Evaluation of Electronic Ambulatory Care Data for Influenza-Like Illness Surveillance, Washington State Kathleen Stigi, MPH; Atar Baer, PhD; Jeffrey S. Duchin, MD; Kathy Lofy, MD rrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrr
growing number of outpatient providers utilize electronic health records (EHR) to identify patient visits for influenza-like illness (ILI) but no standard query guidance exists. We applied an ILI definition validated for emergency department data to EHR from outpatient networks and found ILI visits highly correlated with influenza laboratory detections. Incorporating ambulatory EHR into our ILI surveillance system increased the capacity by more than 300%. Electronic ambulatory care data could be used to augment or replace public health surveillance systems traditionally reliant on manual reporting.
A
KEY WORDS: electronic health records, electronic medical
records, human influenza, influenza-like illness (ILI), sentinel surveillance, syndromic surveillance
The use of electronic health records (EHR) in the ambulatory care setting has increased 4-fold in the United States throughout the past decade. During 2012, 72% of office-based providers reported use of EHR systems.1 The increasing adoption of EHR within this setting provides an opportunity to amplify the capacity of public health surveillance systems traditionally reliant on manual reporting.2 National incentives available for implementing certified EHR technology has motivated providers to submit electronic data to public health authorities,3 creating a unique opportunity to improve surveillance for many conditions, including influenza and influenza-like illness (ILI). National influenza surveillance relies on multiple surveillance systems and methodologies, including J Public Health Management Practice, 2014, 20(6), 580–582 C 2014 Wolters Kluwer Health | Lippincott Williams & Wilkins Copyright
weekly tracking of viral laboratory results, influenzaassociated hospitalizations, influenza- or pneumoniaassociated deaths, and outpatient ILI visits reported through the US Outpatient Influenza-Like Illness Surveillance Network (ILINet).4 Nearly 3000 participating sentinel providers or facilities across the United States report weekly data through ILINet on patient visits for ILI, defined as fever (temperature of ≥100◦ F) with cough or sore throat, in the absence of an alternative diagnosis.4,5 Sentinel sites that utilize EHR to identify ILI visits are encouraged to query their systems using an equivalent definition4 ; however, standard guidance is lacking for identifying patients meeting these criteria in electronic ambulatory care data. A survey of Washington State ILINet providers found that 73% of providers utilized EHR within their practice and, among these, 46% of providers utilized EHR to track and report ILI visit data. Reported query methodologies varied greatly. Our objective was to determine if a syndromic ILI definition validated for use with electronic emergency department data would identify ILI trends in electronic ambulatory care data, using data from laboratories serving each clinic network as comparison.
Author Affiliations: Washington State Department of Health, Shoreline, Washington (Ms Stigi and Dr Lofy); Public Health - Seattle & King County, Seattle, Washington (Drs Baer and Duchin); and University of Washington, Seattle, Washington (Dr Duchin). The authors thank Natasha Close, Tracy Sandifer, and Phil Lowe with the Washington State Department of Health; Washington State’s ILINet sentinel providers and virology laboratories; and Enrique Ramirez with the Chicago Department of Public Health for their support. The authors declare no conflicts of interest. Correspondence: Kathleen Stigi, MPH, Washington State Department of Health, 1610 NE 150th St, MS K17-9, Shoreline, WA 98155 (kamiller@ gwmail.gwu.edu). DOI: 10.1097/PHH.0b013e3182aaa29b
580 Copyright © 2014 Lippincott Williams & Wilkins. Unauthorized reproduction of this article is prohibited.
Use of Electronic Ambulatory Data Surveillance
● Materials and Methods Using electronic data, an ILI visit was defined as the presence of any of the following in free-text and coded chief complaint or diagnosis fields: influenza or “flu”, fever plus cough, fever plus sore throat; and ICD-9 codes 464 (all), 488.1, 780 (all), or 786.2. We utilized EHR from 2 clinic networks that have submitted daily data to public health authorities in Washington State for at least 2 complete years, both via Epic EHR systems (Epicare; Epic Systems Corporation, Madison, Wisconsin). Submitted visit data included the following variables: clinic name, visit date and time, chief complaint, diagnosis, age, sex, patient zip code, and a unique patient and visit key. Clinic Network A data included all visits to 7 family practice clinics during August 2007 to August 2012. Clinic Network B data included all visits to 6 urgent care clinics during October 2010 to October 2012. Electronically submitted data from 2 large laboratories, 1 serving each clinic network, served as a proxy for influenza activity in the community, which was estimated using the number of positive weekly influenza tests. We assessed the correlation between the proportion of weekly ILI visits and the weekly number of positive influenza tests, by clinic network, individual clinics, and age categories, using Pearson productmoment correlation coefficients generated in Microsoft Excel 2007 and SAS 9.2.
