scholarly journals Assessment of the use of ED Chief Complaint Data for monitoring Chronic Diseases

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Megan T. Patel
Keyword(s):  
Emergency Department ◽  
Chronic Disease ◽  
Hospital Discharge ◽  
Pearson Correlation ◽  
Chief Complaint ◽  
Cook County ◽  
Free Text ◽  
Disease Category ◽  
Discharge Data ◽  
Predictive Values

ObjectiveTo create chronic disease categories for emergency department (ED) chief complaint data and evaluate the categories for validity against ED data with discharge diagnoses and hospital discharge data.IntroductionSyndromic Surveillance (SS), traditionally applied to infectious diseases, is more recently being adapted to chronic disease prevention. Its usefulness rests on the large number of diverse individuals visiting emergency rooms with the possibility of real-time monitoring of acute health effects, including effects from environmental events and its potential ability to examine more long-term health effects and trends of chronic diseases on a local level [1-3].MethodsEmergency department chief complaint (CC) data captured by the Cook County Department of Public Health local instance of ESSENCE from Jan 1, 2006 – Dec 31, 2013 was utilized to generate chronic disease categories for: CVD, AMI, ACS, angina, stroke, diabetes, hypertension, asthma, and COPD based on disease symptoms, natural language processing for free text chief complaints, and associated terms present in EMR system menus.A standard category was created for each chronic disease category based on discharge diagnoses (ICD-9 code), and their associated terms. The ICD-9 based categories were applied to the discharge diagnosis field within the ED data. The chief complaint based chronic disease category definitions were compared to the standard classification by determining the sensitivity, specificity, positive predictive value, and negative predictive value.The standard chronic disease categories created with ICD-9 codes for the chronic disease category validation were also applied to Illinois hospital discharge data for Cook County from Jan 1, 2006 – Dec 31, 2013. This data was compared to the chief complaint categories from the ED data for the same time period by visual analysis through time series and strength of correlation by Pearson correlation coefficient analysis. ESSENCE version 1.17 was utilized for the free-text query development and SAS 9.4 was utilized to perform the analyses.ResultsFor the validation analysis, 1,366,525 (24.76%) ED visits of individuals 40 years and older and 867,509 (15.72%) ED visits of individuals less than 18 years of age with a valid chief complaint and discharge diagnosis were included. Validation results are presented in Table 1. Specificity was generally high for most of the categories, with the narrow definitions having a higher specificity (Narrow AMI = 0.9996, Broad AMI = 0.9119). However, the loss in sensitivity is substantial in moving from the broader definition to the narrow definition (Broad AMI = 0.5444, Narrow AMI = 0.1040). The positive predictive values had a wide range from 0.0128 for the Broad ACS category to 0.7199 for the Narrow Asthma definition. The negative predictive values were high for all chronic disease categories ranging from 0.9501 for the Narrow CVD category to 0.9996 for Angina.The Pearson correlation coefficients are presented in Table 2. Graphs showing the comparisons of the chief complaint based ED data to the hospitalization data by chronic disease category definition are presented in Figure 1. Pearson correlations ranged from 0.9323 for Narrow Asthma to 0.1992 for Hypertension.ConclusionsBased on the high specificity and correlation coefficients in comparison to hospital discharge data, emergency department chief complaint data captured with syndromic surveillance could be utilized to examine chronic disease categories: asthma, COPD, CVD, AMI, ACS, stroke, and diabetes at a local, state or national level.References1. Bassil, K.L., et al., Temporal and spatial variation of heat-related illness using 911 medical dispatch data. Environ Res, 2009. 109(5): p. 600-6.2. Mathes, R.W., K. Ito, and T. Matte, Assessing syndromic surveillance of cardiovascular outcomes from emergency department chief complaint data in New York City. PLoS One, 2011. 6(2): p. e14677.3. Zanobetti, A. and J. Schwartz, Air pollution and emergency admissions in Boston, MA. J Epidemiol Community Health, 2006. 60(10): p. 890-5.

