RESEARCH POSTER PRESENTATION DESIGN © 2012 www.PosterPresentations.com ABSTRACT Electronic syndromic surveillance (ESS) utilizes data from clinical and non-clinical sources such as emergency department (ED), consumer purchases, emergency medical services, and poison control centers for monitoring disease outbreaks (1). Compared to other methods of surveillance, ESS has a broader scope in order to focus on overall population health instead of individual disease (1). Characteristics of ESS systems include timeliness, population focus, re-identified data and unfiltered records (1). This information helps public health organizations respond to outbreaks quickly and efficiently (1). Recently, new importance has been placed on ESS to successfully comply with phases of Meaningful Use Stage 2 (1). Historically, most ESS systems have used hospital electronic health record (EHR) inpatient data as the clinical data source for syndromic surveillance (3). Recently, however, clinical outpatient data is also aiding surveillance methods (1). Ambulatory data is a new source of data that is beginning to be used for now being used for ESS (1). Two types of data captured in EHR systems include structured and narrative (1). Both of these data types are collected through a physician’s EHR (1). Ambulatory ESS data aids priority public health surveillance by reporting the volume of outpatient visits for non-reportable events (1). It also assists in assessing the morbidity and mortality of disease outbreaks with the goal of improving public health efforts overall (1). PURPOSE: To investigate and evaluate the use of ambulatory EHR data for ESS obtained from two sources: the New York City Department of Health and Mental Hygiene during the 2009 H1N1 influenza outbreak, and ambulatory clinics associated with the Kaiser Permanente Northern California health system during a 2009 gastrointestinal (GI) disease outbreak. INTRODUCTION RESULTS CONCLUSION REFERENCES ACKNOWLEDGEMENTS Special thanks to Dr. Leanne Field, the University of Texas Health Information Technology Program coordinators and Mr. James Daniel. Syndromic surveillance has been widely adopted to detect health indicators for monitoring disease outbreaks and overall health in communities. Electronic syndromic surveillance (ESS) is essential for timely reporting and responses for potential disease outbreaks. Inpatient clinical data is currently used as a detection method for syndromic surveillance. The results of this investigation showed that the new use of ambulatory data in an influenza and gastrointestinal study have provided useful information for public health officials to assess syndromic surveillance. Aleida Gamez | [email protected] Anitha Kuttemperoor | [email protected] The methods for this research project included the review of: • Primary research literature from the Centers for Disease Control and Prevention (CDC) journals accessed through PubMed. We also used publications that reported on the use and effectiveness of ESS and ambulatory EHR data to aid public health initiatives. • Agency reports and review articles focusing on the revised guidelines for the use of ambulatory EHR data for syndromic surveillance. • Lecture material provided by James Daniel, Public Health Coordinator, Office of the National Coordinator for Health IT. METHODS USEFULNESS OF AMBULATORY DATA FOR ESS OF PANDEMIC INFLUENZA AND GASTROINTESTINAL DISEASE By using both outpatient and impatient data, public health departments in these two investigations were better able to detect correlations and to respond preemptively to potential disease outbreaks (1). These results show that both structured and narrative form outpatient EHR data, have the potential to fill in the gaps left by the inpatient data in the detection process (1). During the 2009 H1N1 influenza pandemic in NYC, health department officials were able to observe significant correlations between the number of inpatient and outpatient ILI cases (2,3,5,6). These matching trends provided officials with a reliable and more accurate description of where the outbreak was in real-time (2,3,5,6). In the 2009 Northern California GI cluster, officials were able to preemptively detect a potential outbreak based on the unusually high number of stool tests being ordered in ambulatory clinics. The re-identification of these data with corresponding zip codes, allowed for an accurate designation of where the potential outbreak might be centralized (7). This is especially impressive considering that the accurate detection came from the tests ordered and not from their results. These two examples illustrate the usefulness of ambulatory data to improve real-time detection of outbreaks days before lab results are confirmed (7). ADVANTAGES AND DISADVANTAGES OF AMBULATORY DATA Advantages of utilizing ambulatory EHR data include: detection of