● Results and Discussion Data from Clinic Network A captured 1558 884 outpatient patient visits during August 2007 to August 2012 (median number weekly visits: 5895). Of these, 11 347 visits (0.73%) met the applied ILI definition. Identified ILI visits correlated well with influenza activity in the community, both overall [correlation coefficient (r) = 0.85] (Figure 1) and by individual clinic (median r = 70.5 [range 0.62-0.84] among 7 individual clinics). Stratified by age category (in years: 0-4, 5-24, 25-49, ≥50), the correlation between ILI visits and laboratory data varied greatly (r = 0.65, 0.86, 0.83, and 0.59, respectively); identified ILI visits in the 5- to 24-year age category best correlated with influenza activity in the community. For all 5 seasons in the study, the proportion of ILI visits and percent positivity peaked at roughly the same time and followed similar patterns. From Clinic Network B, 249 490 outpatient visits during October 2010 to October 2012 were analyzed (median number weekly visits: 2331). Of these, 4070 (1.6%) met the applied ILI definition. Identified ILI visits correlated well with influenza activity in the community, overall (r = 0.89) (Figure 2) and by some individual clinics [median r = 0.78 (range 0.58-0.90) among
❘ 581
FIGURE 1 ● Proportion of visits for influenza-like illness
(ILI) and number positive influenza tests by week, Clinic Network A, Washington State, August 2007-August 2012 qqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq
6 individual clinics]. The correlation between ILI visits by age category (in years: 0-4, 5-24, 25-49, ≥50) and laboratory data showed trends similar to those seen with Network A (0.63, 0.88, 0.85, and 0.72, respectively); ILI visits in the 5- to 24-year age category best correlated with influenza activity in the community, followed by the 25- to 49-year age category. This study found an ILI definition previously validated for use with emergency department data to sufficiently detect ILI trends in electronic ambulatory care data. Consistent with previous literature,5 we found the correlation between ILI visits and influenza activity strongest in the 5- to 24-year age category. The similarity of peaks during the 2009 H1N1 pandemic underscores the robustness of our data as the strong correlation is not restricted to typical influenza seasons. There are many advantages to incorporating electronic ambulatory care data into surveillance systems traditionally reliant on manual data collection and reporting. Data are more robust and reporting is consistent and timely, while tracking ILI visits and the FIGURE 2 ● Proportion of visits for influenza-like illness
(ILI) and number positive influenza tests by week, Clinic Network B, Washington State, October 2010-October 2012 qqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq
Copyright © 2014 Lippincott Williams & Wilkins. Unauthorized reproduction of this article is prohibited.
582 ❘ Journal of Public Health Management and Practice reporting process is less labor-intensive for providers, consistent with previous literature.6 In Washington State, ILINet providers that used EHR to identify weekly reported ILI visits maintained a higher reporting participation level compared with clinics reporting data manually. The addition of clinic network into our ILINet system increased the number of weekly visits captured by more than 300%. Including robust ambulatory care data in our syndromic surveillance system, in addition to emergency department data, broadens our understanding of the impact of influenza on our community and may capture health care visits for less severe illness. Limitations include the ecological nature of this study. While medical records were not reviewed to determine the sensitivity and specificity of the definition, the goal of this analysis was to determine the value of using ambulatory data to provide situational awareness for ILI, not for syndrome validation. Another limitation is that influenza-positive laboratory tests were used as a proxy for influenza activity in the community and ILI visits may represent illness due to a diverse number of pathogens. Furthermore, clinic networks may utilize EHR systems in different ways and our findings may not be generalizable beyond the clinic networks included in this study.
