scholarly journals Using Real-Time Syndromic Surveillance to Analyze the Impact of a Cold Weather Event in New Mexico

2018 ◽  
Vol 2018 ◽  
pp. 1-6
Author(s):  
Victoria F. Dirmyer
Keyword(s):  
Real Time ◽  
Syndromic Surveillance ◽  
Chief Complaint ◽  
Cold Weather ◽  
Weather Event ◽  
Diagnosis Codes ◽  
Chief Complaints ◽  
Icd 10 ◽  
Ed Visits ◽  
The Impact

Objective. This report describes the development of a novel syndromic cold weather syndrome for use in monitoring the impact of cold weather events on emergency department attendance. Methods. Syndromic messages from seven hospitals were analyzed for ED visits that occurred over a 12-day period. A cold weather syndrome was defined using terms in the self-reported chief complaint field as well as specific ICD-10-CM codes related to cold weather. A κ statistic was calculated to assess the overall agreement between the chief complaint field and diagnosis fields to further refine the cold weather syndrome definition. Results. Of the 3,873 ED visits that were reported, 487 were related to the cold weather event. Sixty-three percent were identified by a combination of diagnosis codes and chief complaints. Overall agreement between chief complaint and diagnosis codes was moderate (κ=0.50; 95% confidence interval = 0.48–0.52). Conclusion. Due to the near real-time reporting of syndromic surveillance data, analysis results can be acted upon. Results from this analysis will be used in the state’s emergency operations plan (EOP) for cold weather and winter storms. The EOP will provide guidance for mobilization of supplies/personnel, preparation of roadways and pedestrian walkways, and the coordination efforts of multiple state agencies.

scholarly journals Using a Syndromic Surveillance System to Evaluate the Impact of a Change in Alcohol Law

2015 ◽  
Vol 7 (1) ◽  
Author(s):  
Julia A. Dilley ◽  
Atar Baer ◽  
Jeff Duchin ◽  
Julie E. Maher
Keyword(s):  
Surveillance System ◽  
Syndromic Surveillance ◽  
Washington State ◽  
Chief Complaint ◽  
King County ◽  
Syndromic Surveillance System ◽  
Liquor Stores ◽  
Diagnosis Codes ◽  
Ed Visits ◽  
The Impact

In 2011, Washington State voters passed an initiative which closed state liquor stores and opened private sector liquor sales. We examined trends in alcohol-related emergency department (ED) visits associated with this law change. Data were from the King County syndromic surveillance system. Alcohol-related ED visits were identified using chief complaint search strings and diagnosis codes. We used a linear regression model with a spline at the date of law change and controlled for other factors. Significant increases in alcohol-related ED visits were observed associated with the law change among minors (age <21) and adults ages 40 and older.

scholarly journals Free-Text Mining to Improve Syndrome Definition Matching Across Emergency Departments

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Kristin Arkin
Keyword(s):  
Text Mining ◽  
Influenza Season ◽  
Chief Complaint ◽  
Free Text ◽  
Diagnosis Code ◽  
Diagnosis Codes ◽  
Chief Complaints ◽  
Group A ◽  
Ed Visits ◽  
Group B

