scholarly journals HL7 Terminology Management for Disease Surveillance

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Emily Roberts ◽  
Theron Jeppson ◽  
Rachelle Boulton ◽  
Josh Ridderhoff
Keyword(s):  
Disease Surveillance ◽  
Communicable Disease ◽  
Laboratory Data ◽  
Data Entry ◽  
High Volume ◽  
Electronic Data ◽  
Disease Surveillance System ◽  
Department Of Health ◽  
Message Structure ◽  
Set Up

Objective: The objective of this abstract is to illustrate how the Utah Department of Health processes a high volume of electronic data. We do this by translating what reporters send within an HL7 message into "epidemiologist" language for consumption into our disease surveillance system.Introduction: In 2013, the Utah Department of Health (UDOH) began working with hospital and reference laboratories to implement electronic laboratory reporting (ELR) of reportable communicable disease data. Laboratories utilize HL7 message structure and standard terminologies such as LOINC and SNOMED to send data to UDOH. These messages must be evaluated for validity, translated, and entered into Utah’s communicable disease surveillance system (UT-NEDSS), where they can be accessed by local and state investigators and epidemiologists. Despite the development and use of standardized terminologies, reporters may use different, outdated versions of these terminologies, may not use the appropriate codes, or may send local, home-grown terminologies. These variations cause problems when trying to interpret test results and automate data processing. UDOH has developed a two-step translation process that allows us to first standardize and clean incoming messages, and then translate them for consumption by UT-NEDSS. These processes allow us to efficiently manage several different terminologies and helps to standardize incoming data, maintain data quality, and streamline the data entry process.Methods: UDOH uses the Electronic Message Staging Area (EMSA) to receive ELR messages, manage terminologies such as LOINC and SNOMED, translate messages, and automatically enter laboratory data into UT-NEDSS. LOINCs and other terms, such as facility name, sent by reporting facilities in an HL7 message are considered child terms. All child terms are mapped to a master LOINC or term and each master LOINC or term is mapped to a specific value within UT-NEDSS. In EMSA, the rules engine used for automated processing of electronic data is set to run at the master level and these rules will determine how the message is processed. No rules are set up or run on child terms.Results: As of 09/20/2017, EMSA contains 2,613 unique child LOINCs that are mapped to 906 master LOINCs. Those 906 master LOINCs are mapped to 179 UT-NEDSS test types and 2003 child facility names are mapped to 1043 master facility namesConclusions: Mapping child terminologies from an HL7 message to a master vocabulary helps us to standardize incoming data, allows us to accept non-standard terminologies and correct reporting errors. Translating this data into a format that is understandable to epidemiologists and investigators enables UT-NEDSS to work effectively in identifying outbreaks and improving health outcomes. This framework is working for ELR and will continue to grow and accept more data and the different terminologies that come with that.

scholarly journals From epidemiological surveillance to European integration

1999 ◽  
Vol 4 (9) ◽  
pp. 91-91
Author(s):  
F Tissot
Keyword(s):  
Surveillance System ◽  
Disease Surveillance ◽  
Communicable Disease ◽  
Epidemiological Surveillance ◽  
National Surveillance ◽  
Control Potential ◽  
Disease Surveillance System ◽  
Set Up ◽  
And Control ◽  
National Surveillance System

Between March and June 1999, 442 000 Kosovar refugees arrived in Albania. The national surveillance system was unprepared for this and an emergency communicable disease surveillance system was set up to detect and control potential outbreaks among the ref

Performance Assessment of a Communicable Disease Surveillance System in Response to the Twin Earthquakes of East Azerbaijan

2015 ◽  
Vol 9 (4) ◽  
pp. 367-373 ◽  
Author(s):  
Javad Babaie ◽  
Ali Ardalan ◽  
Hasan Vatandoost ◽  
Mohammad Mehdi Goya ◽  
Ali Akbari Sari
Keyword(s):  
Data Quality ◽  
Surveillance System ◽  
Disease Surveillance ◽  
Communicable Disease ◽  
Communicable Diseases ◽  
Disease Surveillance System ◽  
Health Authorities ◽  
Control And Prevention ◽  
Set Up ◽  
East Azerbaijan