Transitions in “Privatized” Prison Health Systems: Emergency Department Visits and Hospitalizations Among Incarcerated People in Florida, 2011–2018

2021 ◽  
pp. e1-e4
Author(s):  
Jessica L. Adler ◽  
Weiwei Chen ◽  
Timothy F. Page
Keyword(s):  
Public Health ◽  
Health Care ◽  
Emergency Department ◽  
Hospital Discharge ◽  
Emergency Department Visits ◽  
Care Utilization ◽  
Discharge Data ◽  
Prison Health ◽  
Discharge Rates ◽  
Care Problems

Objectives. To examine rates of emergency department (ED) visits and hospitalizations among incarcerated people in Florida during a period when health care management in the state’s prisons underwent transitions. Methods. We used Florida ED visit and hospital discharge data (2011–2018) to depict the trend in ED visit and hospital discharge rates among incarcerated people. We proxied incarcerated people using individuals admitted from and discharged or transferred to a court or law enforcement agency. We fitted a regression with year indicators to examine the significance of yearly changes. Results. Among incarcerated people in Florida, ED visit rates quadrupled, and hospitalization rates doubled, between 2015 and 2018, a period when no similar trends were evident in the nonincarcerated population. Public Health Implications. Increasing the amount and flexibility of payments to contractors overseeing prison health services may foster higher rates of hospital utilization among incarcerated people and higher costs, without addressing major quality of care problems. Hospitals and government agencies should transparently report on health care utilization and outcomes among incarcerated people to ensure better oversight of services for a highly vulnerable population. (Am J Public Health. Published online ahead of print March 18, 2021: e1–e4. https://doi.org/10.2105/AJPH.2020.305988 )

scholarly journals Opioid prescribing in the emergency department of a tertiary hospital: A retrospective audit of hospital discharge data

2019 ◽  
Vol 32 (1) ◽  
pp. 33-38 ◽  
Author(s):  
Beata Stanley ◽  
Lisa J Collins ◽  
Amanda F Norman ◽  
Jonathon Karro ◽  
Monica Jung ◽  
...  
Keyword(s):  
Emergency Department ◽  
Hospital Discharge ◽  
Tertiary Hospital ◽  
Hospital Discharge Data ◽  
Discharge Data ◽  
Opioid Prescribing ◽  
Retrospective Audit

scholarly journals Development of Syndrome Definitions for Acute Unintentional Drug and Heroin Overdose

2017 ◽  
Vol 9 (1) ◽  
Author(s):  
Em Stephens
Keyword(s):  
Public Health ◽  
Chronic Pain ◽  
Real Time ◽  
Correlation Coefficient ◽  
Drug Overdose ◽  
Chief Complaint ◽  
Opioid Overdose ◽  
Free Text ◽  
Discharge Data ◽  
Heroin Overdose