ObjectiveWe sought to use free text mining tools to improve emergency department (ED) chief complaint and discharge diagnosis data syndrome definition matching across facilities with differing robustness of data in the Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE) application in Idaho’s syndromic surveillance system.IntroductionStandard syndrome definitions for ED visits in ESSENCE rely on chief complaints. Visits with more words in the chief complaint field are more likely to match syndrome definitions. While using ESSENCE, we observed geographic differences in chief complaint length, apparently related to differences in electronic health record (EHR) systems, which resulted in disparate syndrome matching across Idaho regions. We hypothesized that chief complaint and diagnosis code co-occurrence among ED visits to facilities with long chief complaints could help identify terms that would improve syndrome match among facilities with short chief complaints.MethodsThe ESSENCE-defined influenza-like illness (ILI) chief complaint syndrome was used as the base syndrome for this analysis. Syndrome-matched visits were defined as visits that match the syndrome definition.We assessed chief complaints and diagnosis code co-occurrence of syndrome-matched visits using the RCRAN TidyText package and developed a bigram network from normalized, concatenated chief complaint and diagnosis code (CCDD) fields and normalized diagnosis code (DD) fields per previously described methodologies.1 Common connections were defined by a natural break in frequency of pair occurrence for CCDD pairs (30 occurrences) and DD pairs (5 occurrences).The ESSENCE syndrome was revised by adding relevant bigram network clusters and logic operators. We compared time series of the percent of ED visits matched to the ESSENCE syndrome with those matched to the revised syndrome. We stratified the time series by facilities grouped by short (average < 4 words, “Group A”) and long (average ≥ 4 words, “Group B”) chief complaint fields (Figure 1). Influenza season start was defined as two consecutive weeks above baseline, or the 95% upper confidence limit of percent syndrome-matched visits outside of the CDC ILI surveillance season. Season trends and influenza-related deaths in Idaho residents were compared.ResultsDuring August 1, 2016 through July 31, 2017, 1,587 (1.17%) of 135,789 ED visits matched the ESSENCE syndrome. Bigram networks of CCDD fields produced clusters already included by the ESSENCE syndrome. The bigram network of DD fields (Figure 2) produced six clusters. The revised syndrome definition included the ESSENCE syndrome, 3 single DD terms, and 3 two DD terms combined. The start of influenza season was identified as the same week for both ILI syndrome definitions (ESSENCE baseline 0.70%; revised baseline 2.21%). The ESSENCE syndrome indicated the season peaked during Morbidity and Mortality Weekly Report (MMWR) week 2017-05 with the season ending MMWR week 2017-14. The revised syndrome indicated 2017-20 as the season end. Multiple peaks seen with the revised syndrome during MMWR weeks 2017-02, 2017-05, and 2017-10 mirrored peaks in influenza-related deaths during MMWR weeks 2017-03, 2017-06, and 2017-11.ILI season onset was five weeks earlier with the revised syndrome compared with the ESSENCE syndrome in Group A facilities, but remained the same in Group B. The annual percentage of ED visits related to ILI was more uniform between facility groups under the revised syndrome than the ESSENCE syndrome. Unlike the trend seen with the ESSENCE syndrome, the revised syndrome shows low-level ILI activity in both groups year-round.ConclusionsIn Idaho, dramatic differences in ED visit chief complaint word counts were seen between facilities; bigram networks were found to be an important tool to identify diagnosis codes and logical operators that built more inclusive syndrome definitions when added to an existing chief complaint syndrome. Bigram networks may aid understanding the relationship between chief complaints and diagnosis codes in syndrome-matched visits.Use of trade names and commercial sources is for identification only and does not imply endorsement by the Centers for Disease Control and Prevention, the Public Health Service, or the U.S. Department of Health and Human Services.References1. Silge, J., Robinson, D. (2017). “Text Mining with R”. O’Reilly.

scholarly journals Tracking drug-related overdoses at the local level: Using Syndromic Surveillance in the CO-NCR

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Emery Shekiro ◽  
Lily Sussman ◽  
Talia Brown
Keyword(s):  
Public Health ◽  
Emergency Department ◽  
Real Time ◽  
Drug Overdose ◽  
Syndromic Surveillance ◽  
Case Definition ◽  
Chief Complaint ◽  
Discharge Diagnosis ◽  
Diagnosis Codes ◽  
Drug Overdoses

