scholarly journals Data Quality Improvements in National Syndromic Surveillance Program (NSSP) Data

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Girum S. Ejigu ◽  
Kakshmi Radhakrishnan ◽  
Paul McMurray ◽  
Roseanne English
Keyword(s):  
Data Quality ◽  
Syndromic Surveillance ◽  
Data Flow ◽  
Chief Complaint ◽  
Surveillance Program ◽  
Discharge Diagnosis ◽  
Standard Data ◽  
Quality Checks ◽  
State And Local ◽  
Data Elements

ObjectiveReview the impact of applying regular data quality checks to assess completeness of core data elements that support syndromic surveillance.IntroductionThe National Syndromic Surveillance Program (NSSP) is a community focused collaboration among federal, state, and local public health agencies and partners for timely exchange of syndromic data. These data, captured in nearly real time, are intended to improve the nation's situational awareness and responsiveness to hazardous events and disease outbreaks. During CDC’s previous implementation of a syndromic surveillance system (BioSense 2), there was a reported lack of transparency and sharing of information on the data processing applied to data feeds, encumbering the identification and resolution of data quality issues. The BioSense Governance Group Data Quality Workgroup paved the way to rethink surveillance data flow and quality. Their work and collaboration with state and local partners led to NSSP redesigning the program’s data flow. The new data flow provided a ripe opportunity for NSSP analysts to study the data landscape (e.g., capturing of HL7 messages and core data elements), assess end-to-end data flow, and make adjustments to ensure all data being reported were processed, stored, and made accessible to the user community. In addition, NSSP extensively documented the new data flow, providing the transparency the community needed to better understand the disposition of facility data. Even with a new and improved data flow, data quality issues that were issues in the past, but went unreported, remained issues in the new data. However, these issues were now identified. The newly designed data flow provided opportunities to report and act on issues found in the data unlike previous versions. Therefore, an important component of the NSSP data flow was the implementation of regularly scheduled standard data quality checks, and release of standard data quality reports summarizing data quality findings.MethodsNSSP data was assessed for the national-level completeness of chief complaint and discharge diagnosis data. Completeness is the rate of non- null values (Batini et al., 2009). It was defined as the percent of visits (e.g., emergency department, urgent care center) with a non-null value found among the one or more records associated with the visit. National completeness rates for visits in 2016 were compared with completeness rates of visits in 2017 (a partial year including visits through August 2017). In addition, facility-level progress was quantified after scoring each facility based on the percent completeness change between 2016 and 2017. Legacy data processed prior to introducing the new NSSP data flow were not included in this assessment.ResultsNationally, the percent completeness of chief complaint for visits in 2016 was 82.06% (N=58,192,721), and the percent completeness of chief complaint for visits in 2017 was 87.15% (N=80,603,991). Of the 2,646 facilities that sent visits data in 2016 and 2017, 114 (4.31%) facilities showed an increase of at least 10% in chief complaint completeness in 2017 compared with 2016. As for discharge diagnosis, national results showed the percent completeness of discharge diagnosis for 2016 visits was 50.83% (N=36,048,334), and the percent completeness of discharge diagnosis for 2017 was 59.23% (N=54,776,310). Of the 2,646 facilities that sent data for visits in 2016 and 2017, 306 (11.56%) facilities showed more than a 10% increase in percent completeness of discharge diagnosis in 2017 compared with 2016.ConclusionsNationally, the percent completeness of chief complaint for visits in 2016 was 82.06% (N=58,192,721), and the percent completeness of chief complaint for visits in 2017 was 87.15% (N=80,603,991). Of the 2,646 facilities that sent visits data in 2016 and 2017, 114 (4.31%) facilities showed an increase of at least 10% in chief complaint completeness in 2017 compared with 2016. As for discharge diagnosis, national results showed the percent completeness of discharge diagnosis for 2016 visits was 50.83% (N=36,048,334), and the percent completeness of discharge diagnosis for 2017 was 59.23% (N=54,776,310). Of the 2,646 facilities that sent data for visits in 2016 and 2017, 306 (11.56%) facilities showed more than a 10% increase in percent completeness of discharge diagnosis in 2017 compared with 2016.ReferencesBatini, C., Cappiello. C., Francalanci, C. and Maurino, A. (2009) Methodologies for data quality assessment and improvement. ACM Comput. Surv., 41(3). 1-52.

scholarly journals Comparison of National and Local Syndromic Surveillance Data - Cook County, IL, 2017

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Zachary Heth ◽  
Kelly Bemis ◽  
Demian Christiansen
Keyword(s):  
Syndromic Surveillance ◽  
Cook County ◽  
Production Data ◽  
Surveillance Program ◽  
Surveillance Systems ◽  
Discharge Diagnosis ◽  
Data Completeness ◽  
Data Elements ◽  
Zip Code ◽  
Total Visit