high volumes of non-reportable diseases, lab tests ordered or exams results, and illnesses for which a patient might not visit an ED (1,7).There are, however, four main challenges to the use of ambulatory EHR data for ESS. These include: the rate of adoption of EHR technology by physicians, data management requirements that are beyond the current capacity of most public health departments, creation, implementation, and maintenance of these new technological streams, and integration of these streams with the current public health systems (1). Measures for dealing with these challenges have been proposed, and it is expected that with time, the use of ambulatory data will become standard as Meaningful Use Stage 2 compliance takes effect (1). A potential gap left by ambulatory clinic data is the lack of information during the weekends (1). This is why inpatient and outpatient data should be used in conjunction to improve the real-time information and syndrome detection received by public health departments. Because use of ambulatory data for ESS remains relatively new, we believe more studies are needed to further assess its value and to identify any potential limitations. CONTACT INFORMATION 1. International Society for Disease Surveillance (2012). Revised guidelines for syndromic surveillance using inpatient and ambulatory clinical care EHR data. ISDS, 1-73. 2. Plagianos, M.G., Wu, W.Y., McCullough, C., et al. (2011). Syndromic surveillance during pandemic (H1N1) 2009 outbreak, New York, USA. Emerg Infect Dis, 17, 1724-1726. 3. Westheimer, E., Paladini, M., Balter, S., et al. (2012). Evaluating the New York City emergency department syndromic surveillance for monitoring influenza activity during the 2009-10 influenza season. PLoS, 4. 4. Daniel, James. (2012). Meaningful Use and Public Health. Office of the National Coordinator for Health IT . (PowerPoint Presentation). 5. Hripcsak, G., Soulakis, N., Li L. et al. (2009). Syndromic Suveillance Using Ambulatory Electronic Health Records. J Am Med Inform Assoc, 16, 354-361. 6. Schirmer, P., Lucero, C., Oda, G., et al. (2010). Effective detection of the 2009 H1N1 influenza pandemic in U.S. veterans affairs medical centers using a national electronic biosurveillance system. PLoS ONE, 5. 7. Greene, S., Huang, J., Abrams, A., et al. (2012). Gastrointestinal disease outbreak detection using multiple data streams from electronic medical records. Foodborne Pathog Dis, 9, 431-441. Figure 3. Percentage of ED and Ambulatory Clinic Visits Positive for H1N1 (2) Start Date Signal Date Number of Zip Codes Observe d (O) Expecte d (E) O/E Recurrenc e Interval (days) 11/9/0 9 11/9/0 9 1 9 0.6 14.4 3,333 11/9/0 9 11/10/ 09 1 11 1.4 8.1 769 11/9/0 9 11/11/ 09 1 14 2.1 6.8 2,000 11/9/0 9 11/12/ 09 1 17 2.7 6.4 5,000 11/9/0 9 11/13/ 09 1 18 3.4 5.4 10,000 This figure displays the chronological development of disease and when the syndromic surveillance system begins to track the disease’s progress. Depending on which data sources are used, the signal may detect an outbreak well in advance of the clinically confirmed diagnosis. Figure 1. Detection Timeline for Syndromic Surveillance (4) This figure compares the percentage of H1N1- related emergency department visits (red), against the percentage of H1N1-related ambulatory clinic visits (blue), during the 2009 NYC H1N1 influenza outbreak. The outpatient and inpatient EHR data streams both displayed similar spikes in the frequency of ILI related patient visits. These findings help validate the reliability of the ambulatory data in this ESS study. Table 1. Stool Culture Tests Ordered in One Zip Code Help Illustrate a Potential Gastrointestinal Disease Outbreak (7) Figure 2. Seven Day Running Average of the ILI Visit Proportions (5) This figure shows the running averages of Influenza Like Illness (ILI) positive clinic cases, which presented during a seven day period, during the 2009 H1N1 outbreak in NYC. Although the different data sources used all seem to correlate, the structured data from the Institute for Family Health (IFH) ambulatory clinics were found to have the highest proportion of ILI-syndrome positive cases. The University of Texas at Austin Health Information Technology Certificate Program - Fall 2012 Anitha Kuttemperoor and Aleida Gomez Ambulatory EHR Data: A New Source for Electronic Syndromic Surveillance This table represents a single data stream analysis of stool culture tests ordered gathered from ambulatory EHR data over a period of five days by physicians from a single zip code. The number of reports from patients with symptoms severe enough to warrant a stool test, signified a potential cluster of GI illness and triggered an important signal for public health officials. The re-identification of data, through HIPAA compliance exemption, helped officials pinpoint the location of the cluster in this study.