● Conclusions This study illustrates that electronic ambulatory care data could be used to augment or replace public health surveillance systems such as ILINet that are traditionally reliant on manual reporting. Further work is needed to identify leading indicators of influenza
and develop standardized definitions for monitoring ILI or other conditions of public health importance. Because clinics and even individual providers utilize EHR in different ways, it is imperative to evaluate the quality of each data source before utilizing for disease surveillance. REFERENCES 1. Hsiao C-J, Hing E. Use and characteristics of electronic health record systems among office-based physician practices: United States, 2001-2012. NCHS Data Brief. 2012;(111):1–8. http://www.ncbi.nlm.nih.gov/pubmed/23384787. Accessed April 1, 2013. 2. Hripcsak G, Soulakis ND, Li L, et al. Syndromic surveillance using ambulatory electronic health records. J Am Med Inform Assoc. 2009;6(3):354-361. http://www.pubmedcentral.nih .gov/articlerender.fcgi?artid=2732227&tool=pmcentrez& rendertype=abstract. Accessed February 28, 2013. 3. Center for Disease Control and Prevention. Meaningful use: syndromic surveillance (SS). http://www.cdc.gov/ ehrmeaningfuluse/syndromic.html. Accessed March 27, 2013. 4. Center for Disease Control and Prevention. Seasonal influenza (flu): overview of influenza surveillance in the United States. http://www.cdc.gov/flu/weekly/overview.htm. Accessed March 27, 2013. 5. Brownstein JS, Kleinman KP, Mandl KD. Identifying pediatric age groups for influenza vaccination using a real-time regional surveillance system. Am J Epidemiol. 2005;162(7):686693. http://www.pubmedcentral.nih.gov/articlerender.fcgi? artid=1266301&tool=pmcentrez&rendertype=abstract. Accessed March 20, 2013. 6. Greene SK, Kulldorff M, Huang J, et al. Timely detection of localized excess influenza activity in Northern California across patient care, prescription, and laboratory data. Stat Med. 2011;30(5):549-559. http://www.pubmedcentral .nih.gov/articlerender.fcgi?artid=3058686&tool=pmcentrez& rendertype=abstract. Accessed April 1, 2013.
Copyright © 2014 Lippincott Williams & Wilkins. Unauthorized reproduction of this article is prohibited.
growing number of outpatient providers utilize electronic health records (EHR) to identify patient visits for influenza-like illness (ILI) but no standard query guidance exists. We applied an ILI definition validated for emergency department data to EHR from outpatient networks and found ILI visits highly correlated with influenza laboratory detections. Incorporating ambulatory EHR into our ILI surveillance system increased the capacity by more than 300%. Electronic ambulatory care data could be used to augment or replace public health surveillance systems traditionally reliant on manual reporting.
A
KEY WORDS: electronic health records, electronic medical
records, human influenza, influenza-like illness (ILI), sentinel surveillance, syndromic surveillance
The use of electronic health records (EHR) in the ambulatory care setting has increased 4-fold in the United States throughout the past decade. During 2012, 72% of office-based providers reported use of EHR systems.1 The increasing adoption of EHR within this setting provides an opportunity to amplify the capacity of public health surveillance systems traditionally reliant on manual reporting.2 National incentives available for implementing certified EHR technology has motivated providers to submit electronic data to public health authorities,3 creating a unique opportunity to improve surveillance for many conditions, including influenza and influenza-like illness (ILI). National influenza surveillance relies on multiple surveillance systems and methodologies, including J Public Health Management Practice, 2014, 20(6), 580–582 C 2014 Wolters Kluwer Health | Lippincott Williams & Wilkins Copyright
weekly tracking of viral laboratory results, influenzaassociated hospitalizations, influenza- or pneumoniaassociated deaths, and outpatient ILI visits reported through the US Outpatient Influenza-Like Illness Surveillance Network (ILINet).4 Nearly 3000 participating sentinel providers or facilities across the United States report weekly data through ILINet on patient visits for ILI, defined as fever (temperature of ≥100◦ F) with cough or sore throat, in the absence of an alternative diagnosis.4,5 Sentinel sites that utilize EHR to identify ILI visits are encouraged to query their systems using an equivalent definition4 ; however, standard guidance is lacking for identifying patients meeting these criteria in electronic ambulatory care data. A survey of Washington State ILINet providers found that 73% of providers utilized EHR within their practice and, among these, 46% of providers utilized EHR to track and report ILI visit data. Reported query methodologies varied greatly. Our objective was to determine if a syndromic ILI definition validated for use with electronic emergency department data would identify ILI trends in electronic ambulatory care data, using data from laboratories serving each clinic network as comparison.