Objective: In order to better describe local drug-related overdoses, we developed a novel syndromic case definition using discharge diagnosis codes from emergency department data in the Colorado North Central Region (CO-NCR). Secondarily, we used free text fields to understand the use of unspecified diagnosis fields.Introduction: The United States is in the midst of a drug crisis; drug-related overdoses are the leading cause of unintentional death in the country. In Colorado the rate of fatal drug overdose increased 68% from 2002-2014 (9.7 deaths per 100,000 to 16.3 per 100,000, respectively)1, and non-fatal overdose also increased during this time period (23% increase in emergency department visits since 2011)2. The CDC’s National Syndromic Surveillance Program (NSSP) provides near-real time monitoring of emergency department (ED) events across the country, with information uploaded daily on patient demographics, chief complaint for visit, diagnosis codes, triage notes, and more. Colorado North Central Region (CO-NCR) receives data for 4 local public health agencies from 25 hospitals across Adams, Arapahoe, Boulder, Denver, Douglas, and Jefferson Counties.Access to local syndromic data in near-real time provides valuable information for local public health program planning, policy, and evaluation efforts. However, use of these data also comes with many challenges. For example, we learned from key informant interviews with ED staff in Boulder and Denver counties, about concern with the accuracy and specificity of drug-related diagnosis codes, specifically for opioid-related overdoses.Methods: Boulder County Public Health (BCPH) and Denver Public Health (DPH) developed a query in Early Notification of Community Based Epidemics (ESSENCE) using ICD-10-CM codes to identify cases of drug-related overdose [T36-T51] from October 2016 to September 2017. The Case definition included unintentional, self-harm, assault and undetermined poisonings, but did not include cases coded as adverse effects or underdosing of medication. Cases identified in the query were stratified by demographic factors (i.e., gender and age) and substance used in poisoning. The first diagnosis code in the file was considered the primary diagnosis. Chief complaint and triage note fields were examined to further describe unspecified cases and to describe how patients present to emergency departments in the CO-NCR. We also explored whether detection of drug overdose visits captured by discharge diagnosis data varied by patient sex, age, or county.Results: The query identified 2,366 drug-related overdoses in the CO-NCR. The prevalence of drug overdoses differed across age groups. The detection of drug overdoses was highest among our youth and young adult populations; 16 to 20 year olds (16.0%), 21-25 year olds (11.4%), 26-30 year olds (11.4%). Females comprised 56.1% of probable general drug overdoses. The majority of primary diagnoses (31.0%) included poisonings related to diuretics and other unspecified drugs (T50), narcotics (T40) (12.6%), or non-opioid analgesics (T39) (7.8%). For some cases with unspecified drug overdose codes there was additional information about drugs used and narcan administration found in the triage notes and chief complaint fields.Conclusions: Syndromic surveillance offers the opportunity to capture drug-related overdose data in near-real time. We found variation in drug-related overdose by demographic groups. Unspecified drug overdose codes are extremely common, which likely negatively impacts the quality of drug-specific surveillance. Leveraging chief complaint and triage notes could improve our understanding of factors involved in drug-related overdose with limitations in discharge diagnosis. Chart reviews and access to more fields from the ED electronic health record could improve local drug surveillance.

scholarly journals Customizing ESSENCE Queries for Select Mental Health Sub-indicators

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Achintya N. Dey ◽  
Michael Coletta ◽  
Hong Zhou ◽  
Nelson Adekoya ◽  
Deborah Gould
Keyword(s):  
Public Health ◽  
Mental Health ◽  
Anxiety Disorders ◽  
Syndromic Surveillance ◽  
Depressive Disorders ◽  
National Level ◽  
Chief Complaint ◽  
Co Morbidity ◽  
Icd 10 ◽  
Ed Visits

