scholarly journals Consensus Development of a Modern Ontology of Emergency Department Presenting Problems – the HierArchical Presenting Problem ontologY (HaPPy)

2017 ◽  
Author(s):  
Steven Horng ◽  
Nathaniel R. Greenbaum ◽  
Larry A. Nathanson ◽  
James C McClay ◽  
Foster R. Goss ◽  
...  
Keyword(s):  
Emergency Department ◽  
Real World ◽  
Emergency Department Patient ◽  
Validation Dataset ◽  
Snomed Ct ◽  
Presenting Problems ◽  
Real World Data ◽  
World Data ◽  
Academic Level ◽  
The Right

ABSTRACTObjectiveNumerous attempts have been made to create a standardized ‘presenting problem’ or ‘chief complaint’ list to characterize the nature of an Emergency Department visit. Previous attempts have failed to gain widespread adoption as none were freely sharable and contained the right level of specificity, structure, and clinical relevance to gain acceptance by the larger emergency medicine community. Using real-world data, we constructed a presenting problem list that addresses these challenges.Materials and MethodsWe prospectively captured the presenting problems for 180,424 consecutive emergency department patient visits at an urban, academic, Level I trauma center in the Boston metro area. No patients were excluded. We used a consensus process to iteratively derive our system using real-world data. We used the first 70% of consecutive visits to derive our ontology; followed by a 6 month washout period, and the remaining 30% for validation. All concepts were mapped to SNOMED-CT.ResultsOur system consists of a polyhierarchical ontology containing 692 unique concepts, 2,118 synonyms, and 30,613 non-visible descriptions to correct misspellings and non-standard terminology. Our ontology successfully captured structured data for 95.9% of visits in our validation dataset.Discussion and ConclusionWe present the HierArchical Presenting Problem ontologY (HaPPy). This ontology was empirically derived then iteratively validated by an expert consensus panel. HaPPy contains 692 presenting problem concepts, each concept being mapped to SNOMED-CT. This freely sharable ontology can help to facilitate presenting problem based quality metrics, research, and patient care.

scholarly journals Consensus Development of a Modern Ontology of Emergency Department Presenting Problems—The Hierarchical Presenting Problem Ontology (HaPPy)

2019 ◽  
Vol 10 (03) ◽  
pp. 409-420 ◽  
Author(s):  
Steven Horng ◽  
Nathaniel R. Greenbaum ◽  
Larry A. Nathanson ◽  
James C. McClay ◽  
Foster R. Goss ◽  
...  
Keyword(s):  
Emergency Department ◽  
Real World ◽  
Emergency Department Patient ◽  
Snomed Ct ◽  
Presenting Problems ◽  
Real World Data ◽  
Validation Data ◽  
Data Set ◽  
World Data ◽  
Academic Level

Objective Numerous attempts have been made to create a standardized “presenting problem” or “chief complaint” list to characterize the nature of an emergency department visit. Previous attempts have failed to gain widespread adoption as they were not freely shareable or did not contain the right level of specificity, structure, and clinical relevance to gain acceptance by the larger emergency medicine community. Using real-world data, we constructed a presenting problem list that addresses these challenges. Materials and Methods We prospectively captured the presenting problems for 180,424 consecutive emergency department patient visits at an urban, academic, Level I trauma center in the Boston metro area. No patients were excluded. We used a consensus process to iteratively derive our system using real-world data. We used the first 70% of consecutive visits to derive our ontology, followed by a 6-month washout period, and the remaining 30% for validation. All concepts were mapped to Systematized Nomenclature of Medicine–Clinical Terms (SNOMED CT). Results Our system consists of a polyhierarchical ontology containing 692 unique concepts, 2,118 synonyms, and 30,613 nonvisible descriptions to correct misspellings and nonstandard terminology. Our ontology successfully captured structured data for 95.9% of visits in our validation data set. Discussion and Conclusion We present the HierArchical Presenting Problem ontologY (HaPPy). This ontology was empirically derived and then iteratively validated by an expert consensus panel. HaPPy contains 692 presenting problem concepts, each concept being mapped to SNOMED CT. This freely sharable ontology can help to facilitate presenting problem-based quality metrics, research, and patient care.

scholarly journals Emergency Department Online Patient-Caregiver Scheduling

2019 ◽  
Vol 33 ◽  
pp. 10013-10014
Author(s):  
Hanan Rosemarin ◽  
Ariel Rosenfeld ◽  
Sarit Kraus
Keyword(s):  
Emergency Department ◽  
Deep Learning ◽  
Medical Care ◽  
Real World ◽  
Ad Hoc ◽  
Performance Metrics ◽  
Empirical Evaluation ◽  
Medical Personnel ◽  
Real World Data ◽  
The Right