Author Affiliations: Washington State Department of Health, Shoreline, Washington (Ms Stigi and Dr Lofy); Public Health - Seattle & King County, Seattle, Washington (Drs Baer and Duchin); and University of Washington, Seattle, Washington (Dr Duchin). The authors thank Natasha Close, Tracy Sandifer, and Phil Lowe with the Washington State Department of Health; Washington State’s ILINet sentinel providers and virology laboratories; and Enrique Ramirez with the Chicago Department of Public Health for their support. The authors declare no conflicts of interest. Correspondence: Kathleen Stigi, MPH, Washington State Department of Health, 1610 NE 150th St, MS K17-9, Shoreline, WA 98155 (kamiller@ gwmail.gwu.edu). DOI: 10.1097/PHH.0b013e3182aaa29b
580 Copyright © 2014 Lippincott Williams & Wilkins. Unauthorized reproduction of this article is prohibited.
Use of Electronic Ambulatory Data Surveillance
● Materials and Methods Using electronic data, an ILI visit was defined as the presence of any of the following in free-text and coded chief complaint or diagnosis fields: influenza or “flu”, fever plus cough, fever plus sore throat; and ICD-9 codes 464 (all), 488.1, 780 (all), or 786.2. We utilized EHR from 2 clinic networks that have submitted daily data to public health authorities in Washington State for at least 2 complete years, both via Epic EHR systems (Epicare; Epic Systems Corporation, Madison, Wisconsin). Submitted visit data included the following variables: clinic name, visit date and time, chief complaint, diagnosis, age, sex, patient zip code, and a unique patient and visit key. Clinic Network A data included all visits to 7 family practice clinics during August 2007 to August 2012. Clinic Network B data included all visits to 6 urgent care clinics during October 2010 to October 2012. Electronically submitted data from 2 large laboratories, 1 serving each clinic network, served as a proxy for influenza activity in the community, which was estimated using the number of positive weekly influenza tests. We assessed the correlation between the proportion of weekly ILI visits and the weekly number of positive influenza tests, by clinic network, individual clinics, and age categories, using Pearson productmoment correlation coefficients generated in Microsoft Excel 2007 and SAS 9.2.
● Results and Discussion Data from Clinic Network A captured 1558 884 outpatient patient visits during August 2007 to August 2012 (median number weekly visits: 5895). Of these, 11 347 visits (0.73%) met the applied ILI definition. Identified ILI visits correlated well with influenza activity in the community, both overall [correlation coefficient (r) = 0.85] (Figure 1) and by individual clinic (median r = 70.5 [range 0.62-0.84] among 7 individual clinics). Stratified by age category (in years: 0-4, 5-24, 25-49, ≥50), the correlation between ILI visits and laboratory data varied greatly (r = 0.65, 0.86, 0.83, and 0.59, respectively); identified ILI visits in the 5- to 24-year age category best correlated with influenza activity in the community. For all 5 seasons in the study, the proportion of ILI visits and percent positivity peaked at roughly the same time and followed similar patterns. From Clinic Network B, 249 490 outpatient visits during October 2010 to October 2012 were analyzed (median number weekly visits: 2331). Of these, 4070 (1.6%) met the applied ILI definition. Identified ILI visits correlated well with influenza activity in the community, overall (r = 0.89) (Figure 2) and by some individual clinics [median r = 0.78 (range 0.58-0.90) among
❘ 581
FIGURE 1 ● Proportion of visits for influenza-like illness
(ILI) and number positive influenza tests by week, Clinic Network A, Washington State, August 2007-August 2012 qqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq
6 individual clinics]. The correlation between ILI visits by age category (in years: 0-4, 5-24, 25-49, ≥50) and laboratory data showed trends similar to those seen with Network A (0.63, 0.88, 0.85, and 0.72, respectively); ILI visits in the 5- to 24-year age category best correlated with influenza activity in the community, followed by the 25- to 49-year age category. This study found an ILI definition previously validated for use with emergency department data to sufficiently detect ILI trends in electronic ambulatory care data. Consistent with previous literature,5 we found the correlation between ILI visits and influenza activity strongest in the 5- to 24-year age category. The similarity of peaks during the 2009 H1N1 pandemic underscores the robustness of our data as the strong correlation is not restricted to typical influenza seasons. There are many advantages to incorporating electronic ambulatory care data into surveillance systems traditionally reliant on manual data collection and reporting. Data are more robust and reporting is consistent and timely, while tracking ILI visits and the FIGURE 2 ● Proportion of visits for influenza-like illness
(ILI) and number positive influenza tests by week, Clinic Network B, Washington State, October 2010-October 2012 qqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq
Copyright © 2014 Lippincott Williams & Wilkins. Unauthorized reproduction of this article is prohibited.
