scholarly journals Standard for improving emergency information interoperability: the HL7 data elements for emergency department systems

2015 ◽  
Vol 22 (3) ◽  
pp. 529-535 ◽  
Author(s):  
James C McClay ◽  
Peter J Park ◽  
Mark G Janczewski ◽  
Laura Heermann Langford

Abstract Background Emergency departments in the United States service over 130 million visits per year. The demands for information from these visits require interoperable data exchange standards. While multiple data exchange specifications are in use, none have undergone rigorous standards review. This paper describes the creation and balloting of the Health Level Seven (HL7) Data Elements for Emergency Department Systems (DEEDS). Methods Existing data exchange specifications were collected and organized into categories reflecting the workflow of emergency care. The concepts were then mapped to existing standards for vocabulary, data types, and the HL7 information model. The HL7 community then processed the specification through the normal balloting process addressing all comments and concerns. The resulting specification was then submitted for publication as an HL7 informational standard. Results The resulting specification contains 525 concepts related to emergency care required for operations and reporting to external agencies. An additional 200 of the most commonly ordered laboratory tests were included. Each concept was given a unique identifier and mapped to Logical Observation Identifiers, Names, and Codes (LOINC). HL7 standard data types were applied. Discussion The HL7 DEEDS specification represents the first set of common ED related data elements to undergo rigorous standards development. The availability of this standard will contribute to improved interoperability of emergency care data.

2018 ◽  
Vol 39 (8) ◽  
pp. 871-879 ◽  
Author(s):  
John G. Schumacher ◽  
Jon Mark Hirshon ◽  
Phillip Magidson ◽  
Marilyn Chrisman ◽  
Terisita Hogan

The traditional model of emergency care no longer fits the growing needs of the over 20 million older adults annually seeking emergency department care. In 2007 a tailored “geriatric emergency department” model was introduced and rapidly replicated among hospitals, rising steeply over the past 5 years. This survey examined all U.S. emergency departments self-identifying themselves as Geriatric Emergency Departments (GEDs) and providing enhanced geriatric emergency care services. It was guided by the recently adopted Geriatric Emergency Department Guidelines and examined domains including, GED identity, staffing, and administration; education, equipment, and supplies; policies, procedures, and protocols; follow-up and transitions of care; and quality improvement. Results reveal a heterogeneous mix of GED staffing, procedures, physical environments and that GEDs’ familiarity with the GED Guidelines is low. Findings will inform emergency departments and gerontologists nationwide about key GED model elements and will help hospitals to improve ED services for their older adult patients.


Author(s):  
Eugenia Rinaldi ◽  
Sylvia Thun

HiGHmed is a German Consortium where eight University Hospitals have agreed to the cross-institutional data exchange through novel medical informatics solutions. The HiGHmed Use Case Infection Control group has modelled a set of infection-related data in the openEHR format. In order to establish interoperability with the other German Consortia belonging to the same national initiative, we mapped the openEHR information to the Fast Healthcare Interoperability Resources (FHIR) format recommended within the initiative. FHIR enables fast exchange of data thanks to the discrete and independent data elements into which information is organized. Furthermore, to explore the possibility of maximizing analysis capabilities for our data set, we subsequently mapped the FHIR elements to the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM). The OMOP data model is designed to support the conduct of research to identify and evaluate associations between interventions and outcomes caused by these interventions. Mapping across standard allows to exploit their peculiarities while establishing and/or maintaining interoperability. This article provides an overview of our experience in mapping infection control related data across three different standards openEHR, FHIR and OMOP CDM.


2021 ◽  
Author(s):  
Florian J Auer ◽  
Frank Kramer

Motivation: The Cytoscape Exchange (CX) format is a JSON-based data structure designed for the transmission of biological networks using standard web technologies. It was developed by the network data exchange (NDEx), which itself serves as online commons to share and collaborate on biological networks. The Cytoscape software for the analysis and visualization of biological networks contributes further elements to capture the visual layout within the CX format. However, there is a fundamental difference between web standards and R of how data has to be structured. Results: Here we present a software package to create, handle, validate, visualize and convert networks in CX format to standard data types and objects within R. Networks in this format can serve as a source for biological knowledge, and also capture the results of the analysis of those while preserving the visual layout across all platforms. The RCX package connects the R environment for statistical computing with platforms for collaboration, analysis and visualization of biological networks. Availability: RCX is a free and open-source R package, available via GitHub (https://github.com/frankkramer-lab/RCX) and submitted to Bioconductor.


