scholarly journals Front-Line Emergency Department Clinician Acceptability and Use of a Prototype Real-Time Cloud-Based Influenza Surveillance System

2021 ◽  
Vol 9 ◽  
Author(s):  
Richard E. Rothman ◽  
Yu-Hsiang Hsieh ◽  
Anna DuVal ◽  
David A. Talan ◽  
Gregory J. Moran ◽  
...  

Objectives: To assess emergency department (ED) clinicians' perceptions of a novel real-time influenza surveillance system using a pre- and post-implementation structured survey.Methods: We created and implemented a laboratory-based real-time influenza surveillance system at two EDs at the beginning of the 2013-2014 influenza season. Patients with acute respiratory illness were tested for influenza using rapid PCR-based Cepheid Xpert Flu assay. Results were instantaneously uploaded to a cloud-based data aggregation system made available to clinicians via a web-based dashboard. Clinicians received bimonthly email updates summating year-to-date results. Clinicians were surveyed prior to, and after the influenza season, to assess their views regarding acceptability and utility of the surveillance system data which were shared via dashboard and email updates.Results: The pre-implementation survey revealed that the majority (82%) of the 151 ED clinicians responded that they “sporadically” or “don't,” actively seek influenza-related information during the season. However, most (75%) reported that they would find additional information regarding influenza prevalence useful. Following implementation, there was an overall increase in the frequency of clinician self-reporting increased access to surveillance information from 50 to 63%, with the majority (75%) indicating that the surveillance emails impacted their general awareness of influenza. Clinicians reported that the additional real-time surveillance data impacted their testing (65%) and treatment (51%) practices.Conclusions: The majority of ED clinicians found surveillance data useful and indicated the additional information impacted their clinical practice. Accurate and timely surveillance information, distributed in a provider-friendly format could impact ED clinician management of patients with suspected influenza.

2015 ◽  
Vol 7 (1) ◽  
Author(s):  
Andrea F. Dugas ◽  
Howard Burkom ◽  
Anna L. DuVal ◽  
Richard Rothman

We provided emergency department providers with a real-time laboratory-based influenza surveillance tool, and evaluated the utility and acceptability of the surveillance information using provider surveys. The majority of emergency department providers found the surveillance data useful and indicated the additional information impacted their clinical decision making regarding influenza testing and treatment.


2018 ◽  
Vol 5 (suppl_1) ◽  
pp. S2-S3 ◽  
Author(s):  
Keith M Ramsey ◽  
M Kathy Cochran ◽  
William Cleve

Abstract Background Vidant Health is an 8-hospital, 1,542-bed system (including the 908-bed teaching hospital for The Brody School of Medicine at East Carolina University) with over 12,000 employees, and uses a sick employee online log (SEOL) to track illnesses among employees. Influenza-like illness (ILI) surveillance is collected from sentinel sites across the state of North Carolina (NC) by the Department of Health. Our goals were to determine the utility of the SEOL to monitor ILI among employees and to compare trends with the NC ILI-system for Influenza surveillance. Methods When an employee calls in sick, symptoms for ILI in both the SEOL system and NC ILI-system include fever plus cough and/or sore throat. SEOL is an internet-based system, so information is collected and analyzed in real time. The number of sick hospital employees with influenza-like illness (ILI) per week during the 2017–2018 Influenza season was compared both to those employees reporting “Flu” and to the NC ILI numbers from the sentinel sites using MS Excel. Results The data analyzed was from October 2017 to April 2018. First, while lesser actual numbers of sick employees reported “Flu,” there was a correlation value of 0.93 between those reporting “Flu” and those reporting ILI symptoms (see Figure 1). Secondly, the SEOL results are available daily, while the NC ILI data are reported 7–12 days from entry; however, the peaks in ILI paralleled those of the peaks in SEOL data for employees reporting symptoms of ILI (see Figure 2) with a correlation value of 0.79 between the two. Finally, there were no breaks in confidentiality for those employees utilizing the SEOL. Conclusion The SEOL provided a real-time tool to monitor employee illnesses due to ILI during influenza season, and without the lag time of the ILI-surveillance by the state. This system maintained confidentiality with a convenient method for data entry. These findings conclude that the SEOL system data correlated positively with the state ILI data and provided an early detection system for the appearance of influenza among our employees. Disclosures All authors: No reported disclosures.