582 ❘ Journal of Public Health Management and Practice reporting process is less labor-intensive for providers, consistent with previous literature.6 In Washington State, ILINet providers that used EHR to identify weekly reported ILI visits maintained a higher reporting participation level compared with clinics reporting data manually. The addition of clinic network into our ILINet system increased the number of weekly visits captured by more than 300%. Including robust ambulatory care data in our syndromic surveillance system, in addition to emergency department data, broadens our understanding of the impact of influenza on our community and may capture health care visits for less severe illness. Limitations include the ecological nature of this study. While medical records were not reviewed to determine the sensitivity and specificity of the definition, the goal of this analysis was to determine the value of using ambulatory data to provide situational awareness for ILI, not for syndrome validation. Another limitation is that influenza-positive laboratory tests were used as a proxy for influenza activity in the community and ILI visits may represent illness due to a diverse number of pathogens. Furthermore, clinic networks may utilize EHR systems in different ways and our findings may not be generalizable beyond the clinic networks included in this study.
● Conclusions This study illustrates that electronic ambulatory care data could be used to augment or replace public health surveillance systems such as ILINet that are traditionally reliant on manual reporting. Further work is needed to identify leading indicators of influenza
and develop standardized definitions for monitoring ILI or other conditions of public health importance. Because clinics and even individual providers utilize EHR in different ways, it is imperative to evaluate the quality of each data source before utilizing for disease surveillance. REFERENCES 1. Hsiao C-J, Hing E. Use and characteristics of electronic health record systems among office-based physician practices: United States, 2001-2012. NCHS Data Brief. 2012;(111):1–8. http://www.ncbi.nlm.nih.gov/pubmed/23384787. Accessed April 1, 2013. 2. Hripcsak G, Soulakis ND, Li L, et al. Syndromic surveillance using ambulatory electronic health records. J Am Med Inform Assoc. 2009;6(3):354-361. http://www.pubmedcentral.nih .gov/articlerender.fcgi?artid=2732227&tool=pmcentrez& rendertype=abstract. Accessed February 28, 2013. 3. Center for Disease Control and Prevention. Meaningful use: syndromic surveillance (SS). http://www.cdc.gov/ ehrmeaningfuluse/syndromic.html. Accessed March 27, 2013. 4. Center for Disease Control and Prevention. Seasonal influenza (flu): overview of influenza surveillance in the United States. http://www.cdc.gov/flu/weekly/overview.htm. Accessed March 27, 2013. 5. Brownstein JS, Kleinman KP, Mandl KD. Identifying pediatric age groups for influenza vaccination using a real-time regional surveillance system. Am J Epidemiol. 2005;162(7):686693. http://www.pubmedcentral.nih.gov/articlerender.fcgi? artid=1266301&tool=pmcentrez&rendertype=abstract. Accessed March 20, 2013. 6. Greene SK, Kulldorff M, Huang J, et al. Timely detection of localized excess influenza activity in Northern California across patient care, prescription, and laboratory data. Stat Med. 2011;30(5):549-559. http://www.pubmedcentral .nih.gov/articlerender.fcgi?artid=3058686&tool=pmcentrez& rendertype=abstract. Accessed April 1, 2013.
Copyright © 2014 Lippincott Williams & Wilkins. Unauthorized reproduction of this article is prohibited.
