scholarly journals Improving Timeliness of Georgia Emergency Room Data

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Lance Ballester ◽  
Karl Soetebier ◽  
Bill Williamson ◽  
Rene Borroto ◽  
Jessica Grippo ◽  
...  
Keyword(s):  
Public Health ◽  
Emergency Room ◽  
Disease Control ◽  
Disease Surveillance ◽  
Syndromic Surveillance ◽  
Kernel Density ◽  
Meaningful Use ◽  
Patient Records ◽  
Data Analyses ◽  
Triage Time

ObjectiveTo explore the timeliness of emergency room surveillance data after the advent of federal Meaningful Use initiatives and determine potential areas for improvement.IntroductionTimeliness of emergency room (ER) data is arguably its strongest attribute in terms of its contribution to disease surveillance. Timely data analyses may improve the efficacy of prevention and control measures.There are a number of studies that have looked at timeliness prior to the advent of Meaningful Use, and these studies note that ER data were not fast enough for them to be useful in real time2,3. However, the change in messaging practices in the Meaningful Use era potentially changes this.Other studies have shown that changes in processes and protocol can dramatically improve timeliness1,4 and this motivates the current study of timeliness to identify processes that can be changed to improve timeliness.MethodsER data were collected from March 2017 through September 2017 from both the Georgia Department of Public Health’s (GDPH) State Electronic Notifiable Disease Surveillance System (SendSS) Syndromic Surveillance Module and the Centers for Disease Control and Prevention (CDC) National Syndromic Surveillance Program’s (NSSP) ESSENCE systems. Patients from hospitals missing 10 or more days of data, as well as patients with missing or invalid triage times, and all visits after August 1st were excluded in order to ensure data were representative of a “typical” time period and that a sufficient amount of time was given for visits to arrive from hospitals.The timeliness of individual records was determined in a number of different ways. All timeliness measurements were determined by subtracting the earlier time event from the later time of the event. The overall measure of timeliness is the time between the patient’s triage time and the data being present in the ESSENCE data system. In between, Georgia’s SendSS system receives and processes the data. This is illustrated in Figure 1. Due to the skewed nature of these measures, they were analyzed using medians and Gaussian kernel density plots.ResultsThe study in total included records from 118 Georgia hospitals, 14,203 data files and 1,897,501 patient records. Overall median timeliness of data from Triage Time to being available in SendSS for analyses was 33.62 hours (IQR=28.5), and in ESSENCE was 45.08 hours (IQR=37.05).The distributions of Triage Time of Day, Time Available in SendSS Staging, and Time Available in ESSENCE Analysis can be seen in Figure 2. Additionally, lines were added for when SendSS makes data available for its own analyses and when it sends data to ESSENCE. These latter lines represent places where the SendSS system itself could improve, and potential improved times were noted based on the kernel densities.Peak triage times for Georgia hospitals were between 10 am to 11 pm, shown in black. This represents the ideal timeliness if Hospitals sent their data immediately. However, data was all batched by Georgia hospitals and sent at different times of the day. The distribution of the time patient records arrived at SendSS staging was indicated in blue.During the period of this study, Georgia processed data into its SendSS system at 6:30am and 11:30am every day and sent data to the ESSENCE system at 1pm each day. These times are highlighted on the plot in green, and red respectively. New potential improved times, based on the kernel density of data being available in SendSS staging, are shown in the lighter shades of these colors at 8:30am and 12pm every day, while being sent to ESSENCE at 9am and 12:30pm to ensure time for data to be properly processed. These were determined to be optimal times for reducing lag in the data, however, may not be optimal for daily analysis.The purple line on the plot represents the times that data were available in ESSENCE’s system for analysis. This was notably delayed by a median 4.15 hours after the data was sent to ESSENCE on a typical day.ConclusionsA data driven approach to choosing processing times could improve timeliness of data analyses in the SendSS and ESSENCE systems. By conducting this type of analysis in an ongoing periodic basis, processing lag times can be kept at a minimum.1. Centers for Disease Control. Progress in improving state and local disease surveillance--United States, 2000-2005. MMWR Morbidity and mortality weekly report. 2005;54(33):822-825.2. Jajosky R, Groseclose S. Evaluation of reporting timeliness of public health surveillance systems for infectious diseases. BMC Public Health. 2004;4(1).3. Travers D, Barnett C, Ising A, Waller A. Timeliness of emergency department diagnoses for syndromic surveillance. AMIA Annual Symposium Proceedings. 2006;Vol. 2006:769.4. Ward M, Brandsema P, van Straten E, Bosman A. Electronic reporting improves timeliness and completeness of infectious disease notification, The Netherlands, 2003. Eurosurveillance. 2005;10(1):7-8.