2019 ◽  
Vol 147 ◽  
Author(s):  
Jessica Y. Wong ◽  
Edward Goldstein ◽  
Vicky J. Fang ◽  
Benjamin J. Cowling ◽  
Peng Wu

Abstract Statistical models are commonly employed in the estimation of influenza-associated excess mortality that, due to various reasons, is often underestimated by laboratory-confirmed influenza deaths reported by healthcare facilities. However, methodology for timely and reliable estimation of that impact remains limited because of the delay in mortality data reporting. We explored real-time estimation of influenza-associated excess mortality by types/subtypes in each year between 2012 and 2018 in Hong Kong using linear regression models fitted to historical mortality and influenza surveillance data. We could predict that during the winter of 2017/2018, there were ~634 (95% confidence interval (CI): (190, 1033)) influenza-associated excess all-cause deaths in Hong Kong in population ⩾18 years, compared to 259 reported laboratory-confirmed deaths. We estimated that influenza was associated with substantial excess deaths in older adults, suggesting the implementation of control measures, such as administration of antivirals and vaccination, in that age group. The approach that we developed appears to provide robust real-time estimates of the impact of influenza circulation and complement surveillance data on laboratory-confirmed deaths. These results improve our understanding of the impact of influenza epidemics and provide a practical approach for a timely estimation of the mortality burden of influenza circulation during an ongoing epidemic.


2019 ◽  
Vol 58 (5) ◽  
pp. 571-577
Author(s):  
Patrick T. Reeves ◽  
Jayasree Krishnamurthy ◽  
Eric A. Pasman ◽  
Cade M. Nylund

During the observance of Christmas, many families display decorations, which increases the risk of unfettered access and subsequent ingestion of small objects by children in the home. Our aim was to characterize the epidemiology of Christmas foreign body ingestion (CFBI) by children. National Electronic Injury Surveillance System data from 1997 to 2015 were obtained for children aged 0 to 17 years who presented to United States Emergency Departments matching “ingested” for “artificial Christmas trees”; “Christmas tree lights”; “Christmas tree stands or supports”; “Christmas decorations, nonelectric”; and “Christmas decorations, electric” (excluding tree lights). An estimated 22 224 children (95% confidence Interval = 18 107-26 340) presented to the emergency department for CFBI over the study period. Children aged 2 years and younger ingested Christmas objects most frequently ( P < .001). CFBI visits demonstrated a seasonal trend ( P < .001). Christmas decoration ingestions are a frequent reason for children to present to the ED, which require dedicated awareness for appropriate diagnosis and care.


Author(s):  
Andrea Dugas ◽  
Howard Burkom ◽  
Richard Rothman

In order to provide real-time access to influenza test results, we created a laboratory-based surveillance system which automatically uploaded influenza test results from a rapid PCR-based influenza test, Xpert Flu, and the associated testing times and locations. On-site, type-specific results were available to physicians and uploaded for public health awareness within 100 minutes of patient nasopharyngeal swab. Expansion of this real-time capability to sentinel facilities could improve both local and national surveillance and response, reducing the need for syndromic influenza surveillance.


2018 ◽  
Author(s):  
Aye Moa ◽  
David Muscatello ◽  
Abrar Chughtai ◽  
Xin Chen ◽  
C Raina MacIntyre