ObjectiveThis analysis was undertaken to determine how the data completeness, consistency, and other attributes of our local syndromic surveillance program compared to the National Syndromic Surveillance Platform.IntroductionIn 2005, the Cook County Department of Public Health (CCDPH) began using the Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE) as an emergency department (ED)-based local syndromic surveillance program (LSSP); 23 (100%) of 23 hospitals in suburban Cook County report to the LSSP. Data are transmitted in delimited ASCII text files (i.e., flat files) and contain a unique patient identifier, visit date and time, zip code, age, sex, and chief complaint. Discharge diagnosis and disposition are optional data elements. Prior to 2017, the Illinois Department of Public Health placed facilities participating in the Cook LSSP in a holding queue to transform their flat file submissions into a HL7 compliant message; however as of 2017, eligible hospitals must submit HL7 formatted production data to IDPH to fulfill Meaningful Use. The primary syndromic surveillance system for Illinois is the National Syndromic Surveillance Program (NSSP), which transitioned to an ESSENCE interface in 2016. As of December 2016, 20 (87%) of 23 hospitals reporting to the LSSP also reported to IDPH and the NSSP. As both syndromic surveillance systems aim to collect the same data, and now can be analyzed with the same interface, CCDPH sought to compare the LSSP and NSSP for data completeness, consistency, and other attributes.MethodsOur comparison of NSSP to the LSSP focused on data completeness for key demographic and medical variables and consistency in total visit counts. Analysis of completeness utilized data from December 2016 for 20 hospitals contributing HL7 production data to IDPH at that time. Total visit counts in both systems were compared for the same 20 hospitals from February 5th-11th 2017, a randomly chosen time period. A target threshold of less than 3% difference in total visit counts was set by the CCDPH system users. Analysis was completed in Microsoft Excel 2010. Other attributes of the surveillance systems were qualitatively assessed by the primary system users at CCDPH.ResultsAll variables required by the LSSP had 98-100% completeness in both the LSSP and NSSP (unique patient identifier, age, sex, zip code, visit time and date, and chief complaint). However, the LSSP optional data elements, discharge diagnosis and discharge disposition, were less complete in the LSSP, compared to the NSSP (Diagnosis: 56% versus 83%, Disposition: 66% versus 80%). Among variables required for NSSP reporting but not reported to the LSSP, completeness ranged from 100% (race, ethnicity) to 82% (county). Optional data elements within NSSP ranged in completeness from 73% (initial pulse oximetry) to 0% (initial blood pressure, insurance coverage). Of the 20 hospitals evaluated for visit counts, only one hospital had <3% difference in visit counts in the LSSP and NSSP for all 7 days assessed. Ten hospitals had >3% difference in visit counts on all seven days. Average seven day differences for hospitals ranged from 0% to 54%. Eighteen (90%) of 20 hospitals were reporting larger numbers of visits to NSSP than to the LSSP.ConclusionsOverall completeness of data was similar between the national and our local ESSENCE systems with most required variables having over 98% completeness. NSSP had higher completeness over the LSSP for discharge diagnosis and disposition. Additional data elements required by NSSP, but unavailable in the LSSP, had similarly high completeness but optional NSSP variables of interest showed greater variability in reporting. Differences in visit counts were higher than expected. An ongoing exploration of these differences has shown they are multifaceted and require hospital-specific interventions. There are strengths and limitations to both the NSSP and LSSP. CCDPH has direct control over data sharing between jurisdictions in the LSSP and there has historically been less system “down time” in the LSSP compared to the NSSP; however, the use of flat files instead of HL7, as well as having fewer incentives for hospital participation (e.g. Meaningful Use) after 2016, results in limited data collection and stagnant growth compared to the NSSP. Jurisdictions using their own LSSPs should consider analyzing their data completeness, consistency, and quality compared to the NSSP.  

scholarly journals Comparing and Contrasting Two ESSENCE Syndrome Definition Query Methods

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Zachary M. Stein ◽  
Sophia Crossen
Keyword(s):  
Best Practices ◽  
Syndromic Surveillance ◽  
Chief Complaint ◽  
Surveillance Program ◽  
Discharge Diagnosis ◽  
True Positive ◽  
Patient Visit ◽  
Production Facility ◽  
Time Period ◽  
Ed Visits