scholarly journals Precision public health through clinic-based syndromic surveillance in communities

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Ta-Chien Chan ◽  
Yung-Chu Teng ◽  
Yen-Hua Chu ◽  
Tzu-Yu Lin
Keyword(s):  
Public Health ◽  
Surveillance System ◽  
Disease Surveillance ◽  
Syndromic Surveillance ◽  
Early Stage ◽  
Age Distribution ◽  
Local Health ◽  
Syndromic Surveillance System ◽  
Taipei City ◽  
Kaohsiung City

ObjectiveSentinel physician surveillance in the communities has played an important role in detecting early aberrations in epidemics. The traditional approach is to ask primary care physicians to actively report some diseases such as influenza-like illness (ILI), and hand, foot, and mouth disease (HFMD) to health authorities on a weekly basis. However, this is labor-intensive and time-consuming work. In this study, we try to set up an automatic sentinel surveillance system to detect 23 syndromic groups in the communites.IntroductionIn December 2009, Taiwan’s CDC stopped its sentinel physician surveillance system. Currently, infectious disease surveillance systems in Taiwan rely on not only the national notifiable disease surveillance system but also real-time outbreak and disease surveillance (RODS) from emergency rooms, and the outpatient and hospitalization surveillance system from National Health Insurance data. However, the timeliness of data exchange and the number of monitored syndromic groups are limited. The spatial resolution of monitoring units is also too coarse, at the city level. Those systems can capture the epidemic situation at the nationwide level, but have difficulty reflecting the real epidemic situation in communities in a timely manner. Based on past epidemic experience, daily and small area surveillance can detect early aberrations. In addition, emerging infectious diseases do not have typical symptoms at the early stage of an epidemic. Traditional disease-based reporting systems cannot capture this kind of signal. Therefore, we have set up a clinic-based surveillance system to monitor 23 kinds of syndromic groups. Through longitudinal surveillance and sensitive statistical models, the system can automatically remind medical practitioners of the epidemic situation of different syndromic groups, and will help them remain vigilant to susceptible patients. Local health departments can take action based on aberrations to prevent an epidemic from getting worse and to reduce the severity of the infected cases.MethodsWe collected data on 23 syndromic groups from participating clinics in Taipei City (in northern Taiwan) and Kaohsiung City (in southern Taiwan). The definitions of 21 of those syndromic groups with ICD-10 diagnoses were adopted from the International Society for Disease Surveillance (https://www.surveillancerepository.org/icd-10-cm-master-mapping-reference-table). The definitions of the other two syndromic groups, including dengue-like illness and enterovirus-like illness, were suggested by infectious disease and emergency medicine specialists.An enhanced sentinel surveillance system named “Sentinel plus” was designed for sentinel clinics and community hospitals. The system was designed with an interactive interface and statistical models for aberration detection. The data will be computed for different combinations of syndromic groups, age groups and gender groups. Every day, each participating clinic will automatically upload the data to the provider of the health information system (HIS) and then the data will be transferred to the research team.This study was approved by the committee of the Institutional Review Board (IRB) at Academia Sinica (AS-IRB02-106262, and AS-IRB02-107139). The databases we used were all stripped of identifying information and thus informed consent of participants was not required.ResultsThis system started to recruit the clinics in May 2018. As of August 2018, there are 89 clinics in Kaohsiung City and 33 clinics and seven community hospitals in Taipei City participating in Sentinel plus. The recruiting process is still ongoing. On average, the monitored volumes of outpatient visits in Kaohsiung City and Taipei City are 5,000 and 14,000 per day.Each clinic is provided one list informing them of the relative importance of syndromic groups, the age distribution of each syndromic group and a time-series chart of outpatient rates at their own clinic. In addition, they can also view the village-level risk map, with different alert colors. In this way, medical practitioners can know what’s going on, not only in their own clinics and communities but also in the surrounding communities.The Department of Health (Figure 1) can know the current increasing and decreasing trends of 23 syndromic groups by red and blue color, respectively. The spatial resolution has four levels including city, township, village and clinic. The map and bar chart represent the difference in outpatient rate between yesterday and the average for the past week. The line chart represents the daily outpatient rates for one selected syndromic group in the past seven days. The age distribution of each syndromic group and age-specific outpatient rates in different syndromic groups can be examined.ConclusionsSentinel plus is still at the early stage of development. The timeliness and the accuracy of the system will be evaluated by comparing with some syndromic groups in emergency rooms and the national notifiable disease surveillance system. The system is designed to assist with surveillance of not only infectious diseases but also some chronic diseases such as asthma. Integrating with external environmental data, Sentinel plus can alert public health workers to implement better intervention for the right population.References1. James W. Buehler AS, Marc Paladini, Paula Soper, Farzad Mostashari: Syndromic Surveillance Practice in the United States: Findings from a Survey of State, Territorial, and Selected Local Health Departments. Advances in Disease Surveillance 2008, 6(3).2. Ding Y, Fei Y, Xu B, Yang J, Yan W, Diwan VK, Sauerborn R, Dong H: Measuring costs of data collection at village clinics by village doctors for a syndromic surveillance system — a cross sectional survey from China. BMC Health Services Research 2015, 15:287.3. Kao JH, Chen CD, Tiger Li ZR, Chan TC, Tung TH, Chu YH, Cheng HY, Liu JW, Shih FY, Shu PY et al.: The Critical Role of Early Dengue Surveillance and Limitations of Clinical Reporting -- Implications for Non-Endemic Countries. PloS one 2016, 11(8):e0160230.4. Chan TC, Hu TH, Hwang JS: Daily forecast of dengue fever incidents for urban villages in a city. International Journal of Health Geographics 2015, 14:9.5. Chan TC, Teng YC, Hwang JS: Detection of influenza-like illness aberrations by directly monitoring Pearson residuals of fitted negative binomial regression models. BMC Public Health 2015, 15:168.6. Ma HT: Syndromic surveillance system for detecting enterovirus outbreaks evaluation and applications in public health. Taipei, Taiwan: National Taiwan University; 2007. 