BACKGROUND Influenza causes serious illness requiring annual health system surge capacity, yet annual seasonal variation makes it difficult to forecast and plan for the severity of an upcoming season. Research shows that hospital and health system stakeholders indicated a preference of forecasting tools that are easy to use and understand, to assist with surge capacity planning for influenza. OBJECTIVE This study aimed to develop a simple risk prediction tool, Flucast, to predict the severity of an emerging influenza season. METHODS Study data were obtained from the National Notifiable Diseases Surveillance System and Australian Influenza Surveillance Reports, Department of Health, Australia. We tested Flucast using retrospective seasonal data for eleven Australian influenza seasons. We compared five different models, using parameters known early in the season and which may be associated with the severity of the season. To calibrate the tool, the resulting estimates of seasonal severity were validated against independent reports of influenza-attributable morbidity and mortality. A model with highest predictive accuracy against retrospective seasonal activity was chosen as a best fit model to develop the Flucast tool. The tool was prospectively tested against the emerging 2018 influenza season. RESULTS The Flucast tool predicted the severity of all retrospectively studied years correctly for influenza seasonal activity in Australia. For 2018, the tool provided a reliable early prediction of severe seasonal influenza with the use of real-time data. The tool meets stakeholder preferences for simplicity and ease of use to assist with surge capacity planning. CONCLUSIONS The Flucast tool may be useful to inform future health system influenza preparedness planning, surge capacity and intervention programs in real time and can be adapted for different settings and geographic locations. CLINICALTRIAL NA


2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Jill DeWitt TenHacken

ObjectiveDuring this session, participants will be able to understand how Harris County Public Health utilized data to make informed decisions on how to combat the influenza season.IntroductionThe 2017 – 2018 influenza season was classified by the Centers for Disease Control and Prevention (CDC) as ‘high severity’ across all age groups. Furthermore, CDC noted that this was the first year to be categorized as such, with the highest peak percentage of influenza-like-illnesses (ILI), since 2009. In Harris County alone, there were 2,665 positive flu tests reported in comparison to the previous season at 1,395 positive tests. In response to the severity of this year’s flu season, Harris County Public Health (HCPH) collaborated across the department to deploy five pop up influenza vaccination events utilizing our Mobile Fleets open to the general public.HCPH epidemiologists are able to collect influenza data from multiple systems and compile it into useful reports/tools. These data include latitudinal and longitudinal data, allowing us to create highly localized maps of where influenza has had impacted communities the hardest. This granular data allowed HCPH to target 5 areas with our Mobile Fleet that had a) high levels of influenza and b) generally limited healthcare/public health infrastructure.Our Mobile Fleet is made up of 8 different Recreational Vehicles that have been retrofitted to offer various public health services including: immunizations, medical visits, dental visits, pet adoptions, mosquito and vector control education, and a fresh food market. The Fleet allows HCPH to offer a full menu of public health services anywhere within the County. While our efforts for this abstract were focused on controlling the influenza outbreak, we leveraged the opportunity to engage with the public on multiple issues such as environmental, veterinary, mosquito control, dental health, and accessible healthy food options.MethodsAs positive flu reports mounted, our epidemiology program provided surveillance data of influenza and ILI in Harris County. Data was obtained through multiple sources including: National Electronic Disease Surveillance System (NEDSS), which includes electronic laboratory reporting; National Respiratory Enteric Virus Surveillance System (NREVSS), which includes all flu tests done in laboratories in Houston; and last, the Flu Portal, which school nurses in Harris County upload school absenteeism rates due to ILI. Once collected and compiled, our Geographic Information System (GIS) team used the data to generate spatial maps of Harris County illustrating the disproportionally high rates. Specifically, our GIS team was able to utilize ArcGIS, and cross layer them with the flu data provided from the epidemiologists. Utilizing these maps, HCPH leadership mobilized the preparedness team to lead a data driven response in five different zip codes throughout the county to hold the influenza vaccination events.ResultsThe Mobile Fleet was operational on five separate dates in five separate zip codes during February and March of 2018. Overall, 477 individuals were provided the influenza vaccine. Of those 477, 304 were 18 years or older, with 173 being under 18 years of age.ConclusionsHaving timely and actionable data is an essential first step to understand and stop an outbreak of any size. However, surveillance data alone won't prevent an outbreak from spreading. That data must be married to effective public health action. Our Mobile Fleet is able to deliver precision public health services by targeting communities most affected and vulnerable to the spread of disease. As surveillance geospatial data becomes more granular so too must our public health service delivery modes become more precise and targeted. 


