scholarly journals Syndromic surveillance from free-text triage chief complaints

2003 ◽  
Vol 80 (S1) ◽  
pp. i120-i120 ◽  
Author(s):  
Wendy W. Chapman ◽  
Michael M. Wagner ◽  
Oleg Ivanov ◽  
Robert Olszewski ◽  
John N. Dowling
Keyword(s):  
Syndromic Surveillance ◽  
Free Text ◽  
Chief Complaints

scholarly journals Recognizing Recreational Water Exposure and Habituating HAB Surveillance in ESSENCE

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Kathryn Kuspis ◽  
Meredith Jagger ◽  
Melissa Powell ◽  
Rebecca Hillwig
Keyword(s):  
Public Health ◽  
Syndromic Surveillance ◽  
Algal Bloom ◽  
Urgent Care ◽  
Contaminated Water ◽  
Free Text ◽  
Recreational Water ◽  
Environmental Public Health ◽  
Gi Symptoms ◽  
Chief Complaints

ObjectiveUse ESSENCE to create a sustainable process for identifying ED and urgent care visits that may be related to harmful algal bloom exposure in Oregon.IntroductionHarmful algal blooms (HABs) consist of colonies of prokaryotic photosynthetic bacteria algae that can produce harmful toxins. The toxins produced by HABs are considered a One Health issue. HABs can occur in all types of water (fresh, brackish, and salt water) and are composed of cyanobacteria or microalgae. As the climate changes, so do many of the factors that contribute to the growth of HABs, which in turn, can increase the incidence of HAB-related illness in humans.There are three main pathways that HAB toxins can affect human health: dermal, gastrointestinal (GI), and neurological. Swimming in or consuming contaminated water and eating contaminated shellfish are ways to develop HAB-related illnesses. Contact with cells from a bloom while recreating can cause a rash on the body. Most commonly, HAB-related illnesses present with GI symptoms that resemble food poisoning and can affect the liver. Rarely, HABs that produce cyanotoxins can present with neurological symptoms.Issuing and lifting freshwater HAB advisories is within the preview of the Environmental Public Health section at the Oregon Public Health Division. However, most water bodies in the state are not monitored. Because of this, syndromic surveillance was considered as a potentially useful source of HAB exposure information, and the Oregon ESSENCE team was asked to develop a query to help monitor HAB-related complaints.MethodsPreliminary research was done on HABs and the associated health issues, and past advisories were examined to identify locations of interest. Next, keywords and symptoms were evaluated.Initially, the objective was to create a single query for HAB syndromic surveillance, but it became evident that multiple queries would have to be developed to fully encompass the various types of HAB-related illnesses: GI, neurological, and rash.Most commonly Oregon ESSENCE uses chief complaint and discharge diagnosis (CCDD) queries. However, the ICD-10 codes relating to HABs are not widely used, with only two occurrences since June 2015. It was determined that using the already established ESSENCE syndromes of Neuro, GI, and Rash would be most useful. To make the queries HAB-specific, an additional exposure element needed to be added. Exposures to HABs that are of interest occur in recreational freshwater sources. After running this query in the CCDD field, it was determined that the triage note field would yield better results. This is because this field often includes the patient’s verbatim complaints. This produced higher quality results, and a seasonal curve of cases could be seen in the historic data.Since the microcystin threshold for illness is significantly lower for pets; and a permanent HAB alert in southern Oregon was established after several dogs died from drinking contaminated water, tracking neurological cases that followed canine illness was investigated. A free-text triage note query was developed for patients mentioning dogs, and it was combined with the ESSENCE Neuro syndrome. After several attempts, it was clear that this would not be helpful for surveillance of HAB-related illnesses.Ultimately, four query configurations were developed to monitor HAB-related illness. Most importantly, a free-text recreational water query was developed to stand alone and then be paired with three distinct ESSENCE syndromes.Recreational water query text: (, (, ^ lake^ ,andnot, (, ^road^ ,or, ^rd^ ,or, ^sky^ ,or, ^oswego^ ,or, ^view^ ,) ,) ,or, ^swim^ ,or, (, ^ river ^ ,andnot, (, ^driver^, or, ^hood^ ,or, ^rd^ ,or, ^road^ ,or, ^three^ ,) ,) ,or, ^ boat^ ,) ,andnot, ^feels like^All queries were compiled into a myESSENCE page that could be shared for easy monitoring by all members of the team (Figure 1).ResultsThe ESSENCE team monitored the HAB myESSENCE page. The monitoring period for this project stretched from May to early August (MMWR weeks 19-31). Motoring was often informed by HAB alerts and required looking closely at individual visits. Over this time, the number of recreational water related visits varied, but the average was approximately 110 visits a week. This techniques also helped identify cases possibly related to unreported blooms. The months of June and July saw 15 specific cases that were potentially due to HAB exposure. These cases were highlighted and forwarded to Environmental Public Health for investigation.ConclusionsThis process helped refine the use of the triage note field when constructing keyword queries. While not all Oregon facilities provide triage notes, using specific terms allows ESSENCE users to search for words that may not be included in chief complaints. This is most be useful when searching for specific places or events. With further analysis, users can see what chief complaints are most likely to occur in conjunction with specific exposures. Moving forward, the development of a recreational water query has proven to be useful beyond the scope of this HAB project. Alternative versions of this query have been used in other contexts.ReferencesHarmful Algal Bloom (HAB)-Associated Illness. (2017, June 01). Retrieved August 01, 2017, from https://www.cdc.gov/habs/index.html