scholarly journals When might Host Heterogeneity Drive the Evolution of Asymptomatic, Pandemic Coronaviruses?

2020 ◽  
Author(s):  
Kenichi W. Okamoto ◽  
Virakbott Ong ◽  
Robert G. Wallace ◽  
Rodrick Wallace ◽  
Luis Fernando Chaves
Keyword(s):  
Public Health ◽  
Infectious Diseases ◽  
Disease Control ◽  
Disease Surveillance ◽  
Fitness Landscape ◽  
Emerging Infectious Diseases ◽  
Disease Suppression ◽  
Contact Tracing ◽  
Pathogen Strains ◽  
Host Heterogeneity

For most emerging infectious diseases, including SARS-Coronavirus-2 (SARS-CoV-2), pharmaceutical intervensions such as drugs and vaccines are not available, and disease surveillance followed by isolating, contact-tracing and quarantining infectious individuals is critical for controlling outbreaks. These interventions often begin by identifying symptomatic individuals. However, by actively removing pathogen strains likely to be symptomatic, such interventions may inadvertently select for strains less likely to result in symptomatic infections. Additionally, the pathogen's fitness landscape is structured around a heterogeneous host pool. In particular, uneven surveillance efforts and distinct transmission risks across host classes can drastically alter selection pressures. Here we explore this interplay between evolution caused by disease control efforts, on the one hand, and host heterogeneity in the efficacy of public health interventions on the other, on the potential for a less symptomatic, but widespread, pathogen to evolve. We use an evolutionary epidemiology model parameterized for SARS-CoV-2, as the widespread potential for silent transmission by asymptomatic hosts has been hypothesized to account, in part, for its rapid global spread. We show that relying on symptoms-driven reporting for disease control ultimately shifts the pathogen's fitness landscape and can cause pandemics. We find such outcomes result when isolation and quarantine efforts are intense, but insufficient for suppression. We further show that when host removal depends on the prevalence of symptomatic infections, intense isolation efforts can select for the emergence and extensive spread of more asymptomatic strains. The severity of selection pressure on pathogens caused by these interventions likely lies somewhere between the extremes of no intervention and thoroughly successful eradication. Identifying the levels of public health responses that facilitate selection for asymptomatic pathogen strains is therefore critical for calibrating disease suppression and surveillance efforts and for sustainably managing emerging infectious diseases.