ObjectiveTo develop and evaluate syndrome definitions for the identificationof acute unintentional drug overdose events including opioid, heroin,and unspecified substances among emergency department (ED) visitsin Virginia.IntroductionNationally, deaths due to opioid overdose have continuallyincreased for the past 15 years1. Deaths specifically related to heroinincreased more than four-fold between 2002 and 20142. Hospitalinpatient discharge data provide information on non-fatal overdoses,but include a significant lag in reporting time3. Syndromic ED visitdata provide near real-time identification of public health issues andcan be leveraged to inform public health actions on the emergingthreat of drug overdose.MethodsVirginia Department of Health (VDH) developed two syndromedefinitions in 2014 to capture acute unintentional drug overdoseevents among syndromic ED visit data. Syndrome 1 captured visitsfor overdose, whether or not a specific substance was mentioned.Syndrome 2 captured only visits for heroin overdose. Definitionswere based on free-text terms found within the chief complaintand standardized text or International Classification of Diseases(ICD) codes within the diagnosis field. In 2016, both definitionswere revised to identify additional inclusion and exclusion criteriaaccording to CDC guidance documentation and syndrome definitionsused by other state jurisdictions.Microsoft SQL was used to modify both definitions based on thenewly identified chief complaint and diagnosis criteria. Record leveldata were analyzed for their adherence to established criteria using aniterative evaluation process.The scope of Syndrome 1 (2016) was narrowed from the 2014version by excluding visits for non-opioid substances, heroin, andnon-acute indicators. It included chief complaint and diagnosisterms related to opioids, unspecified substance overdose, narcotics,and Narcan or naloxone, and excluded terms related to suicide,alcohol overdose alone, withdrawal, detoxification, rehab, addiction,constipation, chronic pain, and any specified non-opioid drug ormedication. Syndrome 2 (2016) included chief complaint or diagnosisterms mentioning heroin overdose and excluded suicide, withdrawal,detoxification, rehab, and addiction. Visits with mention of suicide,rehab, or addiction were identified during the evaluation process,resulting in the exclusion of these terms in the revised query.From January 1, 2015 to July 31, 2016, the number of visitscaptured by the revised syndrome definitions was compared to thenumber captured by the 2014 definitions. Correlation coefficientswere calculated using SAS 9.3.ResultsThe revised Syndrome 1 found 4296 fewer ED visits(29% decrease) for acute unintentional drug overdose betweenJanuary 1, 2015 and July 31, 2016 compared to the 2014 definition.Despite the drop in volume, the monthly trends were similar forthe 2014 and 2016 definitions (correlation coefficient = 0.95,p < 0.001). For the same time period, the revised Syndrome 2 definitionreturned 108 fewer visits (6% decrease) for acute unintentional heroinoverdose. The monthly trends were also similar for the 2014 and 2016definitions (correlation coefficient = 0.98, p < 0.001).ConclusionsBoth revised syndrome definitions improved specificity incapturing overdose visits as Syndrome 1 (2016) identified 29% fewervisits and Syndrome 2 (2016) identified 6% fewer visits found to beunrelated to the desired overdose criteria.When developing the revised syndrome definitions, VDH decidedto exclude non-acute drug-related visits. Terms such as addiction,detoxification, rehab, withdrawal, chronic pain, and constipation wereindicative of habitual drug use or abuse instead of acute overdose andwere thus excluded. In narrowing the scope of Syndrome 1, VDHalso identified and excluded visits for specified drug and medicationoverdose. Together, these expanded exclusion criteria resulted ingreater specificity with both updated syndromes.These revised syndrome definitions enable VDH to better trackopioid and heroin overdose trends in near real-time and overextended time periods which can be used to inform public healthactions. Limitations include the inconsistency of diagnosis codingamong syndromic data submitters, which may lead to geographicunderrepresentation of unintentional drug overdose visits based onthe location of health care systems. VDH will continue to evaluate andrefine these overdose syndrome definitions as this emerging healthissue evolves.

Comparison of Two Major Emergency Department-Based Free-Text Chief-Complaint Coding Systems

2004 ◽  
Author(s):  
Christina A. Mikosz ◽  
Julio Silva ◽  
S. Black ◽  
G. Gibbs ◽  
I. Cardenas
Keyword(s):  
Emergency Department ◽  
Chief Complaint ◽  
Free Text ◽  
Coding Systems

scholarly journals Systematic review and meta-analysis of the effectiveness of pharmacist-led medication reconciliation in the community after hospital discharge

2017 ◽  
Vol 27 (4) ◽  
pp. 308-320 ◽  
Author(s):  
Duncan McNab ◽  
Paul Bowie ◽  
Alastair Ross ◽  
Gordon MacWalter ◽  
Martin Ryan ◽  
...  
Keyword(s):  
Primary Care ◽  
Emergency Department ◽  
Hospital Discharge ◽  
Clinical Relevance ◽  
Medication Reconciliation ◽  
Meta Analysis ◽  
Free Text ◽  
Readmission Rates ◽  
Emergency Department Attendance ◽  
Free Text Search