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 Using n-Grams for Syndromic Surveillance in a Turkish Emergency Department without English Translation: A Feasibility Study

2013 ◽  
Vol 6 ◽  
pp. BII.S11334
Author(s):  
Sylvia Halász ◽  
Philip Brown ◽  
Cem Oktay ◽  
Arif Alper Çevik ◽  
Isa Kılıçaslan ◽  
...  
Keyword(s):  
Emergency Department ◽  
Feasibility Study ◽  
Syndromic Surveillance ◽  
Free Text ◽  
Test Set ◽  
Early Detection Of Disease ◽  
Chief Complaints ◽  
Icd 10 ◽  
Icd Codes ◽  
N Gram

Introduction: Syndromic surveillance is designed for early detection of disease outbreaks. An important data source for syndromic surveillance is free-text chief complaints (CCs), which are generally recorded in the local language. For automated syndromic surveillance, CCs must be classified into predefined syndromic categories. The n-gram classifier is created by using text fragments to measure associations between chief complaints (CC) and a syndromic grouping of ICD codes. Objectives: The objective was to create a Turkish n-gram CC classifier for the respiratory syndrome and then compare daily volumes between the n-gram CC classifier and a respiratory ICD-10 code grouping on a test set of data. Methods: The design was a feasibility study based on retrospective cohort data. The setting was a university hospital emergency department (ED) in Turkey. Included were all ED visits in the 2002 database of this hospital. Two of the authors created a respiratory grouping of International Classification of Diseases, 10th Revision ICD-10-CM codes by consensus, chosen to be similar to a standard respiratory (RESP) grouping of ICD codes created by the Electronic Surveillance System for Early Notification of Community-based Epidemics (ESSENCE), a project of the Centers for Disease Control and Prevention. An n-gram method adapted from AT&T Labs’ technologies was applied to the first 10 months of data as a training set to create a Turkish CC RESP classifier. The classifier was then tested on the subsequent 2 months of visits to generate a time series graph and determine the correlation with daily volumes measured by the CC classifier versus the RESP ICD-10 grouping. Results: The Turkish ED database contained 30,157 visits. The correlation ( R2) of n-gram versus ICD-10 for the test set was 0.78. Conclusion: The n-gram method automatically created a CC RESP classifier of the Turkish CCs that performed similarly to the ICD-10 RESP grouping. The n-gram technique has the advantage of systematic, consistent, and rapid deployment as well as language independence.

scholarly journals Impact of the NSSP’s transition to ESSENCE on chief complaint field-based syndromes