ObjectiveEmergency department (ED) visits related to mental health (MH) disorders have increased since 2006 (1), indicating a potential burden on the healthcare delivery system. Surveillance systems has been developed to identify and understand these changing trends in how EDs are used and to characterize populations seeking care. Many state and local health departments are using syndromic surveillance to monitor MH-related ED visits in near real-time. This presentation describes how queries can be created and customized to identify select MH sub-indicators (for adults) by using chief complaint text terms and diagnoses codes. The MH sub-indicators examined are mood and depressive disorders, schizophrenic disorders, and anxiety disorders. Wider adoption of syndromic surveillance for characterizing MH disorders can support long-term planning for healthcare resources and service delivery.IntroductionSyndromic surveillance systems, although initially developed in response to bioterrorist threats, are increasingly being used at the local, state, and national level to support early identification of infectious disease and other emerging threats to public health. To facilitate detection, one of the goals of CDC’s National Syndromic Surveillance Program (NSSP) is to develop and share new sets of syndrome codes with the syndromic surveillance Community of Practice. Before analysts, epidemiologists, and other practitioners begin customizing queries to meet local needs, especially monitoring ED visits in near-real time during public health emergencies, they need to understand how syndromes are developed.More than 4,000 hospital routinely send data to NSSP’s BioSense Platform, representing about 55 percent of ED visits in the United States (2). The platform’s surveillance component, ESSENCE,* is a web-based application for analyzing and visualizing prediagnostic hospital ED data. ESSENCE’s Chief Complaint Query Validation (CCQV) data source, which is a national-level data source with access to chief complaint (CC) and discharge diagnoses (DD) from reporting sites, was designed for testing new queries.MethodsWe used ESSENCE CCQV to query weekly data for the nine week period from the first quarter of 2018 and looked at three common MH sub-indicators: mood and depressive disorders, schizophrenic disorders, and anxiety disorders. We developed four query types for each MH sub-indicator. Query-1 focused on DD codes; query-2 focused on CC text terms; query-3 focused on a combination of CC, DD, and no exclusion for mental health co-morbidity; and query-4 focused on a combination of CC and DD and excluded mental health co-morbidity. We also examined the summary distribution of CC texts to identify keywords related to MH sub-indicators.For mood and depressive disorders, we queried ICD-9 codes 296, 311; ICD-10 codes F30–F39; CC text terms for words “depressive disorder,” bipolar disorder,” “mood disorder,” “depression,” “manic episodes,” and “psychotic.” For schizophrenic disorders, we queried ICD-9 codes 295; ICD-10 codes F20–F29; CC text terms for words “psychosis,” “psychotic,” “schizo,” “delusional,” “paranoid,” “auditory,” “hallucinations,” and “hearing voices.” For anxiety disorders, we queried ICD-9 codes 300, 306, 307, 308, 309; ICD-10 codes F40–F48; CC text terms for words “anxiety,” “anexiy,” “aniety,” “aniexty,” “ansiety,” “anxety,” “anxity,” “anxiety,” “phobia,” and “panic attack.”ResultsWe identified 2.3 million average weekly ED visits for the 9-week period queried. Table 1 shows average weekly ED visits of select MH sub-indicators from the four query types. Because query 4 focused on specific MH outcomes and excluded MH co-morbidities, the average weekly ED visit for all three sub-indicators was almost half that of query 3, which focused on broader concepts by including MH co-morbidities. Among mood and depressive disorders, query 4 identified on average 23,352 ED visits per week versus 45,504 visits per week for query 3. Similarly, for schizophrenic disorders and anxiety disorders, query 4 identified on average 4,988 and 32,790 visits per week compared with 9,816 and 53,868 visits, respectively, for query 3. Further, more MH-related visits were identified using the DD-coded query (query 1) than CC-based text terms (query 2).ConclusionsAnalysts can benefit from having queries on select sub-indicators readily available and can use these to facilitate routine MH-related monitoring of ED visits, or customize the queries by including local text terms. Consistent with our previous work (3), this analysis demonstrated that MH-related ED visits are more likely to be found in DD codes than in CC alone.* Electronic Surveillance for the Early Notification of Community-based EpidemicsReferences[1] Weiss AJ, Barrett ML, Heslin KC , Stocks C. Trends in Emergency Department Visits Involving Mental and Substance Use Disorders, 2006–2013. HCUP Statistical Brief #216 [Internet]. Rockville (MD): Agency for Healthcare Research and Quality; 2016 Dec [cited 2018 Aug 14]. Available from: http://www.hcup-us.ahrq.gov/reports/statbriefs/sb216-Mental-Substance-Use-Disorder-ED-Visit-Trends.pdf.[2] Gould DW, Walker D, Yoon PW. The Evolution of BioSense: Lessons Learned and Future Directions. Public Health Reports. 2017 Jul/Aug;132(Suppl 1):S7–S11.[3] Dey AN, Gould D, Adekoya N, Hicks P, Ejigu GS, English R, Couse J, Zhou H. Use of Diagnosis Code in Mental Health Syndrome Definition. Online Journal of Public Health Informatics [Internet]. 2018 [cited 2018 Aug 14];10(1). Available from: https://doi.org/10.5210/ojphi.v10i1.8983

scholarly journals Real-time monitoring of a mass K2-related overdose outbreak – Connecticut, 2018

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Kristen Soto ◽  
Erin Grogan ◽  
Alexander Senetcky ◽  
Susan Logan
Keyword(s):  
Emergency Department ◽  
Real Time ◽  
Syndromic Surveillance ◽  
Situational Awareness ◽  
Chief Complaint ◽  
Surveillance Data ◽  
Emergency Department Utilization ◽  
Syndromic Surveillance System ◽  
Ed Visits ◽  
State And Local