BackgroundPharmacists’ completion of medication reconciliation in the community after hospital discharge is intended to reduce harm due to prescribed or omitted medication and increase healthcare efficiency, but the effectiveness of this approach is not clear. We systematically review the literature to evaluate intervention effectiveness in terms of discrepancy identification and resolution, clinical relevance of resolved discrepancies and healthcare utilisation, including readmission rates, emergency department attendance and primary care workload.MethodsThis is a systematic literature review and meta-analysis of extracted data. Medline, Cumulative Index to Nursing and Allied Health Literature (CINAHL), EMBASE, Allied and Complementary Medicine Database (AMED),Education Resources Information Center (ERIC), Scopus, NHS Evidence and the Cochrane databases were searched using a combination of medical subject heading terms and free-text search terms. Controlled studies evaluating pharmacist-led medication reconciliation in the community after hospital discharge were included. Study quality was appraised using the Critical Appraisal Skills Programme. Evidence was assessed through meta-analysis of readmission rates. Discrepancy identification rates, emergency department attendance and primary care workload were assessed narratively.ResultsFourteen studies were included, comprising five randomised controlled trials, six cohort studies and three pre–post intervention studies. Twelve studies had a moderate or high risk of bias. Increased identification and resolution of discrepancies was demonstrated in the four studies where this was evaluated. Reduction in clinically relevant discrepancies was reported in two studies. Meta-analysis did not demonstrate a significant reduction in readmission rate. There was no consistent evidence of reduction in emergency department attendance or primary care workload.ConclusionsPharmacists can identify and resolve discrepancies when completing medication reconciliation after hospital discharge, but patient outcome or care workload improvements were not consistently seen. Future research should examine the clinical relevance of discrepancies and potential benefits on reducing healthcare team workload.

scholarly journals Generating contextual embeddings for emergency department chief complaints

JAMIA Open ◽  
2020 ◽  
Vol 3 (2) ◽  
pp. 160-166
Author(s):  
David Chang ◽  
Woo Suk Hong ◽  
Richard Andrew Taylor
Keyword(s):  
Emergency Department ◽  
Short Term Memory ◽  
Language Model ◽  
Chief Complaint ◽  
Language Models ◽  
Free Text ◽  
Set Size ◽  
Chief Complaints ◽  
Ed Visits ◽  
Cutoff Threshold

Abstract Objective We learn contextual embeddings for emergency department (ED) chief complaints using Bidirectional Encoder Representations from Transformers (BERT), a state-of-the-art language model, to derive a compact and computationally useful representation for free-text chief complaints. Materials and methods Retrospective data on 2.1 million adult and pediatric ED visits was obtained from a large healthcare system covering the period of March 2013 to July 2019. A total of 355 497 (16.4%) visits from 65 737 (8.9%) patients were removed for absence of either a structured or unstructured chief complaint. To ensure adequate training set size, chief complaint labels that comprised less than 0.01%, or 1 in 10 000, of all visits were excluded. The cutoff threshold was incremented on a log scale to create seven datasets of decreasing sparsity. The classification task was to predict the provider-assigned label from the free-text chief complaint using BERT, with Long Short-Term Memory (LSTM) and Embeddings from Language Models (ELMo) as baselines. Performance was measured as the Top-k accuracy from k = 1:5 on a hold-out test set comprising 5% of the samples. The embedding for each free-text chief complaint was extracted as the final 768-dimensional layer of the BERT model and visualized using t-distributed stochastic neighbor embedding (t-SNE). Results The models achieved increasing performance with datasets of decreasing sparsity, with BERT outperforming both LSTM and ELMo. The BERT model yielded Top-1 accuracies of 0.65 and 0.69, Top-3 accuracies of 0.87 and 0.90, and Top-5 accuracies of 0.92 and 0.94 on datasets comprised of 434 and 188 labels, respectively. Visualization using t-SNE mapped the learned embeddings in a clinically meaningful way, with related concepts embedded close to each other and broader types of chief complaints clustered together. Discussion Despite the inherent noise in the chief complaint label space, the model was able to learn a rich representation of chief complaints and generate reasonable predictions of their labels. The learned embeddings accurately predict provider-assigned chief complaint labels and map semantically similar chief complaints to nearby points in vector space. Conclusion Such a model may be used to automatically map free-text chief complaints to structured fields and to assist the development of a standardized, data-driven ontology of chief complaints for healthcare institutions.