ObjectiveTo compare and contrast two ESSENCE syndrome definition query methods and establish best practices for syndrome definition creation.IntroductionThe Kansas Syndromic Surveillance Program (KSSP) utilizes the ESSENCE v.1.20 program provided by the National Syndromic Surveillance Program to view and analyze Kansas Emergency Department (ED) data.Methods that allow an ESSENCE user to query both the Discharge Diagnosis (DD) and Chief Complaint (CC) fields simultaneously allow for more specific and accurate syndromic surveillance definitions. As ESSENCE use increases, two common methodologies have been developed for querying the data in this way.The first is a query of the field named “CC and DD.” The CC and DD field contains a concatenation of the parsed patient chief complaint and the discharge diagnosis. The discharge diagnosis consists of the last non-null value for that patient visit ID and the chief complaint parsed is the first non-null chief complaint value for that patient visit ID that is parsed by the ESSENCE platform. For this comparison, this method shall be called the CCDD method.The second method involves a query of the fields named, “Chief Complaint History” and “Discharge Diagnosis History.” While the first requires only one field be queried, this method queries the CC History and DD History fields, combines the resulting data and de-duplicates this final data set by the C_BioSense_ID. Chief Complaint History is a list of all chief complaint values related to a singular ED visit, and Discharge Diagnosis History is the same concept, except involving all Discharge Diagnosis values. For this comparison, this method shall be called the CCDDHX method.While both methods are based on the same query concept, each method can yield different results.MethodsA program was created in R Studio to analyze a user-provided query.Simple queries were randomly generated. Twenty randomly generated queries were run through the R Studio program and disparities between data sets were recorded. All KSSP production facility ED visits during the month of August 2017 were analyzed.Secondly, three queries actively utilized in KSSP practice were run through the program. These queries were Firework-Related Injuries, Frostbite and Cold Exposure, and Rabies Exposure. The queries were run on all KSSP production facility ED visits, and coincided with the timeline of relevant exposures.ResultsIn the random query trials, an average of 5.4% of the cases captured using the CCDD field method were unique and not captured by the same query in the CCDDHX method. Using the CCDDHX method, an average of 6.1% of the cases captured were unique and not captured by the CCDD method.When using the program to compare syndromes from actively utilized KSSP practice, the disparity between the two methods was much lower.Firework-Related InjuriesDuring the time period queried, the CCDD method returned 171 cases and the CCDDHX method returned 169 cases. All CCDDHX method cases were captured by the CCDD method. The CCDD method returned 2 cases not captured by the CCDDHX method. These two cases were confirmed as true positive firework-related injury cases.Frostbite and Cold ExposureDuring the time period queried, CCDD method returned 328 cases and the CCDDHX method returned 344 cases. The CCDDHX method captured 16 cases that the CCDD method did not. The CCDD method did not capture any additional cases when compared to the CCDDHX method. After review, 10 (62.5%) of these 16 cases not captured by the CCDD method were true positive cases.Rabies ExposureDuring the time period queried, the CCDD method returned 474 cases and the CCDDHX method returned 473 cases. The CCDDHX method captured 7 cases that the CCDD method did not. The CCDD method returned 8 cases not captured by the CCDDHX method. After review, the 7 unique cases captured in the CCDDHX method contained 3 (42.9%) true positive cases and 3 (37.5%) of the 8 cases not captured by the CCDDHX method were true positives.ConclusionsThe twenty random queries showed a disparity between methods. When utilizing the same program to analyze three actively utilized KSSP definitions, both methods yielded similar results with a much smaller disparity. The CCDDHX method inherently requires more steps and requires more queries to be run through ESSENCE, making the method less timely and more difficult to share. Despite these downsides, CCDDHX will capture cases that appear throughout the history of field updates.Further variance between methods is likely due to the CCDD field utilizing the ESSENCE-processed CC while the CCDDHX field utilizes the CC verbatim as produced by the ED facility. This allows the CCDD method to tap into the powerful spelling correction and abbreviation-parsing steps that ESSENCE employs, but incorrect machine corrections and replacements, while rare, can negatively affect syndrome definition performance.The greater disparity in methods for the random queries may be due to the short (3 letter) text portion of the queries. Short segments are more likely to be found in multiple words than text of actual queries. Utilizing larger randomly generated text segments may resolve this and is a planned next step for this research.Our next step is to share the R Studio program to allow further replication. The Kansas Syndromic Surveillance Program is also continuing similar research to ensure that best practices are being met. 

scholarly journals Syndromic Surveillance – Reports of Successes from the Field

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Antheny Wilson ◽  
Teresa Hamby ◽  
Wei Hou ◽  
David J Swenson ◽  
Krystal Collier ◽  
...  
Keyword(s):  
Public Health ◽  
Data Quality ◽  
Syndromic Surveillance ◽  
Surveillance Program ◽  
Local Health ◽  
Health Departments ◽  
Local Health Departments ◽  
Success Stories ◽  
Department Of Health ◽  
State And Local