ObjectiveTo describe the use of syndromic surveillance data for real-time situational awareness of emergency department utilization during a localized mass overdose event related to the substance K2.IntroductionOn August 15, 2018, the Connecticut Department of Public Health (DPH) became aware of a cluster of suspected overdoses in an urban park related to the synthetic cannabinoid K2. Abuse of K2 has been associated with serious adverse effects and overdose clusters have been reported in multiple states. This investigation aimed to characterize the use of syndromic surveillance data to monitor a cluster of suspected overdoses in real time.MethodsThe EpiCenter syndromic surveillance system collects data on all emergency department (ED) visits at Connecticut hospitals. ED visits associated with the event were identified using ad hoc keyword analyses. The number of visits by facility location for the state, county, and city were communicated to state and local partners in real time. Gender, age, and repeated ED visits were assessed. After the event, surveillance findings were summarized for partnersResultsDuring the period of August 15–16, 2018 the number of ED visits with a mention of K2 in the chief complaint increased from three to 30 in the impacted county, compared to a peak of 5 visits during the period of March–July, 2018. An additional 25 ED visits were identified using other related keywords (e.g., weed). After the event, 72 ED visits were identified with K2 and location keywords in the chief complaint or triage notes. These 72 visits comprised 53 unique patients, with 12 patients returning to the ED 2–5 times over the two day period. Of 53 patients, 77% were male and the median age was 40 years (interquartile range 35–51 years). Surveillance findings were shared with partners in real time for situational awareness, and in a summary report on August 21.ConclusionsData from the EpiCenter system were consistent with reports from other data sources regarding this cluster of suspected drug overdoses. Next steps related to this event involve: monitoring data for reference to areas of concentrated substance use, enabling automated alerts to detect clusters of interest, and developing a plan to improve coordinate real-time communication with stakeholderswithin DPH and with external partners during events.

scholarly journals Monitoring suicide-related events using National Syndromic Surveillance Program data

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Marissa L. Zwald ◽  
Kristin M. Holland ◽  
Francis Annor ◽  
Aaron Kite-Powell ◽  
Steven A. Sumner ◽  
...  
Keyword(s):  
Public Health ◽  
United States ◽  
Real Time ◽  
Syndromic Surveillance ◽  
The United States ◽  
Chief Complaint ◽  
Surveillance Program ◽  
Joinpoint Regression ◽  
Self Harm ◽  
Ed Visits

