scholarly journals Impact of Patient Self-Registration in Emergency Departments on Syndromic Surveillance Data

2015 ◽  
Vol 7 (1) ◽  
Author(s):  
Melinda C. Thomas ◽  
David Atrubin ◽  
Janet J. Hamilton
Keyword(s):  
Best Practices ◽  
Emergency Departments ◽  
Surveillance System ◽  
Syndromic Surveillance ◽  
Surveillance Data ◽  
Hospital Emergency ◽  
Syndromic Surveillance System ◽  
Hospital Emergency Departments

This session discusses an assessment of the effect of patient self-registration methods in hospital emergency departments on data in a syndromic surveillance system and provides suggestions for best practices in identifying and analyzing these data.

scholarly journals Spatial analysis of SS population coverage based on emergency regional healthcare

2017 ◽  
Vol 9 (1) ◽  
Author(s):  
Esra Morvan ◽  
Anne Bernadou ◽  
Ludivine Gautier ◽  
Yassungo Silue ◽  
Dominique Jeannel
Keyword(s):  
Spatial Analysis ◽  
General Practitioners ◽  
Emergency Care ◽  
Emergency Departments ◽  
Syndromic Surveillance ◽  
Influential Factor ◽  
Population Coverage ◽  
Hospital Emergency ◽  
Syndromic Surveillance System ◽  
Hospital Emergency Departments

ObjectiveTo analyse population coverage of syndromic surveillance(SS)based on emergency care data by studying i)the attractiveness ofrespectively SOS Médecins (Emergency care general practitioners)and Hospital emergency departments in the Centre-Val de Loireregion and ii) the contribution of ecological deprivation factors inemergency access to healthcare.IntroductionSOS Médecins France (SOS Med) is the first private and permanentnetwork of general practitioners providing emergency care in France.Besides Hospital emergency departments (HED), SOS Med istherefore a major source of data for detecting and measuring near-real-time health phenomena. The emergency services provided by theSOS Med have been subject to important changes in the recent years.Their services are enriched by a medical consultation center togetherwith extended working hours. Besides, the south of the region ismarkedly affected by a declining number of medical practitionersThis study was conducted to analyze the regional population coverageof emergency healthcare data provided by HED and SOS Med tothe French syndromic surveillance system (SurSaUD®) takinginto account distance, health care offer, demographic factors andecological deprivation factors.MethodsAn analysis of the activities and geographic attraction was carriedout based on the data respectively provided by the three regional SOSMed and three HED (Bourges, Orléans and Tours). Quasi-Poissonregression modelling was used to identify the factors influencing theattractiveness of each organization. Next, the findings were refinedthrough spatial analysis of the attractiveness of HED and SOS Medand analysis of the contribution of deprivation based on socio-economical and healthcare facilities ecological indexes.ResultsIn terms of age group, children under 2 years required the largestservice consultations as well as seniors over 75 who sought moreemergency visits at home. The SOS Med were almost always active inurban areas and at least once in two due to continuity of care. So theyare an efficient source of general medical care given present workhours. Distance as an influential factor may explain the differencesin attraction to the support type. The extent of the attraction appearsin 36% SOS Med Bourges and 14% for SOS Med Orleans. Addthe extent of attraction for SOS, remote consultation for SOS Medassociations are a good use of care in general practice in present workhours scheme.In terms of monitoring of epidemics, we note that the SOSMédecins associations are most active in winter, particularly duringthe seasonal epidemics of influenza. This can be explained by the factof patient referrals during calls. The most serious cases are redirectedto the ED and cases of general medicine to the SOS Médecins.It is also important to note that the attraction of ED ofCHR Orléanscovers more or less important a large part of the regional territory,which is not visible to the ED ofCH Bourges. It should neverthelessbe noted that theCHR Orleansa larger bed capacity than theCH Bourges.ConclusionsThis research has analysed the changes taking place in the SOSmédecins associations in the Centre-Val de Loire region. Findingsshows that these associations help ensure access to general medicalcare in a context of strongly reduced medical demography althoughwith an uneven, primarily urban, geographical coverage. Withbetter knowledge of the geographic span and sources and types ofemergency care provision, further research can be undertaken tofurther refine and interpret the data.

scholarly journals Syndromic Surveillance of Acute Liver Failure in Emergency Departments (France, 2010-2012)

2014 ◽  
Vol 6 (1) ◽  
Author(s):  
Corinne Pioche ◽  
Christine Larsen ◽  
Céline Caserio-Schonemann ◽  
Vanina Héraud-Bousquet
Keyword(s):  
Liver Failure ◽  
Acute Liver Failure ◽  
Emergency Departments ◽  
Surveillance System ◽  
Syndromic Surveillance ◽  
Surveillance Data ◽  
Syndromic Surveillance System ◽  
Icd10 Code

Our objectives were to explore the relevance of emergency departments' (ED) data, collected daily through the French syndromic surveillance system (414 EDs, 65% attendances), to describe the characteristics of patients with acute liver failure (ALF). Data corresponding to ICD10 codes related to hepatitis diagnosis that include ALF ICD10 code (K720) were extracted and analyzed. During 2010-2012, 246 730 attendances with hepatitis were recorded of which 2 475 (1%) were linked to ALF. Patients with ALF were male (60%), their median age was 55 years. This study shows the relevance of French syndromic surveillance data to assess the burden of ALF.