scholarly journals “That was then, this is now” improving public health syndromic surveillance baselines

2017 ◽  
Vol 9 (1) ◽  
Author(s):  
Roger Morbey ◽  
Alex J. Elliot ◽  
Paul Loveridge ◽  
Helen Hughes ◽  
Sally Harcourt ◽  
...  
Keyword(s):  
Public Health ◽  
Disease Surveillance ◽  
Syndromic Surveillance ◽  
Regression Models ◽  
Major Change ◽  
Surveillance Systems ◽  
Care Activity ◽  
Working Practices ◽  
Available Information ◽  
Traditional Surveillance

ObjectiveTo improve the ability of syndromic surveillance systems to detectunusual events.IntroductionSyndromic surveillance systems are used by Public Health England(PHE) to detect changes in health care activity that are indicative ofpotential threats to public health. By providing early warning andsituational awareness, these systems play a key role in supportinginfectious disease surveillance programmes, decision making andsupporting public health interventions.In order to improve the identification ofunusualactivity, wecreated new baselines to modelseasonally expectedactivity inthe absence of outbreaks or other incidents. Although historicaldata could be used to model seasonality, changes due to publichealth interventions or working practices affected comparability.Specific examples of these changes included a major change in theway telehealth services were provided in England and the rotavirusvaccination programme introduced in July 2013 that changed theseasonality of gastrointestinal consultations. Therefore, we needed toincorporate these temporal changes in our baselines.MethodsWe used negative binominal regression to model daily syndromicsurveillance, allowing for day of week and public holiday effects.To account for step changes in data caused by changes in healthcaresystem working practices or public health interventions we introducedspecific independent variables into the models. Finally, we smoothedthe regression models to provide short term forecasts of expectedtrends.The new baselines were applied to PHE’s four syndromicsurveillance systems for daily surveillance and public-facing weeklybulletins.ResultsWe replaced traditional surveillance baselines (based on simpleaverages of historical data) with the regression models for dailysurveillance of 53 syndromes across four syndromic surveillancesystems. The improved models captured current seasonal trends andmore closely reflected actual data outside of outbreaks.ConclusionsSyndromic surveillance baselines provide context forepidemiologists to make decisions about seasonal disease activity andemerging public health threats. The improved baselines developedhere showed whether current activity was consistent with expectedactivity, given all available information, and improved interpretationwhen trends diverged from expectations.

scholarly journals A Practitioner-Driven Research Agenda for Syndromic Surveillance

2017 ◽  
Vol 132 (1_suppl) ◽  
pp. 116S-126S ◽  
Author(s):  
Richard S. Hopkins ◽  
Catherine C. Tong ◽  
Howard S. Burkom ◽  
Judy E. Akkina ◽  
John Berezowski ◽  
...  
Keyword(s):  
Public Health ◽  
Disease Surveillance ◽  
Research Agenda ◽  
Syndromic Surveillance ◽  
Surveillance Network ◽  
Systems Research ◽  
Health Practitioners ◽  
Research Questions ◽  
Surveillance Practice ◽  
Public Health Practitioners