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Rasneet S Kumar ◽  
Jessica R White
Keyword(s):  
Time Series ◽  
Syndromic Surveillance ◽  
Data Access ◽  
Case Finding ◽  
Chief Complaint ◽  
Analytical Tool ◽  
Free Text ◽  
Guidance Document ◽  
Maricopa County ◽  
Analytical Tools

Objective: To evaluate the effect and implications of changing the chief complaint field during the National Syndromic Surveillance Program (NSSP) transition from BioSense 2.0 analytical tools to BioSense Platform – ESSENCEIntroduction: In January 2017, the NSSP transitioned their BioSense analytical tools to Electronic Surveillance System for Early Notification of Community-Based Epidemics (ESSENCE). The chief complaint field in BioSense 2.0 was a concatenation of the record’s chief complaint, admission reason, triage notes, and diagnostic impression. Following the transition to ESSENCE, the chief complaint field was comprised of the first chief complaint entered or the first admission reason, if the chief complaint was blank. Furthermore, the ESSENCE chief complaint field was electronically parsed (i.e., the original chief complaint text was altered to translate abbreviations and remove punctuation). This abstract highlights key findings from Maricopa County Department of Public Health’s evaluation of the new chief complaint field, its impact on heat-related illness syndromic surveillance, and implications for ongoing surveillance efforts.Methods: For this evaluation, we used the heat-related illness query recommended in Council of State and Territorial Epidemiologists’ (CSTE)2016 Guidance Document for Implementing Heat-Related Illness Syndromic Surveillance. Before the transition, we used BioSense 2.0’s, phpMyAdmin analytical tool to generate a list of patients who visited Maricopa County emergency departments or inpatient hospitals between 5/1/2016 – 9/30/2016 due to heat-related illness. After the transition, we used the CC and DD Category “Heat-related Illness, v1” in ESSENCE, which was based on the CSTE heat-related illness query, to generate a list of patients for the same time period. We compared the line-lists and time-series trends from phpMyAdmin and ESSENCE.Results: The phpMyAdmin analytical tool identified 785 heat-related illness records with the query (Figure). 642 (82%) of these heat-related illness records were also captured by ESSENCE. Reasons for 143 (18%) records not being identified by ESSENCE included: the patient’s admission reason field contained keywords that were not available in the ESSENCE chief complaint field (n=94, 66%); data access changed, which disabled access to patients who resided in zip codes that crossed a county border (30, 21%); discrepancies between ESSENCE parsing and text in the original chief complaint (11, 8%); heat-related illness discharge diagnoses were removed by the facility after the phpMyAdmin line-list for heat-related illness was extracted (7, 5%); and one record was undetermined. Conversely, ESSENCE captured 36 additional heat-related illness records, not previously captured by phpMyAdmin. Reasons included: a query exclusion term was located in the patient’s admission reason but not the ESSENCE chief complaint field (16, 44%); a heat-related illness discharge diagnosis code was added by the facility after the data were extracted by phpMyAdmin (4, 11%); and 16 (44%) were undetermined. Time-series trend evaluation revealed a significant correlation between the two surveillance tools (Pearson coefficient = 0.97, p < 0.01).Conclusions: Though the data trends over time were not significantly affected by changes in the chief complaint field, differences in the field’s composition have important implications for syndromic surveillance practitioners. Free-text queries designed to search the chief complaint field in ESSENCE may not retrieve records previously identified with BioSense 2.0 analytical tools, which may limit individual case-finding capacity. The elimination of admission reason from the chief complaint field in ESSENCE has the greatest effect on case-finding capacity. Furthermore, surveillance reports produced by ESSENCE cannot be directly compared to reports that were previously published with data from BioSense 2.0. These limitations may be addressed if ESSENCE creates a feature that allows users to easily query fields (e.g., admission reason) in addition to the chief compliant field.

scholarly journals Developing Syndromic Surveillance to Monitor and Respond to Adverse Health Events Related to Psychoactive Substance Use: Methods and Applications

