Electronic Sentinel Surveillance of Influenza-like Illness

Summary

Background: Electronic reporting of Influenza-like illness (eILI) from primary care was implemented and evaluated in three general medical practices in New Zealand during May to September 2015.

Objective: To measure the uptake of eILI and to identify the system’s strength and limitations. Methods: Analysis of transactional data from the eILI system; comparative study of influenza-like illness cases reported using manual methods and eILI; questionnaire administered to clinical and operational stakeholders.

Results: Over the study period 66% of total ILI cases were reported using eILI. Reporting timeliness improved significantly compared to manual reporting with an average of 24 minutes from submission by the clinician to processing in the national database. Users found the system to be user-friendly.

Conclusion: eILI assists clinicians to report ILI cases to public health authorities within a stipulated time period and is associated with faster, more reliable and improved information transfer.

Keywords

Influenza-like Illness - Public Health - Surveillance - HL7

• References

• 1 Global Influenza Surveillance and Response System (GISRS).. World Health Organisation. Available from: http://www.who.int/influenza/gisrs_laboratory/en.
• 2 Influenza Surveillance in New Zealand 2014. Wellington: New Zealand Institute of Environmental Science and Research Ltd (ESR), 2015 18 June 2015. Available from: https://surv.esr.cri.nz/PDF_surveillance/Virology/FluAnnRpt/Influenzasurveillance2014Final.pdf
• 3 Huang QS, Bandaranayake D, Lopez LD, Pirie R, Peacey M, Hall R, Bocacao J, Adlam B, Hope V, Croxson M, Basu I. Surveillance for the 2009 pandemic influenza A (H1N1) virus and seasonal influenza viruses-New Zealand, 2009. Morbidity and Mortality Weekly Report 2009; 58 (Suppl. 33) 918-921.
• 4 Dailey L, Watkins RE, Plant AJ. Timeliness of data sources used for influenza surveillance. Journal of the American Medical Informatics Association 2007; 14 (Suppl. 05) 626-631.
• 5 Schoen C, Osborn R, Squires D, Doty M, Rasmussen P, Pierson R, Applebaum S. A survey of primary care doctors in ten countries shows progress in use of health information technology, less in other areas. Health affairs 2012; 31 (Suppl. 12) 2805-2816.
• 6 Warren J, White S, Day KJ, Gu Y, Pollock M. Introduction of electronic referral from community associated with more timely review by secondary services. Applied clinical informatics 2011; 2 (Suppl. 04) 546-564.
• 7 Liljeqvist GT, Staff M, Puech M, Blom H, Torvaldsen S. Automated data extraction from general practice records in an Australian setting: trends in influenza-like illness in sentinel general practices and emergency departments. BMC Public Health 2011; 11 (Suppl. 01) 1.
• 8 Harder KM, Andersen PH, Bæhr I, Nielsen LP, Ethelberg S, Glismann S, Molbak K. Electronic real-time surveillance for influenza-like illness: experience from the 2009 influenza A (H1N1) pandemic in Denmark. Euro Surveill 2011; 16 (Suppl. 03) pii-19767.
• 9 Yih WK, Cocoros NM, Crockett M, Klompas M, Kruskal BA, Kulldorff M, Lazarus R, Madoff LC, Morrison MJ, Smole S, Platt R. Automated Influenza-Like Illness Reporting-An Efficient Adjunct to Traditional Sentinel Surveillance. Public Health Reports. 2014 Jan 1: 129(1).
• 10 Turbelin C, Boëlle PY. Improving general practice based epidemiologic surveillance using desktop clients: the French Sentinel Network experience. Studies in health technology and informatics 2010; 160 Pt 1 442.
• 11 Adnan M, Peterkin D, Mackereth G. Development of an Electronic Notification System for Influenza-Like Illness Sentinel Surveillance. In Digital Health Innovation for Consumers, Clinicians, Connectivity and Community: Selected Papers from the 24th Australian National Health Informatics Conference (HIC 2016) 2016; 227: 1.
• 12 Adnan M, Peterkin D, McLaughlin A, Hill N. editors. HL7 Middleware Framework for Laboratory Notifications for Notifiable Diseases. In Driving Reform: Digital Health is Everyone’s Business: Selected Papers from the 23rd Australian National Health Informatics Conference (HIC 2015) 2015; 214: 1.
• 13 Tsui FC, Espino JU, Dato VM, Gesteland PH, Hutman J, Wagner MM. Technical description of RODS: a real-time public health surveillance system. Journal of the American Medical Informatics Association 2003; 10 (Suppl. 05) 399-408.
• 14 Edwards JR, Pollock DA, Kupronis BA, Li W, Tolson JS, Peterson KD, Mincey RB, Horan TC. Making use of electronic data: the National Healthcare Safety Network eSurveillance initiative. American journal of infection control 2008; 36 (Suppl. 03) S21-S26.
• 15 New Zealand Ministry of Health.. Health Information Standards Organisation. Referrals status and discharges. 2007 Available from: http://healthitboard.health.govt.nz/system/files/documents/publications/10011-3-rsd-implementation-guide-v2.pdf
• 16 Stoto MA, Fricker Jr RD, Jain A, Diamond A, Davies-Cole JO, Glymph C, Kidane G, Lum G, Jones L, Dehan K, Yuan C. Evaluating statistical methods for syndromic surveillance. In Statistical Methods in Counterterrorism. 2006. pp. 141-172. Springer; New York.:
• 17 Klaucke Klaucke DN, Buehler JW, Thacker SB, Parrish RG, Trowbridge FL, Berkelman RL. Guidelines for evaluating surveillance systems. MMWR Morb Mortal Wkly Rep 1988; 37 (Suppl. 05) 1-8.
• 18 Joffe H, Yardley L. 4 Content And Thematic Analysis. Research methods for clinical and health psychology. California: Sage; 2004: 56-58.
• 19 MacRae J, Love T, Baker MG, Dowell A, Carnachan M, Stubbe M, McBain L. Identifying influenza-like illness presentation from unstructured general practice clinical narrative using a text classifier rule-based expert system versus a clinical expert. BMC medical informatics and decision making 2015; 15 (Suppl. 01) 1.
• 20 Bender D, Sartipi K. HL7 FHIR: An Agile and RESTful approach to healthcare information exchange. In Proceedings of the 26th IEEE International Symposium on Computer-Based Medical Systems 2013 Jun 20. pp. 326-331 IEEE.;