Development and Evaluation of a Fully Automated Surveillance System for Influenza-Associated Hospitalization at a Multihospital Health System in Northeast Ohio

Abstract

Background Performing high-quality surveillance for influenza-associated hospitalization (IAH) is challenging, time-consuming, and essential.

Objectives Our objectives were to develop a fully automated surveillance system for laboratory-confirmed IAH at our multihospital health system, to evaluate the performance of the automated system during the 2018 to 2019 influenza season at eight hospitals by comparing its sensitivity and positive predictive value to that of manual surveillance, and to estimate the time and cost savings associated with reliance on the automated surveillance system.

Methods Infection preventionists (IPs) perform manual surveillance for IAH by reviewing laboratory records and making a determination about each result. For automated surveillance, we programmed a query against our Enterprise Data Vault (EDV) for cases of IAH. The EDV query was established as a dynamic data source to feed our data visualization software, automatically updating every 24 hours.

To establish a gold standard of cases of IAH against which to evaluate the performance of manual and automated surveillance systems, we generated a master list of possible IAH by querying four independent information systems. We reviewed medical records and adjudicated whether each possible case represented a true case of IAH.

Results We found 844 true cases of IAH, 577 (68.4%) of which were detected by the manual system and 774 (91.7%) of which were detected by the automated system. The positive predictive values of the manual and automated systems were 89.3 and 88.3%, respectively.

Relying on the automated surveillance system for IAH resulted in an average recoup of 82 minutes per day for each IP and an estimated system-wide payroll redirection of $32,880 over the four heaviest weeks of influenza activity.

Conclusion Surveillance for IAH can be entirely automated at multihospital health systems, saving time, and money while improving case detection.

Keywords

monitoring and surveillance - public health - patient records - influenza - human - automation

Protection of Human and Animal Subjects

Our health care system Institutional Review Board exempted this project from full review, considering it minimal risk research involving secondary data collected as part of normal health care operations.

Publikationsverlauf

Eingereicht: 02. März 2020

Angenommen: 14. Juli 2020

Artikel online veröffentlicht:
26. August 2020

• References

• 1 Heron M. Deaths: leading causes for 2013. . Natl Vital Stat Rep 2016; 65 (02) 1-95
• 2 Reed C, Chaves SS, Daily Kirley P. , et al. Estimating influenza disease burden from population-based surveillance data in the United States. PLoS One 2015; 10 (03) e0118369
• 3 Rolfes MA, Foppa IM, Garg S. , et al. Annual estimates of the burden of seasonal influenza in the United States: a tool for strengthening influenza surveillance and preparedness. Influenza Other Respir Viruses 2018; 12 (01) 132-137
• 4 CSTE. State Reportable Conditions Assessment Query Tool Version 2.1. Available at: https://www.cste.org/group/SRCAQueryRes . Accessed October 1, 2019
• 5 ODH. Influenza-associated Hospitalization. Infectious Disease Control Manual (IDCN) Section 3. Published 2018. Available at: https://odh.ohio.gov/wps/portal/gov/odh/know-our-programs/infectious-disease-control-manual/section3/section-3-flu-conditions . Accessed October 1, 2019
• 6 Konowitz PM, Petrossian GA, Rose DN. The underreporting of disease and physicians' knowledge of reporting requirements. Public Health Rep 1984; 99 (01) 31-35
• 7 Gibbons CL, Mangen M-JJ, Plass D. , et al; Burden of Communicable diseases in Europe (BCoDE) consortium. Measuring underreporting and under-ascertainment in infectious disease datasets: a comparison of methods. BMC Public Health 2014; 14 (01) 147
• 8 Doyle TJ, Glynn MK, Groseclose SL. Completeness of notifiable infectious disease reporting in the United States: an analytical literature review. Am J Epidemiol 2002; 155 (09) 866-874
• 9 de Bruin JS, Seeling W, Schuh C. Data use and effectiveness in electronic surveillance of healthcare associated infections in the 21st century: a systematic review. J Am Med Inform Assoc 2014; 21 (05) 942-951
• 10 Overhage JM, Grannis S, McDonald CJ. A comparison of the completeness and timeliness of automated electronic laboratory reporting and spontaneous reporting of notifiable conditions. Am J Public Health 2008; 98 (02) 344-350
• 11 Tsui F, Ye Y, Ruiz V, Cooper GF, Wagner MM. Automated influenza case detection for public health surveillance and clinical diagnosis using dynamic influenza prevalence method. J Public Health (Oxf) 2018; 40 (04) 878-885
• 12 Shah SC, Rumoro DP, Hallock MM. , et al. Clinical predictors for laboratory-confirmed influenza infections: exploring case definitions for influenza-like illness. Infect Control Hosp Epidemiol 2015; 36 (03) 241-248
• 13 Adnan M, Peterkin D, Lopez L, Mackereth G. Electronic sentinel surveillance of influenza-like illness. Experience from a pilot study in New Zealand. Appl Clin Inform 2017; 8 (01) 97-107
• 14 Yih WK, Cocoros NM, Crockett M. , et al. Automated influenza-like illness reporting--an efficient adjunct to traditional sentinel surveillance. Public Health Rep 2014; 129 (01) 55-63
• 15 Coelho GE, Leal PL, Cerroni Mde P, Simplicio AC, Siqueira Jr JB. Sensitivity of the dengue surveillance system in Brazil for detecting hospitalized cases. PLoS Negl Trop Dis 2016; 10 (05) e0004705
• 16 German RR. Sensitivity and predictive value positive measurements for public health surveillance systems. Epidemiology 2000; 11 (06) 720-727
• 17 Wahi MM, Dukach N. Visualizing infection surveillance data for policymaking using open source dashboarding. Appl Clin Inform 2019; 10 (03) 534-542
• 18 German RR, Lee LM, Horan JM, Milstein RL, Pertowski CA, Waller MN. . Updated guidelines for evaluating public health surveillance systems: recommendations from the Guidelines Working Group. MMWR Recomm Rep 2001. ;50(RR-13): 1-7
• 19 Burke PC, Eichmuller L, Messam M. , et al. Beyond the abacus: leveraging the electronic medical record for central line day surveillance. Am J Infect Control 2019; 47 (11) 1397-1399
• 20 Ye Y, Wagner MM, Cooper GF. , et al. A study of the transferability of influenza case detection systems between two large healthcare systems. PLoS One 2017; 12 (04) e0174970
• 21 Ferraro JP, Ye Y, Gesteland PH. , et al. The effects of natural language processing on cross-institutional portability of influenza case detection for disease surveillance. Appl Clin Inform 2017; 8 (02) 560-580
• 22 Parasrampuria S, Henry J. Hospitals' use of electronic health records data, 2015–2017. ONC Data Br 2019; (46) 1-13