ObjectiveTo describe epidemiological characteristics of emergency department (ED) visits related to suicidal ideation (SI) or suicidal attempt (SA) using syndromic surveillance data.IntroductionSuicide is a growing public health problem in the United States.1 From 2001 to 2016, ED visit rates for nonfatal self-harm, a common risk factor for suicide, increased 42%.2–4 To improve public health surveillance of suicide-related problems, including SI and SA, the Data and Surveillance Task Force within the National Action Alliance for Suicide Prevention recommended the use of real-time data from hospital ED visits.5 The collection and use of real-time ED visit data on SI and SA could support a more targeted and timely public health response to prevent suicide.5 Therefore, this investigation aimed to monitor ED visits for SI or SA and to identify temporal, demographic, and geographic patterns using data from CDC’s National Syndromic Surveillance Program (NSSP).MethodsCDC’s NSSP data were used to monitor ED visits related to SI or SA among individuals aged 10 years and older from January 1, 2016 through July 31, 2018. A syndrome definition for SI or SA, developed by the International Society for Disease Surveillance’s syndrome definition committee in collaboration with CDC, was used to assess SI or SA-related ED visits. The syndrome definition was based on querying the chief complaint history, discharge diagnosis, and admission reason code and description fields for a combination of symptoms and Boolean operators (for example, hang, laceration, or overdose), as well as ICD-9-CM, ICD-10-CM, and SNOMED diagnostic codes associated with SI or SA. The definition was also developed to include common misspellings of self-harm-related terms and to exclude ED visits in which a patient “denied SI or SA.”The percentage of ED visits involving SI or SA were analyzed by month and stratified by sex, age group, and U.S. region. This was calculated by dividing the number of SI or SA-related ED visits by the total number of ED visits in each month. The average monthly percentage change of SI or SA overall and for each U.S. region was also calculated using the Joinpoint regression software (Surveillance Research Program, National Cancer Institute).6ResultsAmong approximately 259 million ED visits assessed in NSSP from January 2016 to July 2018, a total of 2,301,215 SI or SA-related visits were identified. Over this period, males accounted for 51.2% of ED visits related to SI or SA, and approximately 42.1% of SI or SA-related visits were comprised of patients who were 20-39 years, followed by 40-59 years (29.7%), 10-19 years (20.5%), and ≥60 years (7.7%).During this period, the average monthly percentage of ED visits involving SI or SA significantly increased 1.1%. As shown in Figure 1, all U.S. regions, except for the Southwest region, experienced significant increases in SI or SA ED visits from January 2016 to July 2018. The average monthly increase of SI or SA-related ED visits was 1.9% for the Midwest, 1.5% for the West (1.5%), 1.1% for the Northeast, 0.9% for the Southeast, and 0.5% for the Southwest.ConclusionsED visits for SI or SA increased from January 2016 to June 2018 and varied by U.S. region. In contrast to previous findings reporting data from the National Electronic Injury Surveillance Program – All-Injury Program, we observed different trends in SI or SA by sex, where more ED visits were comprised of patients who were male in our investigation.2 Syndromic surveillance data can fill an existing gap in the national surveillance of suicide-related problems by providing close to real-time information on SI or SA-related ED visits.5 However, our investigation is subject to some limitations. NSSP data is not nationally representative and therefore, these findings are not generalizable to areas not participating in NSSP. The syndrome definition may under-or over-estimate SI or SA based on coding differences and differences in chief complaint or discharge diagnosis data between jurisdictions. Finally, hospital participation in NSSP can vary across months, which could potentially contribute to trends observed in NSSP data. Despite these limitations, states and communities could use this type of surveillance data to detect abnormal patterns at more detailed geographic levels and facilitate rapid response efforts. States and communities can also use resources such as CDC’s Preventing Suicide: A Technical Package of Policy, Programs, and Practices to guide prevention decision-making and implement comprehensive suicide prevention approaches based on the best available evidence.7References1. Stone DM, Simon TR, Fowler KA, et al. Vital Signs: Trends in State Suicide Rates — United States, 1999–2016 and Circumstances Contributing to Suicide — 27 States, 2015. Morb Mortal Wkly Rep. 2018;67(22):617-624.2. CDCs National Center for Injury Prevention and Control. Web-based Injury Statistics Query and Reporting System (WISQARS). https://www.cdc.gov/injury/wisqars/index.html. Published 2018. Accessed September 1, 2018.3. Mercado M, Holland K, Leemis R, Stone D, Wang J. Trends in emergency department visits for nonfatal self-inflicted injuries among youth aged 10 to 24 years in the United States, 2005-2015. J Am Med Assoc. 2017;318(19):1931-1933. doi:10.1001/jama.2017.133174. Olfson M, Blanco C, Wall M, et al. National Trends in Suicide Attempts Among Adults in the United States. JAMA Psychiatry. 2017;10032(11):1095-1103. doi:10.1001/jamapsychiatry.2017.25825. Ikeda R, Hedegaard H, Bossarte R, et al. Improving national data systems for surveillance of suicide-related events. Am J Prev Med. 2014;47(3 SUPPL. 2):S122-S129. doi:10.1016/j.amepre.2014.05.0266. National Cancer Institute. Joinpoint Regression Software. https://surveillance.cancer.gov/joinpoint/. Published 2018. Accessed September 1, 2018.7. Centers for Disease Control and Prevention. Preventing Suicide: A Technical Package of Policy, Programs, and Practices. 

scholarly journals Monitoring Out-of-State Patients during a 2017 Hurricane Response using ESSENCE

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Caleb Wiedeman ◽  
Julie Shaffner ◽  
Kelly Squires ◽  
Jeffrey Leegon ◽  
Rendi Murphree ◽  
...  
Keyword(s):  
Emergency Department ◽  
Syndromic Surveillance ◽  
Situational Awareness ◽  
Chief Complaint ◽  
Free Text ◽  
Chief Complaints ◽  
Major Hurricane ◽  
Ed Visits ◽  
Zip Code ◽  
Hurricane Irma