Objective: This panel will:● Discuss the importance of identifying and developing success stories● Highlight successes from state and local health departments to show how syndromic surveillance activities enhance situational awareness and address public health concerns● Encourage discussion on how to further efforts for developing and disseminating success storiesIntroduction: Syndromic surveillance uses near-real-time emergency department and other health care data for enhancing public health situational awareness and informing public health activities. In recent years, continued progress has been made in developing and strengthening syndromic surveillance activities. At the national level, syndromic surveillance activities are facilitated by the National Syndromic Surveillance Program (NSSP), a collaboration among state and local health departments, the CDC, other federal organizations, and other organizations that enabled collection of syndromic surveillance data in a timely manner, application of advanced data monitoring and analysis techniques, and sharing of best practices. This panel will highlight the importance of success stories. Examples of successes from state and local health departments will be presented and the audience will be encouraged to provide feedback.Description: ●Success stories – acknowledging and informing syndromic surveillance practiceThis presentation will discuss the importance of success stories for NSSP focused on increasing syndromic surveillance representativeness, improving data quality, and strengthening syndromic surveillance practices among grant recipients and partners. From the beginning of the program, the identification of success stories has been an important part of the efforts to develop knowledge base that better guide syndromic surveillance program activities.●NJ and BioSense – Making The Connection The New Jersey Department of Health (NJDOH) uses Health Monitoring’s EpiCenter as its primary ED data for syndromic surveillance. This data is also submitted to CDC’s NSSP BioSense Platform. In April 2017, a spike in ED Visits of Interest was identified by a CDC NSSP subject matter expert and brought to the attention of NJDOH’s data analyst. Data showed an increase in “Exposure” and “School Exposure” chief complaints in two contiguous counties. News reports showed the visits resulted from a dormitory fire at a university in the area. The NSSP and NJDOH staff collaboration integrated data from both NJDOH’s EpiCenter and CDC’s BioSense Platform for further investigation. This activity shows BioSense Platform’s potential as an additional syndromic surveillance tool because of its different classifications and keyword groupings.●Evaluation and Performance Measures at the Utah Department of HealthSyndromic surveillance related evaluation activities at the Utah Department of Health requires collaboration between subject matter experts and system users from the UT-NSSP workgroup. The progress is examined quarterly and outcomes compared with the short-, mid-, and long-term outcomes listed in the NSSP logic model to ensure activities are in sync with the program’s overall goals. Throughout the budget year, a variety of tools were used to keep track of the progress. During this session, challenges and successes, lessons learned, and effective strategies will be discussed.●NSSP R tool Data Download Useful in NHThe New Hampshire Department of Health and Human Services (NH DHHS) uses the state-wide Automated Hospital Emergency Department Data (AHEDD) system as its primary syndromic surveillance system. A copy of this data is submitted to CDC’s NSSP BioSense Platform. In July of 2017, NH worked with the NSSP vendor, CDC staff, a jurisdictional expert, NH Division of Information Technology staff, and an external vendor to create an “R” software download in CSV format and home-based NSSP Cognos report. This allowed NH DHHS staff to compare these data to the home-based data and ultimately, it proved to be an important step in the NSSP data quality assessment process.●Achieving success to improve data quality through collaborative Community of Practice partnerships The Data Quality Committee is a forum to identify, discuss, and attempt to address syndromic surveillance data quality issues. Maintaining data quality for the chief complaint field is a priority as it can impact the creation and refinement in the successful application of a syndrome definition for one of the fundamental data elements. An issue was observed in the Arizona data in the BioSense Platform, where chief complaint was being truncated at 200 characters. Through efforts to build relationships from the committee in the Community of Practice, Arizona was able to discover the root causes for the issue, assess if it affected other jurisdictions, and work with the partners to find a feasible resolution. This talk will discuss how this collaborative approach helped improve data quality.How the Moderator Intends to Engage the Audience in Discussions on the Topic: The moderator will introduce the session and the panelists, and will invite questions and comments from the audience.

scholarly journals Early effect of validation efforts of Massachusetts syndromic surveillance data

2017 ◽  
Vol 9 (1) ◽  
Author(s):  
Mark Bova ◽  
Roas Ergas
Keyword(s):  
Emergency Department ◽  
Data Quality ◽  
Syndromic Surveillance ◽  
Chief Complaint ◽  
Surveillance Program ◽  
Production Environment ◽  
Diagnosis Code ◽  
Diagnosis Codes ◽  
Chief Complaints ◽  
Validation Strategy