AbstractObjectiveFollowing the twin earthquakes on August 11, 2012, in the East Azerbaijan province of Iran, the provincial health center set up a surveillance system to monitor communicable diseases. This study aimed to assess the performance of this surveillance system.MethodsIn this quantitative-qualitative study, performance of the communicable diseases surveillance system was assessed by using the updated guidelines of the Centers for Disease Control and Prevention (CDC). Qualitative data were collected through interviews with the surveillance system participants, and quantitative data were obtained from the surveillance system.ResultsThe surveillance system was useful, simple, representative, timely, and flexible. The data quality, acceptability, and stability of the surveillance system were 65.6%, 10.63%, and 100%, respectively. The sensitivity and positive predictive value were not calculated owing to the absence of a gold standard.ConclusionsThe surveillance system satisfactorily met the goals expected for its setup. The data obtained led to the control of communicable diseases in the affected areas. Required interventions based on the incidence of communicable disease were designed and implemented. The results also reassured health authorities and the public. However, data quality and acceptability should be taken into consideration and reviewed for implementation in future disasters. (Disaster Med Public Health Preparedness. 2015;9:367–373)

scholarly journals Endemic Melioidosis in Southern China: Past and Present

2019 ◽  
Vol 4 (1) ◽  
pp. 39 ◽  
Author(s):  
Xiao Zheng ◽  
Qianfeng Xia ◽  
Lianxu Xia ◽  
Wei Li
Keyword(s):  
Disease Surveillance ◽  
Burkholderia Pseudomallei ◽  
Southern China ◽  
Communicable Disease ◽  
Management Strategies ◽  
Mainland China ◽  
Comprehensive Overview ◽  
Social Burden ◽  
Disease Surveillance System ◽  
Health Authorities

Melioidosis is a severe tropical infectious disease caused by the soil-dwelling bacterium Burkholderia pseudomallei, predominantly endemic to Southeast Asia and northern Australia. Between the 1970s and the 1990s, the presence of B. pseudomallei causing melioidosis in humans and other animals was demonstrated in four coastal provinces in southern China: Hainan, Guangdong, Guangxi, and Fujian, although indigenous cases were rare and the disease failed to raise concern amongst local and national health authorities. In recent years, there has been a rise in the number of melioidosis cases witnessed in the region, particularly in Hainan. Meanwhile, although China has established and maintained an effective communicable disease surveillance system, it has not yet been utilized for melioidosis. Thus, the overall incidence, social burden and epidemiological features of the disease in China remain unclear. In this context, we present a comprehensive overview of both historical and current information on melioidosis in Southern China, highlighting the re-emergence of the disease in Hainan. Surveillance and management strategies for melioidosis should be promoted in mainland China, and more research should be conducted to provide further insights into the present situation.

scholarly journals An Assessment of 2016 National Healthcare Safety Network (NHSN) and National Electronic Disease Surveillance System (NEDSS) Clostridium difficile Infections (CDI) in Nebraska

2017 ◽  
Vol 4 (suppl_1) ◽  
pp. S399-S399
Author(s):  
Caitlin Pedati ◽  
Madison Sullivan ◽  
Margaret Drake ◽  
Alison Keyser ◽  
Tom Safranek ◽  
...  
Keyword(s):  
Clostridium Difficile ◽  
Surveillance System ◽  
Disease Surveillance ◽  
Healthcare Facility ◽  
Data Sources ◽  
National Healthcare ◽  
Disease Surveillance System ◽  
Department Of Health ◽  
National Healthcare Safety Network ◽  
Community Onset

Abstract Background In 2016 all acute care hospitals, inpatient rehab facilities, and PPS-exempt cancer facilities in Nebraska were required to report laboratory identified (LabID) Clostridium difficile infections (CDIs) to the National Healthcare Safety Network (NHSN). Test results indicating CDIs must be reported to the Nebraska Department of Health and Human Services (NDHHS) via the National Electronic Disease Surveillance System (NEDSS). NHSN and NEDSS represent unique sources of CDI reports in Nebraska. Methods The NHSN Nebraska database was queried for CDIs reported in 2016. All lab tests indicating a CDI in 2016 were extracted from NEDSS. These extracts were analyzed to assess descriptive epidemiologic variables and compared for differences. Results In 2016 there were 1,546 CDI LabID events reported to NHSN Nebraska from 28 facilities. There were 249 outpatient CDIs and 1,297 inpatient CDIs. Infections were further characterized as community-onset (N = 773), community-onset, healthcare facility associated (N = 206), and hospital onset (N = 567). An average of 128 CDIs were reported per month (range: 111–155). In 2016 there were 2,177 lab results indicating a CDI reported to NEDSS among Nebraska residents from 42 facilities. Patient ages ranged from 4 months to 104 years (mean = 58 years). An average of 181 CDIs were reported per month (range: 151–218). Comparison of the two data sources found 781 reports among 591 unique patients at 11 facilities that were made to NHSN and were not in NEDSS. Additionally, there were 1,092 reports from 931 unique patients at 12 facilities that were made to NEDSS and should have been made to NHSN but were not. There were 9 shared facilities that accounted for the majority of these discrepancies. Conclusion NHSN and NEDSS represent two unique data sources that allow for a more comprehensive assessment of CDIs. The number and type of facility that report to each system is slightly different but there is some overlap. Therefore, this comparison allows for detection of a greater number of reports overall and also provides an opportunity for data validation. This assessment identified discrepancies in reporting among 9 facilities that can be targeted for further collaborative efforts to improve CDI reporting and management in Nebraska. Disclosures All authors: No reported disclosures.