Author(s):  
Guido Espana ◽  
Sean Cavany ◽  
Rachel J Oidtman ◽  
Carly Barbera ◽  
Alan Costello ◽  
...  

In the United States, schools closed in March 2020 due to COVID-19 and have begun reopening as of August 2020, despite continuing transmission of SARS-CoV-2. To determine the impact of school reopening with varying levels of operating capacity and face-mask adherence, we used an agent-based model calibrated to and validated against multiple data types from the state of Indiana, USA. In our model, transmission can occur in schools, workplaces, community settings, and households, all of which are structured in a realistic way according to state geography and demography. Using this model, we quantified the burden of COVID-19 on K-12 students, teachers, their families, and the general population under alternative scenarios about school reopening. In our primary analysis, we considered three levels of school operating capacity (50%, 75%, and 100%) and three assumptions about face-mask adherence in schools (50%, 75%, and 100%). Under a scenario in which schools operate remotely, we projected 45,579 (95% CrI: 14,109-132,546) infections and 790 (95% CrI: 176-1680) deaths between August 24 and December 31. Reopening at 100% capacity with 50% face-mask adherence in schools resulted in a proportional increase of 42.9 (95% CrI: 41.3-44.3) times that number of infections and 9.2 (95% CrI: 8.9-9.5) times that number of deaths. In contrast, operating at 50% capacity with 100% face-mask adherence resulted in only an 11% (95% CrI: 5%-18%) increase in the number of infections compared to the scenario in which schools operate remotely. We conclude that reduced capacity and high face-mask adherence in schools substantially reduce the burden of COVID-19, both among those with direct ties to schools and across the state. As Indiana and other states proceed with school reopening, our results illustrate quantitatively the benefits of safety measures that schools are undertaking, underscoring their value for both schools and their communities.


2018 ◽  
Vol 09 (03) ◽  
pp. 528-540
Author(s):  
Agnes Sundaresan ◽  
Gargi Schneider ◽  
Joy Reynolds ◽  
H. Kirchner

Background Asthma exacerbation leading to emergency department (ED) visit is prevalent, an indicator of poor control of asthma, and is a potentially preventable clinical outcome. Objective We propose to utilize multiple data elements available in electronic medical records (EMRs) and claims database to create separate algorithms with high validity for clinical and research purposes to identify asthma exacerbation-related ED visit among the general population. Methods We performed a retrospective study with inclusion criteria of patients aged 4 to 40 years, a visit to Geisinger ED from January 1, 2006, to October 28, 2013, with asthma on their problem list. Different electronic data elements including chief complaints, vitals, season, smoking, medication use, and discharge diagnoses were obtained to create the algorithm. A stratified random sample was generated to select the charts for review. Chart review was performed to classify patients with asthma-related ED visit, that is, the gold standard. Two reviewers performed the chart review and validation was done on a small subset. Results There were 966 eligible ED visits in the EMR sample and 731 in the claims sample. Agreement between reviewers was 95.45% and kappa statistic was 0.91. Mean age of the EMR sample was 22 years, and mostly white (93%). Multiple models conventionally used in studies were evaluated and the final model chosen included principal diagnosis, bronchodilator, and steroid use for both algorithms, chief complaints for EMR, and secondary diagnosis for claims. Area under the curve was 0.93 (95% confidence interval: 0.91–0.94) and 0.94 (0.93–0.96), respectively, for EMR and claims data, with positive predictive value of > 94%. The algorithms are visually presented using nomograms. Conclusion We were able to develop two separate algorithms for EMR and claims to identify asthma exacerbation-related ED visit with excellent diagnostic ability and varying discrimination threshold for clinical and research purposes.