ObjectiveTo develop a detailed data validation strategy for facilitiessending emergency department data to the Massachusetts SyndromicSurveillance program and to evaluate the validation strategy bycomparing data quality metrics before and after implementation ofthe strategy.IntroductionAs a participant in the National Syndromic Surveillance Program(NSSP), the Massachusetts Department of Public Health (MDPH)has worked closely with our statewide Health Information Exchange(HIE) and National Syndromic Surveillance Program (NSSP)technical staff to collect and transmit emergency department (ED)data from eligible hospitals (EHs) to the NSSP. Our goal is to ensurecomplete and accurate data using a multi-step process beginning withpre-production data and continuing after EHs are sending live datato production.MethodsWe used an iterative process to establish a framework formonitoring data quality during onboarding of EHs into our syndromicsurveillance system and kept notes of the process.To evaluate the framework, we compared data received duringthe month of January 2016 to the most recent full month of data(June 2016) to describe the following primary data quality metricsand their change over time: total and daily average of message andvisit volume; percent of visits with a chief complaint or diagnosiscode received in the NSSP dataset; and percentage of visits with achief complaint/diagnosis code received within a specified time ofadmission to the ED.ResultsThe strategies for validation we found effective includedexamination of pre-production test HL7 messages and the executionof R scripts for validation of live data in the staging and productionenvironments. Both the staging and production validations areperformed at the individual message level as well as the aggregatedvisit level, and included measures of completeness for requiredfields (Chief Complaint, Diagnosis Codes, Discharge Dispositions),timeliness, examples of text fields (Chief Complaint and TriageNotes), and demographic information. We required EHs to passvalidation in the staging environment before granting access to senddata to the production environment.From January to June 2016, the number of EHs sending data tothe production environment increased from 44 to 48, and the numberof messages and visits captured in the production environmentincreased substantially (see Table 1). The percentage of visits witha chief complaint remained consistently high (>99%); howeverthe percentage of visits with a chief complaint within three hoursof admission decreased during the study period. Both the overallpercentage of visits with a diagnosis code and the percentage of visitswith a diagnosis code within 24 hours of admission increased.ConclusionsFrom January to June 2016, Massachusetts syndromic surveillancedata improved in the percentage of visits with diagnosis codes and thetime from admission to first diagnosis code. This was achieved whilethe volume of data coming into the system increased. The timelinessof chief complaints decreased slightly during the study period, whichmay be due to the inclusion of several new facilities that are unable tosend real-time data. Even with the improvements in the timeliness ofthe diagnosis code field, and the subsequent decrease in the timelinessof the chief complaint field, chief complaints remained a more timelyoption for syndromic surveillance. Pre-production and ongoing dataquality assurance activities are crucial to ensure meaningful dataare acquired for secondary analyses. We found that reviewing testHL7 messages and staging data, daily monitoring of productiondata for key factors such as message volume and percent of visitswith a diagnosis code, and monthly full validation in the productionenvironment were and will continue to be essential to ensure ongoingdata integrity.Table 1: ED Data in the Production Environment

scholarly journals Forming Collaborations through the Data Quality Committee to Address Urgent Incidents

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Krystal S. Collier ◽  
Sophia Crossen ◽  
Courtney Fitzgerald ◽  
Kaitlyn Ciampaglio ◽  
Lakshmi Radhakrishnan ◽  
...  
Keyword(s):  
Data Quality ◽  
Community Of Practice ◽  
Disease Surveillance ◽  
Syndromic Surveillance ◽  
Surveillance Program ◽  
Stakeholder Groups ◽  
The Core ◽  
Processing Data ◽  
Time Required ◽  
Core Issues

ObjectiveThe National Syndromic Surveillance Program (NSSP) Community of Practice (CoP) works to support syndromic surveillance by providing guidance and assistance to help resolve data issues and foster relationships between jurisdictions, stakeholders, and vendors. During this presentation, we will highlight the value of collaboration through the International Society for Disease Surveillance (ISDS) Data Quality Committee (DQC) between jurisdictional sites conducting syndromic surveillance, the Centers for Disease Control and Prevention’s (CDC) NSSP, and electronic health record (EHR) vendors when vendor-specific errors are identified, using a recent incident to illustrate and discuss how this collaboration can work to address suspected data anomalies.IntroductionOn November 20, 2017, several sites participating in the NSSP reported anomalies in their syndromic data. Upon review, it was found that between November 17-18, an EHR vendor’s syndromic product experienced an outage and errors in processing data. The ISDS DQC, NSSP, a large EHR vendor, and many of the affected sites worked together to identify the core issues, evaluate ramifications, and formulate solutions to provide to the entire NSSP CoP.DescriptionOn November 20, 2017, several sites participating in the NSSP reported anomalies in their syndromic data. Upon review, it was found that between November 17-18, an EHR vendor’s syndromic product experienced an outage and errors in processing data. The ISDS DQC, NSSP, a large EHR vendor, and many of the affected sites worked together to identify the core issues, evaluate ramifications, and formulate solutions to provide to the entire NSSP CoP.How the Moderator Intends to Engage the Audience in Discussions on the TopicFollowing presentation of this information, the presenters will lead a discussion on how to improve the response, provide resolution, communicate expectations, and decrease the time required to resolve issues should a similar event happen in the future. Participants from all three stakeholder groups, sites conducting syndromic surveillance, the NSSP, and vendor representatives, will be invited to share their experiences, successes, and concerns.