Communicable Disease Epidemiology following Migration: Studies from the African Famine

1987 ◽  
Vol 21 (3) ◽  
pp. 783-795 ◽  
Author(s):  
P. Shears ◽  
T. Lusty
Keyword(s):  
Disease Surveillance ◽  
Communicable Disease ◽  
Epidemiological Studies ◽  
Morbidity And Mortality ◽  
Health Programs ◽  
Ecological Zones ◽  
Disease Epidemiology ◽  
North East ◽  
Disease Surveillance System ◽  
Mass Migration

Few epidemiological studies have been undertaken of morbidity and mortality due to communicable disease in mass migration. This article reviews data from refugee displacement areas in north-east Africa. Risk factors to increase morbidity and mortality include breakdown of health services, movement to new ecological zones, malnutrition, and crowding and poor sanitation in relief camps. Highest mortalities are recorded in chidren below five years, the principal causes being measles, gastro-enteritis, chest infections and malaria. The greatest morbidity and mortality occurs after arrival in relief camps, and could be reduced by epidemiologically based, selective health programs. This article stresses the importance of regional level coordination between relief agencies and the need for an effective disease surveillance system.

scholarly journals Electronic Processing of Antimicrobial Susceptibilities to Enhance Communicable Disease Surveillance

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Emily Roberts
Keyword(s):  
Pseudomonas Aeruginosa ◽  
Antimicrobial Agent ◽  
Antimicrobial Susceptibility ◽  
Disease Surveillance ◽  
Antimicrobial Agents ◽  
Communicable Disease ◽  
Test Method ◽  
Health Concern ◽  
Acinetobacter Species ◽  
Department Of Health

ObjectiveIllustrate how the Utah Department of Health automatically processes antimicrobial susceptibility results that are received electronically.IntroductionThe emerging threat of antimicrobial resistant organisms is a pressing public health concern. Surveillance for antimicrobial resistance can prevent infections, protect patients in the healthcare setting and improve antimicrobial use. In 2018, the Utah Department of Health mandated the reporting of antimicrobial susceptibility panels performed on selected organisms. Utah utilizes the Electronic Message Staging Area (EMSA), a home-grown application to translate, process, and enter electronic laboratory results into UT-NEDSS, Utah’s integrated disease surveillance system. Processing these results electronically is challenging due to the need to interpret results based on the antimicrobial agent combined with the organism it was performed on. The receipt of antimicrobial susceptibility panels has required enhancements to EMSA for these results to be automatically processed.MethodsStand-alone antimicrobial susceptibility LOINCs are configured within EMSA to concatenate during the preprocessing stage. This tells EMSA that when this LOINC is sent within an HL7 message to find the organism name in the corresponding OBR 26.3 (the parent result field). EMSA then creates a new fabricated code that combines the antimicrobial agent with the organism identified from the culture (example: ‘18906-8 Pseudomonas aeruginosa’ is the fabricated code for Ciprofloxacin susceptibility to Pseudomonas aeruginosa).Once these new fabricated antimicrobial susceptibility codes are created, interpretation rules are programmed using current Clinical and Laboratory Standards Institute (CLSI) breakpoints for each unique organism/antimicrobial combination to determine if the result is Susceptible/Intermediate/Resistant. The interpreted test is then run through a set of condition-specific rules to determine how it should be included into UT-NEDSS.ResultsAntimicrobial susceptibility panels performed on Acinetobacter species, Escherichia coli, Klebsiella species, Pseudomonas aeruginosa, Enterobacter species, Candida auris/haemulonii, Mycobacterium tuberculosis, Neisseria gonorrhoeae, Salmonella species, Shigella species, Streptococcus pneumoniae and invasive Staphylococcus aureus are now included in Utah’s Communicable Disease Reporting Rule. Currently, there are 36 antimicrobial agents programmed into EMSA and there are a total of 217 antimicrobial susceptibility codes programmed into the system.ConclusionsProcessing electronic antimicrobial susceptibility results presents unique challenges for processing. Interpretation of results can vary based on test method, performing laboratory, and organism. Enhancing functionality within EMSA was necessary for combining the antimicrobial agent and organism it was performed on. Implementing systems capable of automatically processing complicated antimicrobial susceptibility results should be a priority for any health department interested in expanding their communicable disease rule to include antimicrobial susceptibility testing.

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