2020 ◽  
Vol 59 (01) ◽  
pp. 048-056
Author(s):  
Karishma Bhatia ◽  
James Tanch ◽  
Elizabeth S. Chen ◽  
Indra Neil Sarkar

Abstract Background There is a recognized need to improve how scholarly data are managed and accessed. The scientific community has proposed the findable, accessible, interoperable, and reusable (FAIR) data principles to address this issue. Objective The objective of this case study was to develop a system for improving the FAIRness of Healthcare Cost and Utilization Project's State Emergency Department Databases (HCUP's SEDD) within the context of data catalog availability. Methods A search tool, EDCat (Emergency Department Catalog), was designed to improve the “FAIRness” of electronic health databases and tested on datasets from HCUP-SEDD. ElasticSearch was used as a database for EDCat's search engine. Datasets were curated and defined. Searchable data dictionary-related elements and unified medical language system (UMLS) concepts were included in the curated metadata. Functionality to standardize search terms using UMLS concepts was added to the user interface. Results The EDCat system improved the overall FAIRness of HCUP-SEDD by improving the findability of individual datasets and increasing the efficacy of searches for specific data elements and data types. Discussion The databases considered for this case study were limited in number as few data distributors make the data dictionaries of datasets available. The publication of data dictionaries should be encouraged through the FAIR principles, and further efforts should be made to improve the specificity and measurability of the FAIR principles. Conclusion In this case study, the distribution of datasets from HCUP-SEDD was made more FAIR through the development of a search tool, EDCat. EDCat will be evaluated and developed further to include datasets from other sources.


Author(s):  
Phillip Carleton ◽  
Sylvan Hoover ◽  
Ben Fields ◽  
Matthew Barnes ◽  
J. David Porter

The rapid growth in the availability and utility of vast amounts of digital data is arguably one of the most significant technological developments in recent years. In public transit, many agencies utilize modern technologies to collect large amounts of data, whereas smaller agencies with fewer resources and less expertise still use more traditional, manual data collection methods. Regardless of their technological capabilities, transit agencies recognize that some amount of transit data is useful and required. To the best of our knowledge, no standard data description of detailed fixed-route ridership exists today in the United States, forcing transit agencies to develop their own system of collecting, storing, and analyzing ridership and related data. In response to this need, this research aimed at developing one of the first public transit ridership data standards for fixed-route services and to support and promote its adoption and use. The resulting standard, an extension to the General Transit Feed Specification (GTFS) data standard, is referred to as GTFS-ride. GTFS-ride is easy to understand, able to accommodate the complexities of larger transit agencies, and capable of establishing a strong connection to the state of a transit network as it existed when the ridership data was collected. The first complete draft of GTFS-ride was released on September 6, 2017. This paper explains the structure of the five files that compose GTFS-ride, introduces additional support elements developed to facilitate its promotion and adoption, and documents the lessons learned from pilot implementations of GTFS-ride at three Oregon public transit agencies.


New data management prerequisites have been released by massive data development and cloud computing. Numerous applications really need to interact on the basis of the types they really need to control with a lot of heterogeneous data stores: standard data types, documents, social network graph data, simple key-value data, etc. A unifying data model is attracting considerable for developers due to flexible communication with heterogeneous relations and NoSQL data stores. The OPEN-PaaS-DataBase API (ODBAPI), which is a well-accepted REST API, allows developers to code their implementation software in the target data storage system separately. Secondly, we propose virtual data stores that interact with the integrated data store wrapped in ODBAPI. It was shown that this prototype responsible for probable solution technologically advanced capable to introduce OpenPaaS circumstances.


2020 ◽  
Author(s):  
Tyler Hill ◽  
Yun Jiang ◽  
Christopher Friese ◽  
Lynae Darbes ◽  
Christopher K. Blazes ◽  
...  