scholarly journals Syndromic Surveillance for Situation Awareness: Understanding Syndrome Performance

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Kristin Arkin
Keyword(s):  
Emergency Department ◽  
Situation Awareness ◽  
Syndromic Surveillance ◽  
Chief Complaint ◽  
Surveillance Program ◽  
Initial Visit ◽  
Word Count ◽  
Data Submission ◽  
Daily Frequency ◽  
Ed Visits

ObjectiveIn August 2017, a large influx of visitors was expected to view the total solar eclipse in Idaho. The Idaho Syndromic Surveillance program planned to enhance situation awareness during the event. In preparation, we sought to examine syndrome performance of several newly developed chief complaint and combination chief complaint and diagnosis code syndrome definitions to aid in interpretation of syndromic surveillance data during the event.IntroductionThe August 21, 2017 total solar eclipse in Idaho was anticipated to lead to a large influx of visitors in many communities, prompting a widespread effort to assure Idaho was prepared. To support these efforts, the Idaho Syndromic Surveillance program (ISSp) developed a plan to enhance situation awareness during the event by conducting syndromic surveillance using emergency department (ED) visit data contributed to the National Syndromic Surveillance Program’s BioSense platform by Idaho hospitals. ISSp sought input on anticipated threats from state and local emergency management and public health partners, and selected 8 syndromes for surveillance.Ideally, the first electronic message containing information on an emergency department visit is sent to ISSp within 24 hours of the visit and includes the chief complaint for the visit. Data on other variables, such as diagnosis codes, are updated by subsequent messages for several days after the visit. Chief complaint (CC) text and discharge diagnosis (DD) codes are the primary variables used for syndrome match; delay in reporting these variables adversely affects timely syndrome match of visits. Because our plan included development of new syndrome definitions and querying data within 24 hours of visits, earlier than ISSp had done previously for trend analysis, we sought to better understand syndrome performance.MethodsWe defined messages with completed CC and DD as the last message regarding a visit where term count increased from previous messages regarding that visit, indicating new information was added to the field. We retrospectively assessed the total number of ED visits and calculated the daily frequency of completed CC and DD by days since visit date for visits during June 1–July 31, 2017. Additionally, we calculated facility mean word count in CC fields by averaging the word count of parsed, complete CC fields for visits occurring June 1–July 31, 2017 for each facility.During July 10–24, 2017, we calculated the daily frequency of visits occurring in the previous 90 days for total ED visits and syndrome-matched visits for 8 selected syndromes (heat-related illness; cold exposure; influenza-like-illness; nausea, vomiting, and diarrhea; animal/bug bites and stings; drowning/submersion; alcohol/drug intoxication; and medication replacement). Syndrome-matched visits were defined as visits with CC or DD that match the syndrome definition. We calculated the percent of syndrome-matched visits by syndromes defined with CC or CC and DD combined (CCDD) over time. Syndromes with fewer than 5 matched visits were excluded from analysis.ResultsComplete CCs were received for 99.1% of visits and complete DDs were received for 89.8% of visits. Complete CCs were submitted for 58.2% of visits within 1 day of the visit, 88.9% of visits within 3 days, and 98.9% of visits within 7 days. In contrast, complete DDs were submitted for 24.3% of visits within 1 day, 38.7% of visits within 3 days, and 53.7% of visits within 7 days (Table 1).During the observation period, data submission from facilities representing approximately 33% of visits was interrupted for 5 (36%) of 14 days. Heat-related illness, cold exposure, and drowning/submersion, were excluded from syndrome-match analysis. During the 9 days of uninterrupted data submission, 100% syndrome-matched visits for syndromes defined by CC alone and 69.1% syndrome-matched visits for syndromes defined by CCDD were identified within 6–7 days of initial visit. Facilities with interrupted data submission contributed 75% of CC syndrome-matched visits and 33% of CCDD syndrome-matched visits. The facility mean word count in CC fields from these facilities was >15 compared with 2–4 from other facilities.ConclusionsExamination of syndrome performance prior to a known event quantitated differences in timeliness of CC and DD completeness and syndrome match. CCs and DDs in visit messages were not complete within 24 hours of initial visit. CC completion was nearly 34 percentage points greater than DD completeness 1 day after initial visit and did not converge until ≥15 days after initial visit. Higher percentages of syndrome match within 6–7 days of initial visit were seen by CC alone than CCDD defined syndromes. Facilities using longer CCs contributed disproportionately to syndrome matching using CC, but not CCDD syndrome definitions. Syndromic surveillance system characteristics, including timeliness of CCs and DDs, length of CCs, and characteristics of facilities from which data transmission is interrupted should be considered when building syndrome definitions that will be used for surveillance within 7 days of emergency department visits and when interpreting syndromic surveillance findings.