ObjectiveTo demonstrate the use of ESSENCE in the BioSense Platform to monitor out-of-State patients seeking emergency healthcare in Tennessee during Hurricanes Harvey and Irma.IntroductionSyndromic surveillance is the monitoring of symptom combinations (i.e., syndromes) or other indicators within a population to inform public health actions. The Tennessee Department of Health (TDH) collects emergency department (ED) data from more than 70 hospitals across Tennessee to support statewide syndromic surveillance activities. Hospitals in Tennessee typically provide data within 48 hours of a patient encounter. While syndromic surveillance often supplements disease- or condition-specific surveillance, it can also provide general situational awareness about emergency department patients during an event or response.During Hurricanes Harvey (continental US landfall on August 25, 2017) and Irma (continental US landfall on September 10, 2017), TDH supported all hazards situational awareness using the Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE) in the BioSense Platform supported by the National Syndromic Surveillance Program (NSSP). The volume of out-of-state patients in Tennessee was monitored to assess the impact on the healthcare system and any geographic- or hospital-specific clustering of out-of-state patients within Tennessee. Results were included in daily State Health Operations Center (SHOC) situation reports and shared with agency response partners such as the Tennessee Emergency Management Agency (TEMA).MethodsData were monitored from August 18, 2017 through September 24, 2017. A simple query was established in ESSENCE using the Patient Location (Full Details) dataset. Data were limited to hospital ED visits reported by Tennessee (Site = “Tennessee”). To monitor ED visits among residents of Texas before, during, and after Major Hurricane Harvey, data were queried for a patient zip code within Texas (State = “Texas”). ED visits among Florida residents were monitored similarly (State = “Florida”) before, during, and after Major Hurricane Irma. Additionally, a free text chief complaint search was implemented for the terms “Harvey”, “Irma, “hurricane”, “evacuee”, “evacuate”, “Florida”, and “Texas”. Chief complaint search results were then filtered to remove encounters with patient zip codes within Tennessee.ResultsFrom August 18, 2017 through September 24, 2017, Tennessee hospital EDs reported 277 patient encounters among Texas residents and 1,041 patient encounters among Florida residents. The number of encounters among patients from Texas remained stable throughout the monitoring period. In contrast, the number of encounters among patients from Florida exceeded the expected value on September 7, peaked September 10 at 116 patient encounters, and returned to expected levels on September 16 (Figure 1). The increase in patients from Florida was evenly distributed across most of Tennessee, with some clustering around a popular tourism area in East Tennessee. No concerning trends in reported syndromes or chief complaints were identified among Texas or Florida patients.The free text chief complaint query first exceeded the expected value on September 9, peaked on September 11 with 5 patient encounters, and returned to expected levels on September 14. From August 18 through September 24, 21 of 30 visits captured by the query were among Florida residents. One Tennessee hospital appeared to be intentionally using the term “Irma” in their chief complaint field to indicate patients from Florida impacted by the hurricane.ConclusionsThe ESSENCE instance in the BioSense platform provided TDH the opportunity to easily locate and monitor out-of-state patients seen in Tennessee hospital EDs. While TDH was unable to validate whether all patients identified as residents of Florida were displaced because of Major Hurricane Irma, the timing of the rise and fall of patient encounters was highly suggestive. Likewise, seeing no substantial increase ED patients with residence in Texas reassured TDH that the effects of Hurricane Harvey were not impacting hospital emergency departments in Tennessee.TDH used information and charts from ESSENCE to support situational awareness in our SHOC and at TEMA. Use of patient zip code to identify out-of-state residents was more sensitive than chief complaint searches by keyword during this event. ESSENCE allowed TDH to see where out-of-state patients appeared to be concentrating in Tennessee and monitor the need for targeting messaging and resources to heavily affected areas. Additionally, close surveillance of chief complaints among out-of-state patients provided assurance that no unusual patterns in illness or injury were occurring.ESSENCE is the only TDH information source capable of rapidly collecting health information on out-of-state patients. ESSENCE allowed TDH to quickly identify a change within the patient population seen at Tennessee emergency departments and monitor the situation until the patient population returned to baseline levels.