scholarly journals Enhanced Surveillance of Heat-Related Illness in Pinal County

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Dylan C. Kent ◽  
Rachel Z. Garcia ◽  
Samuel Packard ◽  
Graham Briggs ◽  
Clancey Hill ◽  
...  
Keyword(s):  
Public Health ◽  
Risk Factors ◽  
Emergency Department ◽  
Emergency Room ◽  
Medical Record ◽  
Hospital Discharge ◽  
Syndromic Surveillance ◽  
Extreme Heat ◽  
Discharge Data ◽  
Icd 10

ObjectiveUsing a syndromic surveillance system to understand the magnitude and risk factors related to heat-related illness (HRI) in Pinal County, AZ.IntroductionExtreme heat is a major cause of weather-related morbidity and mortality in the United States (US).1 HRI is the most frequent cause of environmental exposure-related injury treated in US emergency departments.2 More than 65,000 emergency room visits occur for acute HRI each summer nationwide.3 In Arizona, HRI accounts for an estimated 2,000 emergency room patients and 118 deaths each year.4 As heat-related illness becomes increasingly recognized as a public health issue, local health departments are tasked with building capacity to conduct enhanced surveillance of HRI in order to inform public health preparedness and response efforts. In Pinal County, understanding the magnitude and risk factors of HRI is important for informing prevention efforts as well as developing strategies to respond to extreme heat.MethodsTo gain a better understanding of the magnitude of HRI in Pinal County, historical cases were reviewed from hospital discharge data (HDD) from 2010-2016. Cases were included if the discharge record included any ICD codes consistent with HRI (ICD-9 codes 992 or ICD-10 codes T67 or X30) and if the patient’s county of residence was Pinal County. Recent HRI cases during the summer of 2017 were identified using the National Syndromic Surveillance Program BioSense Platform. The ESSENCE syndromic surveillance tool within the BioSense Platform includes data reported by local hospitals. This data can be used to detect abnormal activity for public health investigation. HRI cases were identified in ESSENCE based on ICD-10 codes and chief complaint terms according to a standardized algorithm developed by the Council of State and Territorial Epidemiologists.1 Both emergency department and admitted patients with a HRI were abstracted from HDD and ESSENCE. To assess HRI risk factors for the summer of 2017, a survey instrument was developed. Survey questions included the nature and location of the HRI incident, potential risk factors, and knowledge and awareness of HRI. Cases were identified in ESSENSE on a weekly basis from May 1, 2017-September 12, 2017, and follow up phone interviews were conducted with eligible cases. For HRI cases eligible for interview, three attempts were made to contact the patient by phone. Cases were excluded if the patient was incarcerated, deceased, or did not have a HRI upon medical record review. An exploratory analysis was performed for the data from HDD, ESSENCE, and interviews.ResultsPinal County Public Health Services District identified 1,321 HRI cases from 2010-2016, an average of 189 per year. Hospital discharge data suggest HRI cases are more likely to occur in males between the ages of 20-44 years old (27%). It is also notable that a sharp increase in HRI cases is observed each year in mid-to-late June, with an estimated 14% of annual cases occurring during the third week of June. Further analysis of HDD showed 31% of cases received medical treatment in Casa Grande in central Pinal County. Between May 1st and September 12th of 2017, 161 HRI cases were detected using ESSENCE. Of which 149 cases were determined to be HRI; 22 cases did not have contact information, and 4 cases were ineligible due to incarceration or death. A total of 31 HRI cases were interviewed out of the eligible 123 ESSENSE cases (25% response rate). Interview data indicated occupational exposure to extreme heat as a major risk factor for HRI. Additional risk factors reported during interviews included exposure to extreme heat while at home or traveling, although interview results are not representative due to a small sample size (n=31).ConclusionsSyndromic surveillance combined with interviews and a review of HDD provides an informative approach for monitoring and responding to HRI. Data suggest Pinal County should expect an increase in HRI cases by mid-June each year, typically coinciding with the first National Weather Service Extreme Heat Warning of the season. Preliminary results suggest that cases occur more frequently in working males ages 20-44 years old in occupations that expose workers to extreme heat conditions. Additional information is needed to assess risk factors for HRI among vulnerable populations in Pinal County who were not represented in this study, including individuals who are homeless, undocumented, elderly, or in correctional facilities. Future areas for improvement include improving the phone interview script to include English and Spanish language versions and performing medical record abstractions on all HRI cases. Enhanced syndromic surveillance is recommended to provide information on risk factors for HRI to inform prevention efforts in Pinal County.References1. Heat-Related Illness Syndrome Query: A Guidance Document For Implementing Heat-Related Illness Syndromic Surveillance in Public Health Practice. In: Epidemiologists CoSaT, ed. Vol 1.02016:1-12.2. Pillai SK, Noe RS, Murphy MW, et al. Heat illness: predictors of hospital admissions among emergency department visits-Georgia, 2002-2008. J Community Health. 2014;39(1):90-98.3. Centers for Disease Control and Prevention . Climate Change and Extreme Heat: What You Can Do to Prepare. 2016; Available from https://www.cdc.gov/climateandhealth/pubs/extreme-heat-guidebook.pdf4. Trends in Morbidity and Mortality from Exposure to Excessive Natural Heat in Arizona, 2012 report. In: Services ADoH, ed2012.