Tracing data elements through a standard data flow

2011 ◽  
Vol 4 (1-2) ◽  
pp. 59-69 ◽  
Author(s):  
Alistair Dootson
Keyword(s):  
Data Flow ◽  
Standard Data ◽  
Data Elements

scholarly journals The Validity of Chief Complaint and Discharge Diagnosis in Emergency Department-based Syndromic Surveillance

2004 ◽  
Vol 11 (12) ◽  
pp. 1262-1267 ◽  
Author(s):  
Aaron T. Fleischauer ◽  
Benjamin J. Silk ◽  
Mare Schumacher ◽  
Ken Komatsu ◽  
Sarah Santana ◽  
...  
Keyword(s):  
Emergency Department ◽  
Syndromic Surveillance ◽  
Chief Complaint ◽  
Discharge Diagnosis

scholarly journals Developing and Validating a Fireworks-Related Syndrome Definition in Kansas

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Zachary M Stein
Keyword(s):  
Syndromic Surveillance ◽  
Public Information ◽  
False Positives ◽  
Chief Complaint ◽  
Surveillance Program ◽  
Anatomical Location ◽  
Free Text ◽  
Data Set ◽  
Ed Visits ◽  
Key Terms

ObjectiveTo develop a syndrome definition and analyze syndromic surveillance data usefulness in surveillance of firework-related emergency department visits in Kansas. Introduction Across the U.S.A., multiple people seek treatment for fireworks-related injuries around the July 4th holiday. Syndromic surveillance in Kansas allows for near real-time analysis of the injuries occurring during the firework selling season. During the 2017 July 4thholiday, the Kansas Syndromic Surveillance Program (KSSP) production data feed received data from 88 EDs at excellent quality and timeliness. Previous and current firework safety messaging in Kansas is dependent on voluntary reporting from hospitals across the state. With widespread coverage of EDs by KSSP, data can be more complete and timely to better drive analysis and public information Methods:KSSP data was queried through the Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE) v.1.20 provided by the National Syndromic Surveillance Program. Data between June 12, 2017 and August 13, 2017 were queried. The first query (Query A, Table 1.) searched the Discharge Diagnosis History field for the “W39” ICD-10 Diagnosis code, “Discharge of firework.” These records were searched for common firework terms contained in the Chief Complaint History field. These firework-related free text terms (Query B, Table 1.) were then combined with other potential firework-related terms to create a preliminary free text query (Query C, Table 1.). This preliminary query was run on the Chief Complaint History field. Data were then searched for false positive cases and appropriate negation terms were included to accommodate this. The new query with negation terms (Query D, Table 1.) was run on the Chief Complaint History field, combined with the results from the Discharge Diagnosis History field, and then combined records were de-duplicated based on a unique visit identifier. The final data set was then classified by the anatomical location of the injury and the gender and age group of the patient. Results:The initial query (Query A, Table 1.) for the diagnosis code “W39” returned 101 unique ED visits. Of these 101 unique ED visits, the following terms were identified in the Chief Complaint History field: shell, artillery, bomb, sparkler, grenade, fire cracker, firework, and firework show. These key terms were translated into Query B, Table 1. Other key terms deemed likely to capture specific firework-related exposures were then included into Query C, Table 1. , including roman, candle, lighter, M80, and punk. Query C was then used to query the Chief Complaint History field, returning 144 unique ED visits. Cases captured by Query C were then reviewed by hand for false positives and the negation terms, lighter fluid, fish, nut, and pistachio, were incorporated the Query D, Table 1. The previous process for Query C was then repeated on Query D, leaving a remaining 136 unique cases. Query A’s 101 unique ED visits was then combined with the 136 unique ED visits captured by Query D and de-duplicated. The de-duplicated data set contained 170 unique ED visits which were then reviewed by hand for false positives. The final removal of false positives from the combined and de-duplicated data set left a remaining 154 unique ED visits for firework-related injuries during this time period.For these data, the most common victims of firework injuries were males, accounting for 65.5% of all firework related ED visits and children ages 0 to 19 accounting for 44.2% of these visits. At every age breakout, male injuries exceeded female injuries. The most common anatomical location of the injury was one or both hands with 38.3% of all injuries mentioned hands as their primary injury. Injuries to the eyes, face, and head accounted for the second most injuries (28.6% of all patients). Conclusions: The selling of fireworks will be a yearly occurrence of a specific exposure that can potentially lead to injuries. Utilizing syndromic surveillance to review the holiday firework injuries is a very rapid method to assess the impact of these injuries and may allow for future direction of public information during the holiday. Having a syndrome definition that builds on knowledge from previous years will allow for quicker case identification as well.State public information regarding firework safety can be significantly bolstered by accurate and rapid data assessment. Developing a firework injury syndrome definition that is accurate and returns information rapidly has allowed for increased buy-in to the Kansas Syndromic Surveillance Program from public information offices, fire marshal’s offices, and other program fields.