2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Phunlerd Piyaraj ◽  
Nira Pet-hoi ◽  
Chaiyos Kunanusont ◽  
Supanee Sangiamsak ◽  
Somsak Wankijcharoen ◽  
...  

Objective: We describe the Bangkok Dusit Medical Services Surveillance System (BDMS-SS) and use of surveillance efforts for influenza as an example of surveillance capability in near real-time among a network of 20 hospitals in the Bangkok Dusit Medical Services group (BDMS).Introduction: Influenza is one of the significant causes of morbidity and mortality globally. Previous studies have demonstrated the benefit of laboratory surveillance and its capability to accurately detect influenza outbreaks earlier than syndromic surveillance.1-3 Current laboratory surveillance has an approximately 4-week lag due to laboratory test turn-around time, data collection and data analysis. As part of strengthening influenza virus surveillance in response to the 2009 influenza A (H1N1) pandemic, the real-time laboratory-based influenza surveillance system, the Bangkok Dusit Medical Services Surveillance System (BDMS-SS), was developed in 2010 by the Bangkok Health Research Center (BHRC). The primary objective of the BDMS-SS is to alert relevant stakeholders on the incidence trends of the influenza virus. Type-specific results along with patient demographic and geographic information were available to physicians and uploaded for public health awareness within 24 hours after patient nasopharyngeal swab was collected. This system advances early warning and supports better decision making during infectious disease events.2 The BDMS-SS operates all year round collecting results of all routinely tested respiratory clinical samples from participating hospitals from the largest group of private hospitals in Thailand.Methods: The BDMS has a comprehensive network of laboratory, epidemiologic, and early warning surveillance systems which represents the largest body of information from private hospitals across Thailand. Hospitals and clinical laboratories have deployed automatic reporting mechanisms since 2010 and have effectively improved timeliness of laboratory data reporting. In April 2017, the capacity of near real-time influenza surveillance in BDMS was found to have a demonstrated and sustainable capability.Results: From October 2010 to April 2017, a total of 482,789 subjects were tested and 86,110 (17.8%) cases of influenza were identified. Of those who tested positive for influenza they were aged <2 years old (4.6%), 2-4 year old (10.9%), 5-14 years old (29.8%), 15-49 years old (41.9%), 50-64 years old (8.3%) and >65 years old (3.7%). Approximately 50% of subjects were male and female. Of these, 40,552 (47.0%) were influenza type B, 31,412 (36.4%) were influenza A unspecified subtype, 6,181 (7.2%) were influenza A H1N1, 4,001 (4.6%) were influenza A H3N2, 3,835 (4.4%) were influenza A seasonal and 196 (0.4%) were respiratory syncytial virus (RSV).The number of influenza-positive specimens reported by the real-time influenza surveillance system were from week 40, 2015 to week 39, 2016. A total of 117,867 subjects were tested and 17,572 (14.91%) cases tested positive for the influenza virus (Figure 1). Based on the long-term monitoring of collected information, this system can delineate the epidemiologic pattern of circulating viruses in near real-time manner, which clearly shows annual peaks in winter dominated by influenza subtype B in 2015-1016 season. This surveillance system helps to provide near real-time reporting, enabling rapid implementation of control measures for influenza outbreaks.Conclusions: This surveillance system was the first real-time, daily reporting surveillance system to report on the largest data base of private hospitals in Thailand and provides timely reports and feedback to all stakeholders. It provides an important supplement to the routine influenza surveillance system in Thailand. This illustrates a high level of awareness and willingness among the BDMS hospital network to report emerging infectious diseases, and highlights the robust and sensitive nature of BDMS’s surveillance system. This system demonstrates the flexibility of the surveillance systems in BDMS to evaluate to emerging infectious disease and major communicable diseases. Through participation in the Thailand influenza surveillance network, BDMS can more actively collaborate with national counterparts and use its expertise to strengthen global and regional surveillance capacity in Southeast Asia, in order to secure advances for a world safe and secure from infectious disease. Furthermore, this system can be quickly adapted and used to monitor future influenzas pandemics and other major outbreaks of respiratory infectious disease, including novel pathogens.


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