Abstract Background : We aimed to characterize Emergency Department (ED) utilization and outcomes of patients with depression seeking emergency care for all reasons. Methods : Using 2014–2016 ED data from the National Hospital Ambulatory Medical Care Survey, we investigated demographics, ED resource utilization, clinical characteristics, and disposition of patients with depression versus those without depression. Results : Approximately 10,626,184 (11.4%) out of 92,899,685 annual ED visits were by patients with depression. ED patients with depression were mostly non-Hispanic White (74.0%) and were less likely to be male than patients without depression (aOR: 0.56; [95%] CI: 0.56–0.56). ED patients with depression were more likely to be admitted to the hospital (aOR: 1.56; CI: 1.55–1.56) and intensive care unit (ICU) (aOR: 1.28; CI: 1.27–1.28) than patients without depression. Among ED patients with depression, males were more likely than females to be seeking emergency care for psychiatric reasons (aOR: 2.04; CI: 2.03–2.05) and to present with overdose/poisoning (aOR: 1.35; CI: 1.34–1.36). Conclusions : We described the unique demographic, socioeconomic, and clinical characteristics of ED patients with depression, using the most comprehensive, nationally representative study to date. We revealed notable gender disparities in rates and reasons for admissions. The higher hospital and ICU admission rates of ED patients with depression suggests this population requires a higher level of emergency care, for reasons that remain poorly understood.


Blood ◽  
2009 ◽  
Vol 114 (22) ◽  
pp. 1413-1413
Author(s):  
Paula Tanabe ◽  
Ted Wun ◽  
Victoria Thornton ◽  
Knox Todd ◽  
John S Lyons

Abstract Abstract 1413 Poster Board I-436 Objectives: There are relatively few centers across the United States that either specialize in SCD care or have day hospitals where patients can be evaluated and urgently treated for acute pain crises. While most patients come to the ED for management of an acute pain crisis, SCD patients are at risk for many life-threatening complications. Most patients with SCD require an ED visit at some point. The complexity of SCD warrants a comprehensive assessment in the emergency department. While it may be challenging to conduct such an assessment in the ED, a succinct decision support tool may help guide clinicians in the performance of such an assessment. The benefits of such an assessment would identify unmet patient needs and help guide ED management and referrals. The goal of this project was to develop a brief, easy to use tool that guides the emergency clinicians in the identification of such needs and aid in accomplishing the following goals: 1) rapidly and aggressively manage ED pain, 2) identify other life-threatening conditions, 3) decrease hospital admission rates, 4) decrease return visits to the ED, 5) identify and increase the number of referrals made from the ED setting, and 6) increase both patient and clinician satisfaction with the ED experience. Methods: A series of seven clinician and patient focus groups were conducted in four cities across the United States (Chicago, Denver, Durham, and New York) to obtain key stakeholder input. Visits at three SCD centers of excellence (University of Colorado Denver, Duke University, Virginia Commonwealth University) were conducted, a literature search was conducted, and the PI attended SCD clinics to observe practice patterns with sickle cell experts at the University of Illinois and University of Chicago sickle cell clinics. Focus group data was analyzed using qualitative methods and is reported elsewhere. All data was synthesized and a draft tool was created and reviewed by outside experts. Revisions were made. Results: The following six key decisions were identified as being critical in achieving the tools aims: (1) what is the correct triage level, (2) how should pain be treated, (3) does the patient require a diagnostic work-up, (4) should the patient be admitted to the hospital, (5) if discharged home, is there a need for analgesic prescriptions, and (6) does the patient need a referral to a sickle cell expert or mental health or social services? Supporting data elements for each decision were also identified and included as part of the tool which will be formulated into an easy to use algorithm. Data elements include key history and physical indicators of a potential high risk situation necessitating further evaluation, pain assessment and history of analgesic use, relationship with a sickle cell expert, ED and hospital utilization history, and evaluation of psychosocial needs (self-report of anxiety or depression, work/employment status, home situation). Conclusions: Critical decisions and associated supporting elements to facilitate ED management were identified. Future work will involve finalizing and testing this communimetric tool, which will guide emergency department evaluation and management, as well as guide analgesic management in real time. Disclosures: Tanabe: NIH, and Mayday Fund: Research Funding. Todd: NIH: Research Funding; Xanodyne: Consultancy; Merck: Consultancy; Alpharma: Consultancy; Abbott: Consultancy; Baxter Healthcare: Consultancy; Fralex Therapeutics: Consultancy; Intranasal Therapeutics: Consultancy; Baxter Health: Research Funding; Roxro: Consultancy.


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