scholarly journals Evaluation of ESSENCE Syndromic Definitions for ED Visits Related to Falls in Icy Weather

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Jessica Hensley ◽  
Sandra Gonzalez ◽  
Derry Stover ◽  
Thomas Safranek ◽  
Ming Qu
Keyword(s):  
Public Health ◽  
Positive Predictive Value ◽  
Predictive Value ◽  
Syndromic Surveillance ◽  
Hospital Admissions ◽  
Chief Complaint ◽  
Surveillance Program ◽  
Search Terms ◽  
Ed Visits ◽  
Sensitivity Specificity

ObjectiveThis project evaluated and compared two ESSENCE syndromic surveillance definitions for emergency department (ED) visits related to injuries associated with falls in icy weather using 2016-2017 data from two hospitals in Douglas County, Nebraska. The project determined the validity of the syndromic surveillance definition as applied to chief complaint and triage notes and compared the chief complaint data alone to chief complaint plus triage notes definitions to find the most reliable definition for ED visits resulting from fall-related injuries.IntroductionIcy weather events increase the risk for injury from falls on untreated or inadequately treated surfaces. These events often result in ED visits, which represents a significant public health and economic impact1.The goal of this project was to start the process toward an evaluation of the public health impact and the economic impact of falls associated to icy weather in Douglas County, NE for the ultimate purpose of designing and implementing injury prevention related public health protection measures. Additionally, the validated definition will be used by NE DHHS Occupational Health Surveillance Program to identify work related ice-related fall injuries that were covered by workers compensation. To achieve the goal, the first step was to identify a valid and reliable syndromic surveillance. Specifically, this project looked at the applicability of the ESSENCE syndromic surveillance definitions related to injuries associated with falls. Two syndromic surveillance definitions were compared, one that includes triage note and chief complaint search terms, and another that only includes chief complaint. The hypothesis was that the ESSENCE syndromic surveillance definition that includes triage note and chief complaint search terms, rather than the syndromic surveillance definition that only includes chief complaint, would be more effective at identifying ED visits resulting from fall-related injuries.MethodsThis project included 751 EDs visits from two hospitals located in Douglas County Nebraska, during ice events on December 16-18, 2016, January 10-12, 2017, and January 15-18, 2017.Two ESSENCE syndromic surveillance definitions, “Chief Complaint or Triage Note” and “Chief Complaint Only,” were used to identify fall-related ED visits from two participating EDs in Douglas County, NE. In the chief complaint and the triage note fields, the keywords selected were: fall, fell, or slip. In that the ESSENCE time series analysis indicated the increase in the number of falls were associated with ice events from baseline, an assumption was made that the increase was a result of the weather. Then, the Syndromic Surveillance Event Detection of Nebraska database was used to find the patient and visit identification numbers. These two identification numbers were used to identify the EHRs needed for a gold standard review. Chart data was used to evaluate the reliability and validity of the two syndromic surveillance definitions for the detection of falls on the study dates. This analysis was used to find the sensitivity, specificity and predictive value.ResultsThe sensitivity, specificity and positive predictive value for the “Chief Complaint Only” definition yielded 71.7%, 100%, and 100% respectively. The “Chief Complaint or Triage Note” definition results were 90.9%, 98.8%, and 95.5% for these analyses. Negative predictive value for both definitions was 97.5%.ConclusionsThe sensitivity indicates both definitions are unlikely to give false positives, and the positive predictive value indicates both definitions successfully identify most of the true positives found in the visits. However, the “Chief Complaint Only” definition resulted in a minimally higher specificity and positive predictive value. Therefore, the results indicate that although both definitions have similar specificity and positive predictive value, the “Chief Complaint or Triage Note” definition is more likely than the “Chief Complaint Only” definition to correctly identify ED visits related to falls in icy weather.References1. Beynon C, Wyke S, Jarman I, Robinson M, Mason J, Murphy K, Bellis MA, Perkins C. The cost of emergency hospital admissions for falls on snow and ice in England during winter 2009/10: a cross sectional analysis. Environmental Health 2011;10(60).

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