Syndromic surveillance has expanded since 2001 in both scope and geographic reach and has benefited from research studies adapted from numerous disciplines. The practice of syndromic surveillance continues to evolve rapidly. The International Society for Disease Surveillance solicited input from its global surveillance network on key research questions, with the goal of improving syndromic surveillance practice. A workgroup of syndromic surveillance subject matter experts was convened from February to June 2016 to review and categorize the proposed topics. The workgroup identified 12 topic areas in 4 syndromic surveillance categories: informatics, analytics, systems research, and communications. This article details the context of each topic and its implications for public health. This research agenda can help catalyze the research that public health practitioners identified as most important.

scholarly journals Syndromic infectious disease surveillance of refugees in Greece: a mixed methods analysis

2019 ◽  
Vol 29 (Supplement_4) ◽  
Author(s):  
J Graef ◽  
M Omar ◽  
A Abbara
Keyword(s):  
Public Health ◽  
Infectious Disease ◽  
User Experience ◽  
Disease Surveillance ◽  
Syndromic Surveillance ◽  
Respiratory Infections ◽  
National Level ◽  
Structural Barriers ◽  
Syndromic Surveillance System ◽  
Humanitarian Response

Abstract Background An estimated 1,174,140 refugees have migrated into Greece, a main entry point for refugees into Europe, since 2014. Their infectious disease profile is monitored by a national-level ad-hoc syndromic surveillance system in refugee-migrant reception centres. The utility of this system is explored to contribute evidence to and improve syndromic surveillance in European refugee responses. Methods Proportional morbidities, numbers of cases and signals, cases above expected numbers, of 14 syndromes are collated from weekly reports between 2016-2019, graphed and analysed in the context of the humanitarian response. Semi-structured key informant interviews are conducted and thematically analysed. Results Between 20.06.2016 and 17.02.2019, 36358 cases and 116 signals occurred. Public health responses resulted and there were no significant outbreaks. On average 5% of all consultations in centres were on infectious syndromes. Respiratory infections with fever (57%), gastroenteritis (22%), suspected scabies (13%) and rashes with fever (5%) were most commonly reported. Every week, between 68-100% of 25-58 participating centres completed reporting adequately. 6 informants reported on their syndromic system user experience. The system’s benefits, providing information and safeguarding refugees, outweighed harms. Data was timely and complete, but likely under-reported for common conditions. Poor living conditions and inter-agency coordination complicated reporting and public health responses. Conclusions Infectious burdens and trends were provided by the system and allowed for timely responses. Data quality was adequate. The system was valuable and feasible to informants. The set-up of the humanitarian response, inadequate ownership and poor coordination of authorities reduced the system’s utility. Key messages Syndromic surveillance is useful for monitoring refugee infectious health. Structural barriers need to be resolved to improve systems’ data and user experience.

The First 24 Hours of the World Trade Center Attacks of 2001—The Centers for Disease Control and Prevention Emergency Phase Response

2007 ◽  
Vol 22 (6) ◽  
pp. 473-477 ◽  
Author(s):  
Miguel A. Cruz ◽  
Ronald Burger ◽  
Mark Keim
Keyword(s):  
Public Health ◽  
Disease Control ◽  
World Trade ◽  
Disease Surveillance ◽  
Technical Assistance ◽  
World Trade Center ◽  
The World ◽  
Control And Prevention ◽  
Trade Center ◽  
Centers For Disease Control

AbstractOn 11 September 2001, terrorists hijacked two passenger planes and crashed them into the two towers of the World Trade Center (WTC) in New York City. These synchronized attacks were the largest act of terrorism ever committed on US soil. The impacts, fires, and subsequent collapse of the towers killed and injured thousands of people.Within minutes after the first plane crashed into the WTC, the Centers for Disease Control and Prevention (CDC) in Atlanta, Georgia, initiated one of the largest public health responses in its history. Staff of the CDC provided technical assistance on several key public health issues. During the acute phase of the event, CDC personnel assisted with: (1) assessing hospital capacity; (2) establishing injury and disease surveillance activities; (3) deploying emergency coordinators/liaisons to facilitate inter-agency coordination with the affected jurisdictions; and (4) arranging rapid delivery of emergency medical supplies, therapeutics, and personal protective equipment. This incident highlighted the need for adequate planning for all potential hazards and the importance of interagency and interdepartmental coordination in preparing for and responding to public health emergencies.