Emergency Departments (EDs) provide an imperative source of medical care. Central to the ED workflow is the patientcaregiver scheduling, directed at getting the right patient to the right caregiver at the right time. Unfortunately, common ED scheduling practices are based on ad-hoc heuristics which may not be aligned with the complex and partially conflicting ED’s objectives. In this paper, we propose a novel online deep-learning scheduling approach for the automatic assignment and scheduling of medical personnel to arriving patients. Our approach allows for the optimization of explicit, hospitalspecific multi-variate objectives and takes advantage of available data, without altering the existing workflow of the ED. In an extensive empirical evaluation, using real-world data, we show that our approach can significantly improve an ED’s performance metrics.

scholarly journals Emergency Department Online Patient-Caregiver Scheduling

2019 ◽  
Vol 33 ◽  
pp. 695-701 ◽  
Author(s):  
Hanan Rosemarin ◽  
Ariel Rosenfeld ◽  
Sarit Kraus
Keyword(s):  
Emergency Department ◽  
Deep Learning ◽  
Medical Care ◽  
Real World ◽  
Ad Hoc ◽  
Performance Metrics ◽  
Empirical Evaluation ◽  
Medical Personnel ◽  
Real World Data ◽  
The Right

Emergency Departments (EDs) provide an imperative source of medical care. Central to the ED workflow is the patientcaregiver scheduling, directed at getting the right patient to the right caregiver at the right time. Unfortunately, common ED scheduling practices are based on ad-hoc heuristics which may not be aligned with the complex and partially conflicting ED's objectives. In this paper, we propose a novel online deep-learning scheduling approach for the automatic assignment and scheduling of medical personnel to arriving patients. Our approach allows for the optimization of explicit, hospital-specific multi-variate objectives and takes advantage of available data, without altering the existing workflow of the ED. In an extensive empirical evaluation, using real-world data, we show that our approach can significantly improve an ED's performance metrics.

scholarly journals Adherence to Anaphylaxis Guidelines: Real-World Data From the Emergency Department of a Tertiary Hospital

2018 ◽  
Vol 28 (4) ◽  
pp. 246-252 ◽  
Author(s):  
LV Ponce Guevara ◽  
E Laffond Yges ◽  
MT Gracia Bara ◽  
E Moreno Rodilla ◽  
FJ Muñoz Bellido ◽  
...  
Keyword(s):  
Emergency Department ◽  
Real World ◽  
Tertiary Hospital ◽  
Real World Data ◽  
World Data

scholarly journals A review of the trend of microlearning

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Kelvin Leong ◽  
Anna Sung ◽  
David Au ◽  
Claire Blanchard
Keyword(s):  
Language Learning ◽  
Real World ◽  
Google Trends ◽  
Real World Data ◽  
Content Type ◽  
World Data ◽  
E Learning ◽  
Internet Searches ◽  
Relevant Publication ◽  
The Right

PurposeMicrolearning has been considered as a promising topic in work-based learning. This paper aims to review the trends of microlearning in terms of related publications and Internet searches. Hopefully, the findings can serve as a reference for the education sector, government, business and academia to promote, design and use microlearning.Design/methodology/approachIn this study, two sets of analysis were conducted. Firstly, the authors analysed the publication trend of microlearning. Second, the authors analysed the trend of Internet searches related to microlearning. More specifically, the authors analysed real-world data of 14 years obtained from Scopus and Google Trends for the purpose. These data include the first relevant publication found in the database.FindingsIn total, 476 relevant publications have been identified during 2006–2019. According to the findings from the analysis of the identified publications, microlearning is a relevantly new and emerging global topic involving authors, affiliations and funding sponsors from different countries. Moreover, many microlearning-related publications were conducted from perspectives of e-learning or mobile learning. Furthermore, the authors notice higher education was the most frequently mentioned education level in the identified publications. On the other hand, language learning (i.e. second language, vocabulary learning, etc.) had been mentioned more times in the titles and abstracts than other subject areas. Overall, the increasing trend of publications on “microlearning” (as a knowledge supply) is in line with the established increasing Internet searches of “microlearning” (as a practical demand) in recent years.Practical implicationsFrom the work-based learning perspective, microlearning has been considered as one of the key topics in talent development topics. Policymakers, educators, researchers and participators have the responsibility to explore how to promote, design and use microlearning to help people to learn in the right direction through valid knowledge with ethical consideration.Originality/valueAlthough many works had been done on microlearning, there is a lack of comprehensive studies reviewing the trends of microlearning in terms of related publications and Internet searches. This study aims to fill this gap by analysing real-world data obtained from Scopus and Google Trends – these data include the first relevant publication found in the database. The authors believe this is the first time that a study has been conducted to comprehensively review the development trends of microlearning. Hopefully, this study can shed some light on related research.

Abstract #946: Assessment of Acromegaly Patients with and Without Diabetes Treated with Lanreotide Depot: 2-Year Real World Data

2016 ◽  
Vol 22 ◽  
pp. 219
Author(s):  
Roberto Salvatori ◽  
Olga Gambetti ◽  
Whitney Woodmansee ◽  
David Cox ◽  
Beloo Mirakhur ◽  
...  
Keyword(s):  
Real World ◽  
Real World Data ◽  
World Data
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