Improving ED Emergency Severity Index Acuity Assignment Using Machine Learning and Clinical Natural Language Processing

Иванов, О.П.; Wolf, Lisa; Brecher, Deena; Lewis, Erica; Masek, K; Montgomery, Kyla; Andrieiev, Yurii; McLaughlin, Moss; Liu, Stephen V.; Dunne, Robert; Klauer, Kevin; Reilly, Christian

doi:10.1016/j.jen.2020.11.001

Cited by 54 publications

(58 citation statements)

References 41 publications

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“…A basic strategy is to reduce the waiting time for triage or to prioritize patients according to the urgency of the clinical situation. [ 30 ] Ajami et al . (2021) A study aimed at Wait Time in Emergency Department (ED) Processes do this study The questionnaires of the mentioned study were sent to 663 patients over a 2-week period.…”

Section: Resultsmentioning

confidence: 99%

Benefits of simulation-based education in hospital emergency departments

Moslehi

Masoumi

Shirazi

2022

Journal of Education and Health Promotion

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BACKGROUND: The emergency department is one of the most important parts of all hospitals. For this reason, many simulation programs are performed in this department to increase the knowledge, skills, and productivity of health-care workers. The purpose of this study was to identify the benefits of simulation in hospital emergency departments. MATERIALS AND METHODS: In the present systematic study, using “AND” and “OR” operators, we searched for the keywords “benefits,” “simulation,” and “hospital emergency department” in PubMed, Web of Science, Scopus, Google Scholar as well as Persian language databases such SID, Magiran, Irandoc, and Iran Medex. Then, a three-step screening process was used to select studies relevant to simulation and hospital emergency from 2005 to 2021 using the PRISMA checklist, and finally, the obtained data were analyzed. RESULTS: A total of three main groups, each with several subgroups, were extracted and identified as the benefits of using simulation in hospital emergency departments. They included improving the diagnosis of the disease (rapid prediction of the disease, rapid diagnosis, and patient triage), improving the treatment process (improvement of treatment results, anticipation of admission and discharge of patients, acceleration of interventions, and reduction of medical errors), and improving knowledge and skills (improvement of the speed of decision-making, staff's acquisition of knowledge and skills, simple, convenient, and low-cost training, improvement of staff's preparedness in crisis). CONCLUSION: Based on the results of the present study, it is suggested to develop some training programs in order to help staff upgrade their knowledge and performance as well as acquire practical skills and also to improve the diagnosis and treatment process in hospital emergency departments. Virtual methods are also proposed to be applied as potential and cost-effective platforms for learning, teaching, and evaluating the staff of hospital emergency departments.

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Section: Resultsmentioning

confidence: 99%

Benefits of simulation-based education in hospital emergency departments

Moslehi

Masoumi

Shirazi

2022

Journal of Education and Health Promotion

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“…Another potential application of our model is "self-triage". Certain computer algorithms had been proposed for patients to perform self-triage before ED visits, in the hope of better patient streamlining [37][38][39][40] . However, most of the algorithms either require many variables to perform prediction or fail to demonstrate a sufficient prediction power [37][38][39][40] .…”

Section: Discussionmentioning

confidence: 99%

“…Certain computer algorithms had been proposed for patients to perform self-triage before ED visits, in the hope of better patient streamlining [37][38][39][40] . However, most of the algorithms either require many variables to perform prediction or fail to demonstrate a sufficient prediction power [37][38][39][40] . Our model offers a reliable prediction of hospital admission using very limited variables that could be obtained by the patients themselves.…”

Section: Discussionmentioning

confidence: 99%

“…Finally, we did not perform a comparison analysis with other computer or human (physicians or triage nurses) prediction models. To our knowledge, despite certain studies on triage accuracy [37][38][39] , no model for prediction of hospitalization had been reported. This is an interesting subject for future studies, which would facilitate the integration of the AI model into the work of the triage crew.…”

Section: Limitationsmentioning

confidence: 99%

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Prediction of hospitalization using artificial intelligence for urgent patients in the emergency department