scholarly journals New Master Mapping Reference Table (MMRT) to Assist ICD-10 Transition for Syndromic Surveillance

2016 ◽  
Vol 8 (1) ◽  
Author(s):  
Brooke Evans ◽  
Peter Hicks ◽  
Julie A. Pavlin ◽  
Aaron Kite-Powell ◽  
Atar Baer ◽  
...  
Keyword(s):  
Public Health ◽  
Disease Surveillance ◽  
Syndromic Surveillance ◽  
Federal State ◽  
Local Public Health ◽  
Reference Table ◽  
Routine Surveillance ◽  
Icd 10 ◽  
State And Local ◽  
Council Of State

The International Society for Disease Surveillance (ISDS), in consultation with the CDC and the Council of State and Territorial Epidemiologists (CSTE), conducted a project to develop consensus-driven syndrome definitions based on ICD-10-CM codes. The goal was to have the newly created ICD-9-CM-to-ICD-10-CM mappings and corresponding syndromic definitions fully reviewed and vetted by the syndromic surveillance community, which relies on these codes for routine surveillance as well as for research purposes. The resulting tool, the Master Mapping Reference Table, may be leveraged by other federal, state, and local public health entities to better prepare and improve their surveillance, analytics, and reporting activities impacted by the ICD-10-CM transition.

scholarly journals Updates to the HL7 2.5.1 Implementation Guide for Syndromic Surveillance

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Emilie Lamb ◽  
Dave Trepanier ◽  
Shandy Dearth
Keyword(s):  
Public Health ◽  
Syndromic Surveillance ◽  
Review Process ◽  
Meaningful Use ◽  
Management Tool ◽  
Second Phase ◽  
Diverse Range ◽  
Online Portal ◽  
Wide Range ◽  
Icd 10

