Machine learning–based triage to identify low-severity patients with a short discharge length of stay in emergency department

Chang, Yu-Hsin; Shih, Hong-Mo; Wu, Jia-En; Huang, Fen-Wei; Chen, Wei-Kung; Chen, Dar-Min; Chung, Yu-Ting; Wang, Charles C.N.

doi:10.1186/s12873-022-00632-6

Cited by 12 publications

(4 citation statements)

References 32 publications

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“…CatBoost. They established their model based on two health institutions' data that employ the TTAS triage system in Taiwan [14]. Another study in Iran inspected the possibility of navigating the work ow of emergency cases through simulation modeling and ML algorithms, where they evaluated the impact of a few factors relevant to resources, numbers of providers, and available inpatient beds on the waiting time of triage-run units and fast-track units.…”

Section: Discussionmentioning

confidence: 99%

“…While Chang et al utilized tree-based algorithms and boosting methods to identify low-severity cases among level III patients in Taiwan, they excluded levels IV and IV. Moreover, they did not incorporate neural network algorithms, which are prominent in different classi cation scenarios [14]. To our knowledge, no study explored the e cacy of implementing Arti cial Neural Network (ANN) algorithms to categorize emergency patients into fast-track and complex based on well-established triage systems, including the ESI scale, along with comparing their performance to the conventional machine learning (ML) approaches.…”

Section: Introductionmentioning

confidence: 99%

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Classifying Emergency Patients into Fast-Track and Complex Cases using Machine Learning

Karajeh,

Eskicioglu

2024

IJAIA

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Emergency medicine is a lifeline specialty at hospitals that patients head to for various reasons, including serious health problems, traumas, and adventitious conditions. Emergency departments are restricted to limited resources and personnel, complicating the optimal handling of all received cases. Therefore, crowded waiting areas and long waiting durations result. In this research, the databases of MIMIC-IV-ED and MIMIC-IV were utilized to obtain records of patients who visited the Beth Israel Deaconess Medical Center in the USA. Triage data, dispositions, and length of stay of these individuals were extracted. Subsequently, the urgency of these cases was inferred based on standards stated in the literature and followed in developed countries. A comparative framework using four different machine learning algorithms besides a reference model was developed to classify these patients into complex and fast–track categories. Moreover, the relative importance of employed predictors was determined. This study proposes an approach to deal with non-urgent visits and lower overall waiting times at the emergency by utilizing the powers of machine learning to identify high-severity and low-severity patients. Given the provision of the required resources, the proposed classification would help improve the overall throughput and patient satisfaction.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Classifying Emergency Patients into Fast-Track and Complex Cases using Machine Learning

Karajeh,

Eskicioglu

2024

IJAIA

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“…Fast-tracking stable patients in an emergency is a concept that has been investigated in a few studies, which in turn indicated hopeful rami cations. [14]. Another study in Iran inspected the possibility of navigating the work ow of emergency cases through simulation modeling and ML algorithms, where they evaluated the impact of a few factors relevant to resources, numbers of providers, and available inpatient beds on the waiting time of triage-run units and fast-track units.…”

Section: Discussionmentioning

confidence: 99%

Classifying Emergency Patients into Fast-Track and Complex Cases Using Machine Learning

Karajeh,

Eskicioglu

2023

Preprint

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Background: Emergency medicine is a lifeline specialty at hospitals that patients head to for various reasons, including serious health problems, traumas, and adventitious conditions. Emergency departments are restricted to limited resources and personnel, which complicates handling all received cases optimally. Therefore, crowded waiting areas and long waiting durations result, which prompts some patients to leave before being examined. Methods: We utilized the databases (MIMIC-IV-ED and MIMIC-IV) to obtain records of patients who visited the Beth Israel Deaconess Medical Center in the USA. Triage and demographic data, dispositions, and length of stay of these individuals were extracted accordingly. Subsequently, the urgency of these cases was inferred based on standards stated in the literature and followed in developed countries, which are less than four-hour lengths of stay besides being discharged at the end of the emergency visit. Five classifying models were established by utilizing logistic regression, random forests, stochastic gradient boosting, classification and regression trees besides multi-layer perceptron algorithms. Results: A comparative framework using the five different machine learning algorithms was developed to classify these patients into two categories where the multi-layer perceptron model outperformed the others. Moreover, the relative importance of the outcome predictors was determined. Conclusions: This study suggests an approach to deal with non-urgent visits and lower overall waiting times at the emergency by utilizing the powers of machine learning to identify fast-track patients and discern them from critical cases.

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“…It has also proven to be useful in predicting events in asymptomatic patients and in patients following ACS [24,25], or, when using textual data, by determining cardiovascular disease risk from social media [26]. In-hospital AI, outside of the field of cardiology, has been able to identify patients admitted to the ED at risk of clinical deterioration [27], and identify low-severity patients for quick discharge [28]. However, the evidence for the use in prehospital triage is scarce.…”

Section: Renderxmentioning

confidence: 99%

AI Algorithm to Predict Acute Coronary Syndrome in Prehospital Cardiac Care: Retrospective Cohort Study

de Koning,

van der Haas,

Saguna

et al. 2023

JMIR Cardio

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Background Overcrowding of hospitals and emergency departments (EDs) is a growing problem. However, not all ED consultations are necessary. For example, 80% of patients in the ED with chest pain do not have an acute coronary syndrome (ACS). Artificial intelligence (AI) is useful in analyzing (medical) data, and might aid health care workers in prehospital clinical decision-making before patients are presented to the hospital. Objective The aim of this study was to develop an AI model which would be able to predict ACS before patients visit the ED. The model retrospectively analyzed prehospital data acquired by emergency medical services' nurse paramedics. Methods Patients presenting to the emergency medical services with symptoms suggestive of ACS between September 2018 and September 2020 were included. An AI model using a supervised text classification algorithm was developed to analyze data. Data were analyzed for all 7458 patients (mean 68, SD 15 years, 54% men). Specificity, sensitivity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for control and intervention groups. At first, a machine learning (ML) algorithm (or model) was chosen; afterward, the features needed were selected and then the model was tested and improved using iterative evaluation and in a further step through hyperparameter tuning. Finally, a method was selected to explain the final AI model. Results The AI model had a specificity of 11% and a sensitivity of 99.5% whereas usual care had a specificity of 1% and a sensitivity of 99.5%. The PPV of the AI model was 15% and the NPV was 99%. The PPV of usual care was 13% and the NPV was 94%. Conclusions The AI model was able to predict ACS based on retrospective data from the prehospital setting. It led to an increase in specificity (from 1% to 11%) and NPV (from 94% to 99%) when compared to usual care, with a similar sensitivity. Due to the retrospective nature of this study and the singular focus on ACS it should be seen as a proof-of-concept. Other (possibly life-threatening) diagnoses were not analyzed. Future prospective validation is necessary before implementation.

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Machine learning–based triage to identify low-severity patients with a short discharge length of stay in emergency department

Cited by 12 publications

References 32 publications

Classifying Emergency Patients into Fast-Track and Complex Cases using Machine Learning

Classifying Emergency Patients into Fast-Track and Complex Cases using Machine Learning

Classifying Emergency Patients into Fast-Track and Complex Cases Using Machine Learning

AI Algorithm to Predict Acute Coronary Syndrome in Prehospital Cardiac Care: Retrospective Cohort Study

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