scholarly journals Using Emergency Department Data to Conduct Dog and Animal Bite Surveillance in New York City, 2003–2006

2012 ◽  
Vol 127 (2) ◽  
pp. 195-201 ◽  
Author(s):  
Brooke Bregman ◽  
Sally Slavinski
Keyword(s):  
Emergency Department ◽  
New York ◽  
New York City ◽  
York City ◽  
Surveillance System ◽  
Syndromic Surveillance ◽  
The United States ◽  
Chief Complaint ◽  
Free Text ◽  
Animal Bites

Objectives. Most animal bites in the United States are due to dogs, with approximately 4.7 million reports per year. Surveillance for dog and other animal bites requires a substantial investment of time and resources, and underreporting is common. We described the use and findings of electronic hospital emergency department (ED) chief complaint data to characterize patients and summarize trends in people treated for dog and other animal bites in New York City (NYC) EDs between 2003 and 2006. Methods. Retrospective data were obtained from the syndromic surveillance system at the NYC Department of Health and Mental Hygiene. We used a statistical program to identify chief complaint free-text fields as one of four categories of animal bites. We evaluated descriptive statistics and univariate associations on the available demographic data. The findings were also compared with data collected through the existing passive reporting animal bite surveillance system. Results. During the study period, more than 6,000 animal bite patient visits were recorded per year. The proportion of visits for animal bites did not appear to change over time. Dog bites accounted for more than 70% and cat bites accounted for 13% of animal bite patient visits. Demographic characteristics of patients were similar to those identified in NYC's passive surveillance system. Conclusions. Our findings suggest that the use of ED data offers a simple, less resource-intensive, and sustainable way of conducting animal bite surveillance and a novel use of syndromic surveillance data. However, it cannot replace traditional surveillance used to manage individual patients for potential rabies exposures.

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.

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