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RESEARCH POSTER PRESENTATION DESIGN © 2012
www.PosterPresentations.com
ABSTRACT
Electronic syndromic surveillance (ESS) utilizes data from clinical and non-clinical sources such as emergency department (ED), consumer purchases, emergency medical services, and poison control centers for monitoring disease outbreaks (1). Compared to other methods of surveillance, ESS has a broader scope in order to focus on overall population health instead of individual disease (1). Characteristics of ESS systems include timeliness, population focus, re-identified data and unfiltered records (1). This information helps public health organizations respond to outbreaks quickly and efficiently (1). Recently, new importance has been placed on ESS to successfully comply with phases of Meaningful Use Stage 2 (1).
Historically, most ESS systems have used hospital electronic health record (EHR) inpatient data as the clinical data source for syndromic surveillance (3). Recently, however, clinical outpatient data is also aiding surveillance methods (1). Ambulatory data is a new source of data that is beginning to be used for now being used for ESS (1). Two types of data captured in EHR systems include structured and narrative (1). Both of these data types are collected through a physician’s EHR (1). Ambulatory ESS data aids priority public health surveillance by reporting the volume of outpatient visits for non-reportable events (1). It also assists in assessing the morbidity and mortality of disease outbreaks with the goal of improving public health efforts overall (1).
PURPOSE: To investigate and evaluate the use of ambulatory EHR data for ESS obtained from two sources: the New York City Department of Health and Mental Hygiene during the 2009 H1N1 influenza outbreak, and ambulatory clinics associated with the Kaiser Permanente Northern California health system during a 2009 gastrointestinal (GI) disease outbreak.
INTRODUCTION
RESULTS CONCLUSION
REFERENCES
ACKNOWLEDGEMENTSSpecial thanks to Dr. Leanne Field, the University of Texas Health Information Technology Program coordinators and Mr. James Daniel.
Syndromic surveillance has been widely adopted to detect health indicators for monitoring disease outbreaks and overall health in communities. Electronic syndromic surveillance (ESS) is essential for timely reporting and responses for potential disease outbreaks. Inpatient clinical data is currently used as a detection method for syndromic surveillance. The results of this investigation showed that the new use of ambulatory data in an influenza and gastrointestinal study have provided useful information for public health officials to assess syndromic surveillance.
Aleida Gamez | [email protected] Kuttemperoor | [email protected]
The methods for this research project included the review of:• Primary research literature from the Centers for Disease Control and Prevention (CDC) journals accessed through PubMed. We also used publications that reported on the use and effectiveness of ESS and ambulatory EHR data to aid public health initiatives.• Agency reports and review articles focusing on the revised guidelines for the use of ambulatory EHR data for syndromic surveillance. • Lecture material provided by James Daniel, Public Health Coordinator, Office of the National Coordinator for Health IT.
METHODS
USEFULNESS OF AMBULATORY DATA FOR ESS OF PANDEMIC INFLUENZA AND GASTROINTESTINAL DISEASE
By using both outpatient and impatient data, public health departments in these two investigations were better able to detect correlations and to respond preemptively to potential disease outbreaks (1). These results show that both structured and narrative form outpatient EHR data, have the potential to fill in the gaps left by the inpatient data in the detection process (1). During the 2009 H1N1 influenza pandemic in NYC, health department officials were able to observe significant correlations between the number of inpatient and outpatient ILI cases (2,3,5,6). These matching trends provided officials with a reliable and more accurate description of where the outbreak was in real-time (2,3,5,6). In the 2009 Northern California GI cluster, officials were able to preemptively detect a potential outbreak based on the unusually high number of stool tests being ordered in ambulatory clinics. The re-identification of these data with corresponding zip codes, allowed for an accurate designation of where the potential outbreak might be centralized (7). This is especially impressive considering that the accurate detection came from the tests ordered and not from their results. These two examples illustrate the usefulness of ambulatory data to improve real-time detection of outbreaks days before lab results are confirmed (7).