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 Interrupted time series design to evaluate ICD-9-CM to ICD-10-CM coding changes on trends in Colorado emergency department visits related to traumatic brain injury

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Lauren Alexis De Crescenzo ◽  
Barbara Alison Gabella ◽  
Jewell Johnson
Keyword(s):  
Public Health ◽  
Traumatic Brain Injury ◽  
Emergency Department ◽  
Brain Injury ◽  
Interrupted Time Series ◽  
Emergency Department Visits ◽  
Control And Prevention ◽  
Icd 10 ◽  
Ed Visits ◽  
The Impact

Abstract Background The transition in 2015 to the Tenth Revision of the International Classification of Disease, Clinical Modification (ICD-10-CM) in the US led the Centers for Disease Control and Prevention (CDC) to propose a surveillance definition of traumatic brain injury (TBI) utilizing ICD-10-CM codes. The CDC’s proposed surveillance definition excludes “unspecified injury of the head,” previously included in the ICD-9-CM TBI surveillance definition. The study purpose was to evaluate the impact of the TBI surveillance definition change on monthly rates of TBI-related emergency department (ED) visits in Colorado from 2012 to 2017. Results The monthly rate of TBI-related ED visits was 55.6 visits per 100,000 persons in January 2012. This rate in the transition month to ICD-10-CM (October 2015) decreased by 41 visits per 100,000 persons (p-value < 0.0001), compared to September 2015, and remained low through December 2017, due to the exclusion of “unspecified injury of head” (ICD-10-CM code S09.90) in the proposed TBI definition. The average increase in the rate was 0.33 visits per month (p < 0.01) prior to October 2015, and 0.04 visits after. When S09.90 was included in the model, the monthly TBI rate in Colorado remained smooth from ICD-9-CM to ICD-10-CM and the transition was no longer significant (p = 0.97). Conclusion The reduction in the monthly TBI-related ED visit rate resulted from the CDC TBI surveillance definition excluding unspecified head injury, not necessarily the coding transition itself. Public health practitioners should be aware that the definition change could lead to a drastic reduction in the magnitude and trend of TBI-related ED visits, which could affect decisions regarding the allocation of TBI resources. This study highlights a challenge in creating a standardized set of TBI ICD-10-CM codes for public health surveillance that provides comparable yet clinically relevant estimates that span the ICD transition.

scholarly journals Syndromic Surveillance of Emergency Department Visits for Acute Adverse Effects of Marijuana, Tri-County Health Department, Colorado, 2016-2017

2019 ◽  
Vol 134 (2) ◽  
pp. 132-140 ◽  
Author(s):  
Grace E. Marx ◽  
Yushiuan Chen ◽  
Michele Askenazi ◽  
Bernadette A. Albanese
Keyword(s):  
Emergency Department ◽  
Adverse Effects ◽  
Medical Records ◽  
Surveillance System ◽  
Syndromic Surveillance ◽  
Marijuana Use ◽  
Emergency Department Visits ◽  
Diagnostic Codes ◽  
Icd 10 ◽  
Ed Visits

Objectives: In Colorado, legalization of recreational marijuana in 2014 increased public access to marijuana and might also have led to an increase in emergency department (ED) visits. We examined the validity of using syndromic surveillance data to detect marijuana-associated ED visits by comparing the performance of surveillance queries with physician-reviewed medical records. Methods: We developed queries of combinations of marijuana-specific International Classification of Diseases, Tenth Revision (ICD-10) diagnostic codes or keywords. We applied these queries to ED visit data submitted through the Electronic Surveillance System for the Early Notification of Community-Based Epidemics (ESSENCE) syndromic surveillance system at 3 hospitals during 2016-2017. One physician reviewed the medical records of ED visits identified by ≥1 query and calculated the positive predictive value (PPV) of each query. We defined cases of acute adverse effects of marijuana (AAEM) as determined by the ED provider’s clinical impression during the visit. Results: Of 44 942 total ED visits, ESSENCE queries detected 453 (1%) as potential AAEM cases; a review of 422 (93%) medical records identified 188 (45%) true AAEM cases. Queries using ICD-10 diagnostic codes or keywords in the triage note identified all true AAEM cases; PPV varied by hospital from 36% to 64%. Of the 188 true AAEM cases, 109 (58%) were among men and 178 (95%) reported intentional use of marijuana. Compared with noncases of AAEM, cases were significantly more likely to be among non-Colorado residents than among Colorado residents and were significantly more likely to report edible marijuana use rather than smoked marijuana use ( P < .001). Conclusions: ICD-10 diagnostic codes and triage note keyword queries in ESSENCE, validated by medical record review, can be used to track ED visits for AAEM.

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