ObjectiveTo describe the latest revisions and modifications to the “HL7 2.5.1 Implementation Guide for Syndromic Surveillance” (formerly the PHIN Message Guide for Syndromic Surveillance) that were made based on community commentary and resolution of feedback from the HL7 balloting process. In addition, the next steps and future activities as the IG becomes an “HL7 Standard for Trial Use” will be highlighted.IntroductionIn 2011, the Centers for Disease Control and Prevention (CDC) released the PHIN Messaging Guide for Syndromic Surveillance v. 1. In the intervening years, new technological advancements including Electronic Health Record capabilities, as well as new epidemiological and Meaningful Use requirements have led to the periodic updating and revision of the Message Guide. These updates occurred through informal and semi-structured solicitation and in response to comments from across public health, governmental, academic, and EHR vendor stakeholders. Following the Message Guide v.2.0 release in 2015, CDC initiated a multi-year endeavor to update the Message Guide in a more systematic manner and released further updates via an Erratum and a technical document developed with the National Institute of Standards and Technology (NIST) to clarify validation policies and certification parameters. This trio of documents were consolidated into the Message Guide v.2.1 release and used to inform the development of the NIST Syndromic Surveillance Test Suite (http://hl7v2-ss-r2-testing.nist.gov/ss-r2/#/home), validate test cases, and develop a new rules-based IG built using NIST’s Implementation Guide Authoring and Management Tool (IGAMT).As part of a Cooperative Agreement (CoAg) initiated in 2017, CDC partnered with ISDS to build upon prior activities and renew efforts in engaging the Syndromic Surveillance Community of Practice for comment on the Message Guide. The goal of this CoAg is have the final product become an “HL7 Standard for Trial Use” following the second phase of formal HL7 balloting p in Fall 2018.MethodsISDS coordinated a multi-stakeholder working group to revisit the consolidated Message Guide, v.2.1 and collect structured comments via an online portal, which facilitated the documentation, tracking, and prioritization of comments for developing consensus and reconciliation and resolution when there were errors, conflicts, or differing perspectives for select specifications. Over 220 comments were received during the most recent review period via the HL& balloting process (April – June 2018) with sixteen elements captured for each comment, which included: Subject, Request Type, Clinical Venue Application, Submitter Name, IG Section #, Priority, Working and Final Resolution (Figure 1). The online portal was used to communicate with members of the Message Guide Workgroup to provide feedback directly to one another through a ‘conversation tab’. This became an important feature in teasing out underlying concerns and issues with a given comment across different local, state, and private sector partners (Figure 2). Some comments were able to be fully described and resolved using this feature. Following the HL7 balloting period, ISDS continued the weekly webinar-based review process to delve into specific issues in detail. Each week ISDS staff would lead the webinars structured around similar comment types (e.g. values sets, DG1 Segments, IN1 Segments, Conformance Statements, etc.). This leveraged the expertise of individuals and institutions with concerns revolving around a specific domain, messages segment, or specification described within the Message Guide. Comments for which consensus and resolution was achieved were “closed-out’ on the portal inventory and new assignments for review would be disseminated across the Message Guide Workgroup for consideration and discussion during the subsequent webinar.ResultsTo date this review process has identified and updated a wide-range of specification and requirements described within the Message Guide v.2.0. These include: specifications for persistent patient ID across venues of service, inclusion of the ICD-10-CM value set for diagnosis, removal of the ICD-9-CM requirement for testing and messages, modification of values such as pregnancy status, travel history, and medication list from “O” to “RE”, and the update of value sets and PHIN VADS references for FIPS, SNOmed, ICD-10-CM, Acuity, Patient Class, and Discharge Disposition.ConclusionsThe results of this multi-agency comment and review process will be synthesized and compiled by ISDS. The updated version of the Message Guide (re-branded to the HL7 V 2.5.1 Implementation Guide for Syndromic Surveillance) will go through a second round of review and commentary thru HL7 in Fall 2018.This systematic and structured review and documentation process has allowed for the synthetization and reconciliation of a wide range of disparate specifications, historical hold-overs, and requirements via the perspectives of a diverse range of public health partners. As this review process continues it is anticipated that the final HL7 balloted “Standard for Trial Use” IG 2.5 will represent a more refined and extensible product that can support syndromic surveillance activities across a wider and more diverse range of clinical venues, EHR implementations, and public health authorities.ISDS and CDC have recommended that future modifications to the Promoting Interoperability (PI) Programs (formerly Meaningful Use) reference and require the utilization of the revised Implication Guide for Certification. The HL7 2.5.1 Implementation Guide can be found: https://cdn.ymaws.com/www.healthsurveillance.org/resource/resmgr/docs/Group_Files/Message_Guide/IG_SyS_Release_1.pdf

scholarly journals Detection of a Salmonellosis Outbreak using Syndromic Surveillance in Georgia

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Rene Borroto ◽  
Jessica Pavlick ◽  
Karl Soetebier ◽  
Bill Williamson ◽  
Patrick Pitcher ◽  
...  
Keyword(s):  
Public Health ◽  
Disease Surveillance ◽  
Syndromic Surveillance ◽  
Food Poisoning ◽  
Cluster Detection ◽  
Free Text ◽  
Family Reunion ◽  
Text Query ◽  
Diarrhea Syndrome ◽  
Zip Code

