

  1. Website for the International Society for Disease Surveillance (http://www.syndromic.org) This website includes the online journal Advances in Disease Surveillance, as well as a variety of wikis addressing research and public health topics.
  2. NC DETECT website (http://www.ncdetect.org) The NC DETECT website contains links to numerous abstracts and presentations related to ED data use for biosurveillance, as well as details on the technical components of NC DETECT.

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Anna E. Waller
    • 1
  • Matthew Scholer
  • Amy I. Ising
  • Debbie A. Travers
  1. 1.Carolina Center for Health Informatics, Department of Emergency MedicineUniversity of North Carolina at Chapel HillChapel HillUSA

Personalised recommendations