ADVANTAGES AND DISADVANTAGES OF AMBULATORY DATAAdvantages of utilizing ambulatory EHR data include: detection of high
volumes of non-reportable diseases, lab tests ordered or exams results, and illnesses for which a patient might not visit an ED (1,7).There are, however, four main challenges to the use of ambulatory EHR data for ESS. These include: the rate of adoption of EHR technology by physicians, data management requirements that are beyond the current capacity of most public health departments, creation, implementation, and maintenance of these new technological streams, and integration of these streams with the current public health systems (1). Measures for dealing with these challenges have been proposed, and it is expected that with time, the use of ambulatory data will become standard as Meaningful Use Stage 2 compliance takes effect (1). A potential gap left by ambulatory clinic data is the lack of information during the weekends (1). This is why inpatient and outpatient data should be used in conjunction to improve the real-time information and syndrome detection received by public health departments. Because use of ambulatory data for ESS remains relatively new, we believe more studies are needed to further assess its value and to identify any potential limitations.
CONTACT INFORMATION
1. International Society for Disease Surveillance (2012). Revised guidelines for syndromic surveillance using inpatient and ambulatory clinical care EHR data. ISDS, 1-73.
2. Plagianos, M.G., Wu, W.Y., McCullough, C., et al. (2011). Syndromic surveillance during pandemic (H1N1) 2009 outbreak, New York, USA. Emerg Infect Dis, 17, 1724-1726.
3. Westheimer, E., Paladini, M., Balter, S., et al. (2012). Evaluating the New York City emergency department syndromic surveillance for monitoring influenza activity during the 2009-10 influenza season. PLoS, 4.
4. Daniel, James. (2012). Meaningful Use and Public Health. Office of the National Coordinator for Health IT. (PowerPoint Presentation).
5. Hripcsak, G., Soulakis, N., Li L. et al. (2009). Syndromic Suveillance Using Ambulatory Electronic Health Records. J Am Med Inform Assoc, 16, 354-361.
6. Schirmer, P., Lucero, C., Oda, G., et al. (2010). Effective detection of the 2009 H1N1 influenza pandemic in U.S. veterans affairs medical centers using a national electronic biosurveillance system. PLoS ONE, 5.
7. Greene, S., Huang, J., Abrams, A., et al. (2012). Gastrointestinal disease outbreak detection using multiple data streams from electronic medical records. Foodborne Pathog Dis, 9, 431-441.
Figure 3. Percentage of ED and Ambulatory Clinic Visits Positive for H1N1 (2)
Start Date
Signal Date
Number of Zip Codes
Observed (O)
Expected (E)
O/E Recurrence Interval (days)
11/9/09 11/9/09 1 9 0.6 14.4 3,333
11/9/09 11/10/09 1 11 1.4 8.1 769
11/9/09 11/11/09 1 14 2.1 6.8 2,000
11/9/09 11/12/09 1 17 2.7 6.4 5,000
11/9/09 11/13/09 1 18 3.4 5.4 10,000
This figure displays the chronological development of disease and when the syndromic surveillance system begins to track the disease’s progress. Depending on which data sources are used, the signal may detect an outbreak well in advance of the clinically confirmed diagnosis.
Figure 1. Detection Timeline for Syndromic Surveillance (4)
This figure compares the percentage of H1N1-related emergency department visits (red), against the percentage of H1N1-related ambulatory clinic visits (blue), during the 2009 NYC H1N1 influenza outbreak. The outpatient and inpatient EHR data streams both displayed similar spikes in the frequency of ILI related patient visits. These findings help validate the reliability of the ambulatory data in this ESS study.
Table 1. Stool Culture Tests Ordered in One Zip Code Help Illustrate a Potential Gastrointestinal Disease Outbreak (7)
Figure 2. Seven Day Running Average of the ILI Visit Proportions (5)
This figure shows the running averages of Influenza Like Illness (ILI) positive clinic cases, which presented during a seven day period, during the 2009 H1N1 outbreak in NYC. Although the different data sources used all seem to correlate, the structured data from the Institute for Family Health (IFH) ambulatory clinics were found to have the highest proportion of ILI-syndrome positive cases.
The University of Texas at Austin Health Information Technology Certificate Program - Fall 2012
Anitha Kuttemperoor and Aleida GomezAmbulatory EHR Data: A New Source for Electronic Syndromic Surveillance
This table represents a single data stream analysis of stool culture tests ordered gathered from ambulatory EHR data over a period of five days by physicians from a single zip code. The number of reports from patients with symptoms severe enough to warrant a stool test, signified a potential cluster of GI illness and triggered an important signal for public health officials. The re-identification of data, through HIPAA compliance exemption, helped officials pinpoint the location of the cluster in this study.
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