Early short-term prediction of emergency department length of stay using natural language processing for low-acuity outpatients

Chen, Chien‐Hua; Hsieh, Jer‐Guang; Cheng, Shu-Ling; Lin, Yih-Lon; Lin, Po-Hsiang; Jeng, Jyh-Horng

doi:10.1016/j.ajem.2020.03.019

Cited by 14 publications

(7 citation statements)

References 14 publications

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“…Taiwan’s implementation of National Health Insurance in 1994 enhanced public access to healthcare. From 2000 to 2015, the number of ED visits in Taiwan increased by about 20.7%, leading to ED crowding and a larger number of frequent ED users [ 5 , 6 ].…”

Section: Introductionmentioning

confidence: 99%

Impact of the COVID-19 Pandemic on Healthcare-Seeking Behaviors among Frequent Emergency Department Users: A Cohort Study

Chou

Yen

Chu

et al. 2021

IJERPH

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In 2020, Taiwan’s healthcare system faced a notable burden imposed by the coronavirus disease (COVID-19) pandemic. Emergency department (ED) is a high-risk area for severe acute respiratory syndrome coronavirus 2 transmission. The effect of COVID-19 on the utilization of ED services among frequent ED users remains unknown. This cohort study determined the impact of the COVID-19 pandemic on healthcare-seeking behaviors among frequent ED users at Taipei City Hospital, Taiwan. We included ED users aged ≥18 years admitted to Taipei City Hospital during February 2019–January 2020 (before the pandemic) and February 2020–January 2021 (during the pandemic). Frequent ED users were patients with four or more ED visits per year. Stepwise logistic regression was performed to identify predictors of frequent ED use during the COVID-19 pandemic. Frequent ED users had shorter hospital stays in the ED during the pandemic. After adjusting for sociodemographic factors and other covariates, patients with a triage status of level 4–5, pneumonia diagnosis, giddiness, or dyspnea were more likely frequent ED visitors during the COVID-19 pandemic. To reduce the risk of acquiring COVID-19, it is important to utilize territorial healthcare or telehealth to avoid inappropriate ED visits for patients with a low level of risk or chronic disease.

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Section: Introductionmentioning

confidence: 99%

Impact of the COVID-19 Pandemic on Healthcare-Seeking Behaviors among Frequent Emergency Department Users: A Cohort Study

Chou

Yen

Chu

et al. 2021

IJERPH

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“…Indeed, there is a significant number of existing corpora, datasets and resources available in English. Yet, we observe an increasing number of publications dedicated to other languages and a greater variety of languages: Arabic [ 20 ], Chinese [ 21 22 23 24 25 26 ], Croatian [ 27 ], Finnish [ 28 , 29 ], French [ 30 , 31 ], German [ 32 33 34 ], Hebrew [ 35 ], Italian [ 36 37 38 ], Japanese [ 39 , 40 ], Korean [ 41 , 42 ], Norwegian [ 43 ], Portuguese [ 44 ], Spanish [ 45 46 47 48 ], Swedish [ 49 ], and Turkish [ 28 ]. Overall, we believe that the trend observed in previous years is continuing.…”

Section: Current Trends In Biomedical Nlpmentioning

confidence: 99%

Year 2020 (with COVID): Observation of Scientific Literature on Clinical Natural Language Processing

Grabar

Grouin

2021

Yearb Med Inform

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Summary Objectives: To analyze the content of publications within the medical NLP domain in 2020. Methods: Automatic and manual preselection of publications to be reviewed, and selection of the best NLP papers of the year. Analysis of the important issues. Results: Three best papers have been selected in 2020. We also propose an analysis of the content of the NLP publications in 2020, all topics included. Conclusion: The two main issues addressed in 2020 are related to the investigation of COVID-related questions and to the further adaptation and use of transformer models. Besides, the trends from the past years continue, such as diversification of languages processed and use of information from social networks

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“…Forecasting patients' LOS is a subject widely studied in the literature through different methods and applications. Some studies use simple methods such as linear regression analysis (e.g., [6]), while more current studies use artificial intelligence techniques based on machine learning and deep learning (e.g., [7]). Literature review studies devoted to forecasting patients' LOS in hospitals mainly analyze the adult population.…”

Section: Introductionmentioning

confidence: 99%

Forecasting the length-of-stay of pediatric patients in hospitals: a scoping review

Medeiros

Fogliatto

Rocha

et al. 2021

BMC Health Serv Res

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Background Healthcare management faces complex challenges in allocating hospital resources, and predicting patients’ length-of-stay (LOS) is critical in effectively managing those resources. This work aims to map approaches used to forecast the LOS of Pediatric Patients in Hospitals (LOS–P) and patients’ populations and environments used to develop the models. Methods Using the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) methodology, we performed a scoping review that identified 28 studies and analyzed them. The search was conducted on four databases (Science Direct, Scopus, Web of Science, and Medline). The identification of relevant studies was structured around three axes related to the research questions: (i) forecast models, (ii) hospital length-of-stay, and (iii) pediatric patients. Two authors carried out all stages to ensure the reliability of the review process. Articles that passed the initial screening had their data charted on a spreadsheet. Methods reported in the literature were classified according to the stage in which they are used in the modeling process: (i) pre-processing of data, (ii) variable selection, and (iii) cross-validation. Results Forecasting models are most often applied to newborn patients and, consequently, in neonatal intensive care units. Regression analysis is the most widely used modeling approach; techniques associated with Machine Learning are still incipient and primarily used in emergency departments to model patients in specific situations. Conclusions The studies’ main benefits include informing family members about the patient’s expected discharge date and enabling hospital resources’ allocation and planning. Main research gaps are associated with the lack of generalization of forecasting models and limited reported applicability in hospital management. This study also provides a practical guide to LOS–P forecasting methods and a future research agenda.

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Early short-term prediction of emergency department length of stay using natural language processing for low-acuity outpatients

Cited by 14 publications

References 14 publications

Impact of the COVID-19 Pandemic on Healthcare-Seeking Behaviors among Frequent Emergency Department Users: A Cohort Study

Impact of the COVID-19 Pandemic on Healthcare-Seeking Behaviors among Frequent Emergency Department Users: A Cohort Study

Year 2020 (with COVID): Observation of Scientific Literature on Clinical Natural Language Processing

Forecasting the length-of-stay of pediatric patients in hospitals: a scoping review

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