ObjectiveDescribe how the Georgia Department of Public Health (DPH) used data from its State Electronic Notifiable Disease Surveillance System (SendSS) Syndromic Surveillance (SS) module for early detection of an outbreak of salmonellosis in Camden County, Georgia.IntroductionEvidence about the value of syndromic surveillance data for outbreak detection is limited (1). In July 2018, a salmonellosis outbreak occurred following a family reunion of 300 persons held in Camden County, Georgia, where one meal was served on 7/27/2018 and on 7/28/2018.MethodsSendSS-SS and SAS were used for cluster detection of Emergency Department (ED) patients with similar Chief Complaint (CC), Triage Notes (TN), or Discharge Diagnoses (DDx) by facility, time of ED visit, and zip code / county of residence. A SAS-based free-text query related to food poisoning in the CC and DDx fields was also performed on a daily basis. County- and hospital-specific charting of the Diarrhea syndrome was also conducted in SendSS-SS, whereas county- and zip code-specific charting of the same syndrome were done in both SendSS-SS and SAS (2).ResultsOn Sunday July 29th, 2018, three children and three adults were seen within 18 hours at the ED of Hospital A in Camden County, Georgia. All patients complained of diarrhea, vomiting, and food poisoning, after a large family reunion that had been held the day before. This early cluster was detected by the SAS-based free-text query of ‘food poisoning’ and the SAS-based cluster detection tool for patients with Diarrhea syndrome. The District Epidemiologists (DE) in the Coastal Health District were notified on Monday, July 30th, 2018. One-year high daily spikes of the Diarrhea syndrome occurred from July 29th to July 31st, 2018 in a local hospital ED (Fig 1), Camden County, and zip code 31548. Two HIPAA-compliant line lists with a total of 27 patients seen at EDs were emailed to the DEs to support active case finding. No further spikes of the Diarrhea syndrome were detected in Camden County during the 2-week period after the 3-day spike.ConclusionsSyndromic surveillance was a useful surveillance tool for early detection of a salmonellosis outbreak, helping with the active search for outbreak cases, tracking the peak of the outbreak, and assuring that no further spikes were occurring.References1.R Hopkins, C Tong, H Burkom, et al. A Practitioner-Driven Research Agenda for Syndromic Surveillance. Public Health Reports 2017; 132(Supplement1): 116S-126S.2. G Zhang, A Llau, J Suarez, E O'Connell, E Rico, R Borroto, F Leguen. Using ESSENCE to Track a Gastrointestinal Outbreak in a Homeless Shelter in Miami-Dade County, 2008. Advances in Disease Surveillance. 2008; 5:139. 

scholarly journals Emergency department syndromic surveillance systems: a systematic review

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Helen E. Hughes ◽  
Obaghe Edeghere ◽  
Sarah J. O’Brien ◽  
Roberto Vivancos ◽  
Alex J. Elliot
Keyword(s):  
Public Health ◽  
Emergency Department ◽  
Disease Surveillance ◽  
Syndromic Surveillance ◽  
Best Practice ◽  
Public Health Practice ◽  
Healthcare Services ◽  
Urgent Care ◽  
Severe Illness ◽  
Surveillance Systems

Abstract Background Syndromic surveillance provides public health intelligence to aid in early warning and monitoring of public health impacts (e.g. seasonal influenza), or reassurance when an impact has not occurred. Using information collected during routine patient care, syndromic surveillance can be based on signs/symptoms/preliminary diagnoses. This approach makes syndromic surveillance much timelier than surveillance requiring laboratory confirmed diagnoses. The provision of healthcare services and patient access to them varies globally. However, emergency departments (EDs) exist worldwide, providing unscheduled urgent care to people in acute need. This provision of care makes ED syndromic surveillance (EDSyS) a potentially valuable tool for public health surveillance internationally. The objective of this study was to identify and describe the key characteristics of EDSyS systems that have been established and used globally. Methods We systematically reviewed studies published in peer review journals and presented at International Society of Infectious Disease Surveillance conferences (up to and including 2017) to identify EDSyS systems which have been created and used for public health purposes. Search criteria developed to identify “emergency department” and “syndromic surveillance” were applied to NICE healthcare, Global Health and Scopus databases. Results In total, 559 studies were identified as eligible for inclusion in the review, comprising 136 journal articles and 423 conference abstracts/papers. From these studies we identified 115 EDSyS systems in 15 different countries/territories across North America, Europe, Asia and Australasia. Systems ranged from local surveillance based on a single ED, to comprehensive national systems. National EDSyS systems were identified in 8 countries/territories: 2 reported inclusion of ≥85% of ED visits nationally (France and Taiwan). Conclusions EDSyS provides a valuable tool for the identification and monitoring of trends in severe illness. Technological advances, particularly in the emergency care patient record, have enabled the evolution of EDSyS over time. EDSyS reporting has become closer to ‘real-time’, with automated, secure electronic extraction and analysis possible on a daily, or more frequent basis. The dissemination of methods employed and evidence of successful application to public health practice should be encouraged to support learning from best practice, enabling future improvement, harmonisation and collaboration between systems in future. Prospero number CRD42017069150.

Sign in / Sign up

Export Citation Format

Share Document