Lee

Hsieh

Lin

et al. 2021

Sci Rep

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Timely assessment to accurately prioritize patients is crucial for emergency department (ED) management. Urgent (i.e., level-3, on a 5-level emergency severity index system) patients have become a challenge since under-triage and over-triage often occur. This study was aimed to develop a computational model by artificial intelligence (AI) methodologies to accurately predict urgent patient outcomes using data that are readily available in most ED triage systems. We retrospectively collected data from the ED of a tertiary teaching hospital between January 1, 2015 and December 31, 2019. Eleven variables were used for data analysis and prediction model building, including 1 response, 2 demographic, and 8 clinical variables. A model to predict hospital admission was developed using neural networks and machine learning methodologies. A total of 282,971 samples of urgent (level-3) visits were included in the analysis. Our model achieved a validation area under the curve (AUC) of 0.8004 (95% CI 0.7963–0.8045). The optimal cutoff value identified by Youden's index for determining hospital admission was 0.5517. Using this cutoff value, the sensitivity was 0.6721 (95% CI 0.6624–0.6818), and the specificity was 0.7814 (95% CI 0.7777–0.7851), with a positive predictive value of 0.3660 (95% CI 0.3586–0.3733) and a negative predictive value of 0.9270 (95% CI 0.9244–0.9295). Subgroup analysis revealed that this model performed better in the nontraumatic adult subgroup and achieved a validation AUC of 0.8166 (95% CI 0.8199–0.8212). Our AI model accurately assessed the need for hospitalization for urgent patients, which constituted nearly 70% of ED visits. This model demonstrates the potential for streamlining ED operations using a very limited number of variables that are readily available in most ED triage systems. Subgroup analysis is an important topic for future investigation.

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“…The ability to identify cases is of particular importance for rare or complex presentations that are not easily captured using existing diagnostic codes 16. These triage narratives have been clinically operationalised by the Emergency Nurses Association, which incorporated AI into triage decision support 23. The clinical implementation of AI into triage suggests that the research field is poised to expand significantly, and as such a mapping of the current literature base is needed.…”

Section: Introductionmentioning

confidence: 99%

Emergency nurses’ triage narrative data, their uses and structure: a scoping review protocol

et al. 2022

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IntroductionThe first clinical interaction most patients have in the emergency department occurs during triage. An unstructured narrative is generated during triage and is the first source of in-hospital documentation. These narratives capture the patient’s reported reason for the visit and the initial assessment and offer significantly more nuanced descriptions of the patient’s complaints than fixed field data. Previous research demonstrated these data are useful for predicting important clinical outcomes. Previous reviews examined these narratives in combination or isolation with other free-text sources, but used restricted searches and are becoming outdated. Furthermore, there are no reviews focused solely on nurses’ (the primary collectors of these data) narratives.Methods and analysisUsing the Arksey and O’Malley scoping review framework and PRISMA-ScR reporting guidelines, we will perform structured searches of CINAHL, Ovid MEDLINE, ProQuest Central, Ovid Embase and Cochrane Library (via Wiley). Additionally, we will forward citation searches of all included studies. No geographical or study design exclusion criteria will be used. Studies examining disaster triage, published before 1990, and non-English language literature will be excluded. Data will be managed using online management tools; extracted data will be independently confirmed by a separate reviewer using prepiloted extraction forms. Cohen’s kappa will be used to examine inter-rater agreement on pilot and final screening. Quantitative data will be expressed using measures of range and central tendency, counts, proportions and percentages, as appropriate. Qualitative data will be narrative summaries of the authors’ primary findings.Patient and public involvementNo patients involved.Ethics and disseminationNo ethics approval is required. Findings will be submitted to peer-reviewed conferences and journals. Results will be disseminated using individual and institutional social media platforms.

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Improving ED Emergency Severity Index Acuity Assignment Using Machine Learning and Clinical Natural Language Processing

Cited by 54 publications

References 41 publications

Benefits of simulation-based education in hospital emergency departments

Benefits of simulation-based education in hospital emergency departments

Prediction of hospitalization using artificial intelligence for urgent patients in the emergency department

Emergency nurses’ triage narrative data, their uses and structure: a scoping review protocol

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