2017 ◽  
Vol 132 (1_suppl) ◽  
pp. 65S-72S ◽  
Author(s):  
Michelle L. Nolan ◽  
Hillary V. Kunins ◽  
Ramona Lall ◽  
Denise Paone
Keyword(s):  
Public Health ◽  
New York ◽  
New York City ◽  
York City ◽  
Syndromic Surveillance ◽  
Psychoactive Drug ◽  
Surveillance Systems ◽  
Chief Complaints ◽  
Level Information ◽  
Ed Visits

Introduction: Recent increases in drug overdose deaths, both in New York City and nationally, highlight the need for timely data on psychoactive drug-related morbidity. We developed drug syndrome definitions for syndromic surveillance to monitor drug-related emergency department (ED) visits in real time. Materials and Methods: We used 2012 archived syndromic surveillance data from New York City hospitals to develop definitions for psychoactive drug-related syndromes. The dataset contained ED visit-level information that included patients’ chief complaints, dates of visits, ZIP codes of residence, discharge diagnoses, and dispositions. After manually reviewing chief complaints, we developed a classification scheme comprising 3 categories (overdose, drug mention, and drug abuse/misuse), which we used to define 25 psychoactive drug syndromes. From July 2013 through December 2015, the New York City Department of Health and Mental Hygiene performed daily syndromic surveillance of psychoactive drug-related ED visits using the 25 syndrome definitions. Results: Syndromic surveillance triggered 4 public health investigations, supported 8 other public health investigations that had been triggered by other mechanisms, and resulted in the identification of 5 psychoactive drug-related outbreaks. Syndromic surveillance also identified a substantial increase in synthetic cannabinoid-related visits (from an average of 3 per week in January 2014 to >300 per week in July 2015) and an increase in heroin overdose visits (from 80 to 171 in the first 3 quarters of 2012 and 2014, respectively) in a single neighborhood. Practice Implications: Syndromic surveillance using these novel definitions enabled monitoring of trends in psychoactive drug-related morbidity, initiation and support of public health investigations, and targeting of interventions. Health departments can refine these definitions for their jurisdictions using the described methods and integrate them into existing syndromic surveillance systems.

Classifying free-text triage chief complaints into syndromic categories with natural language processing

2005 ◽  
Vol 33 (1) ◽  
pp. 31-40 ◽  
Author(s):  
Wendy W. Chapman ◽  
Lee M. Christensen ◽  
Michael M. Wagner ◽  
Peter J. Haug ◽  
Oleg Ivanov ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Free Text ◽  
Chief Complaints

Window of Opportunity for New Disease Surveillance: Developing Keyword Lists for Monitoring Mental Health and Injury Through Syndromic Surveillance

2016 ◽  
Vol 11 (2) ◽  
pp. 173-178 ◽  
Author(s):  
Ursula Lauper ◽  
Jian-Hua Chen ◽  
Shao Lin
Keyword(s):  
Public Health ◽  
Mental Health ◽  
New York ◽  
Syndromic Surveillance ◽  
New York State ◽  
Hurricane Sandy ◽  
York State Department ◽  
Free Text ◽  
York State ◽  
The Impact

AbstractStudies have documented the impact that hurricanes have on mental health and injury rates before, during, and after the event. Since timely tracking of these disease patterns is crucial to disaster planning, response, and recovery, syndromic surveillance keyword filters were developed by the New York State Department of Health to study the short- and long-term impacts of Hurricane Sandy. Emergency department syndromic surveillance is recognized as a valuable tool for informing public health activities during and immediately following a disaster. Data typically consist of daily visit reports from hospital emergency departments (EDs) of basic patient data and free-text chief complaints. To develop keyword lists, comparisons were made with existing CDC categories and then integrated with lists from the New York City and New Jersey health departments in a collaborative effort. Two comprehensive lists were developed, each containing multiple subcategories and over 100 keywords for both mental health and injury. The data classifiers using these keywords were used to assess impacts of Sandy on mental health and injuries in New York State. The lists will be validated by comparing the ED chief complaint keyword with the final ICD diagnosis code. (Disaster Med Public Health Preparedness. 2017;11:173–178)

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.

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