Use of a syndromic surveillance system to describe the trend in cycling-related presentations to emergency departments in Sydney

2015 ◽  
Vol 27 (4) ◽  
pp. 343-347 ◽  
Author(s):  
Michael M Dinh ◽  
Christopher Kastelein ◽  
Kendall J Bein ◽  
Timothy C Green ◽  
Tanya Bautovich ◽  
...  
Keyword(s):  
Emergency Departments ◽  
Surveillance System ◽  
Syndromic Surveillance ◽  
Syndromic Surveillance System

scholarly journals Validation of Syndromic ILI Data for Use in CDC’s ILINet Surveillance, Pennsylvania

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Sameh W Boktor ◽  
Kristen Waller ◽  
Lenee Blanton ◽  
Krista Kniss
Keyword(s):  
Sore Throat ◽  
Surveillance System ◽  
Syndromic Surveillance ◽  
Correlation Coefficients ◽  
Chief Complaint ◽  
The State ◽  
Surveillance Data ◽  
Syndromic Surveillance System ◽  
Ed Visits ◽  
Number Of Visits

Objective: Discuss use of syndromic surveillance as a source for the state’s ILI/Influenza surveillanceDiscuss reliability of syndromic data and methods to address problems caused by data outliers and inconsistencies.Introduction: ILINet is a CDC program that has been used for years for influenza-like illness (ILI) surveillance, using a network of outpatient providers who volunteer to track and report weekly the number of visits due to ILI and the total number of visits to their practice. Pennsylvania has a network of 95 providers and urgent care clinics that submit data to ILINet. However, ongoing challenges in recruiting and retaining providers, and inconsistent weekly reporting are barriers to receiving accurate, representative, and timely ILI surveillance data year-round. Syndromic surveillance data have been used to enhance outpatient ILI surveillance in a number of jurisdictions, including Pennsylvania. At present, 156 hospitals, or 90% of all Pennsylvania hospitals with emergency departments (EDs), send chief complaint and other information on their ED visits to the Department of Health’s (PADOH) syndromic surveillance system. PADOH evaluated the consistency and reliability of ILI syndromic data as compared to ILINet data, to confirm that syndromic data were suitable for use in ILINet.Methods: Pennsylvania ILINet data from the past 6 influenza seasons (2011-2012 to 2016-2017, or 314 weeks of data) were downloaded from the CDC’s ILINet website. The statewide weekly percent of visits due to ILI in ILINet was used as the standard for comparisons. For syndromic surveillance, PADOH uses the Epicenter platform hosted by Health Monitoring Systems (HMS); visit-level data are also stored in SAS datasets at PADOH, and HMS forwards a subset of data to the National Syndromic Surveillance System Program. Using syndromic data from the same time period, the proportion of weeks with no syndromic data available was calculated for each facility. A state-developed ILI algorithm (very similar to the 2016 algorithm developed by the ISDS Syndrome Definitions Workgroup) was applied to ED visit chief complaint data to identify visits likely to be due to ILI. The algorithm flags the ER visit as ILI if chief complaint has any combinations of words for flu or fever plus either cough and sore throat or fever and both cough or sore throat . The percent of ED visits due to ILI per the syndromic algorithm (ILIsyn) was calculated for each week by hospital and state-wide. Facility ILIsyn trends were compared to the State level percent ILI data from ILINet by visually examining plots and by calculating Pearson correlation coefficients. Facilities that had >=15 weeks where ILIsyn differed from percent ILI in ILINet by more than 5% were considered to be poorly correlated.Results: A total of 156 hospitals were evaluated in the study. Twenty of the hospitals were excluded because they did not have syndromic data for at least 50% of the weeks in the study period, and an additional 20 were excluded because they had not agreed to have data forwarded to CDC. Of the remaining 116 facilities, individual facility correlation coefficients between ILIsyn and ILINet trends ranged from 0.03 to 0.82 (examples are in Figure 1). Twenty-four hospitals (20.7%) were determined to be poorly correlated. When data from the remaining 92 hospitals were combined, the state ILINet and state-wide ILIsyn trends were strongly correlated statistically and graphically (r=0.82, p <0.0001, Figure 2). Syndromic data from these 92 facilities were deemed acceptable for inclusion in ILINet. Conclusions: Syndromic surveillance data are a valuable source for ILI surveillance. However, evaluation at the hospital-specific level revealed that useful information is not obtained from all facilities. This project demonstrated that validation of data at the facility level is crucial to obtaining reliable and meaningful information. More work is needed to understand which factors distinguish well-correlated from poorly-correlated facilities, and how to improve the quality of information obtained from poorly-correlated facilities.

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