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Predicting Unscheduled Emergency Department Return Visits Among Older Adults: Population-Based Retrospective Study

BACKGROUND: Unscheduled emergency department return visits (EDRVs) are key indicators for monitoring the quality of emergency medical care. A high return rate implies that the medical services provided by the emergency department (ED) failed to achieve the expected results of accurate diagnosis and...

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Autores principales: Chen, Rai-Fu, Cheng, Kuei-Chen, Lin, Yu-Yin, Chang, I-Chiu, Tsai, Cheng-Han
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8367131/
https://www.ncbi.nlm.nih.gov/pubmed/34319244
http://dx.doi.org/10.2196/22491
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author Chen, Rai-Fu
Cheng, Kuei-Chen
Lin, Yu-Yin
Chang, I-Chiu
Tsai, Cheng-Han
author_facet Chen, Rai-Fu
Cheng, Kuei-Chen
Lin, Yu-Yin
Chang, I-Chiu
Tsai, Cheng-Han
author_sort Chen, Rai-Fu
collection PubMed
description BACKGROUND: Unscheduled emergency department return visits (EDRVs) are key indicators for monitoring the quality of emergency medical care. A high return rate implies that the medical services provided by the emergency department (ED) failed to achieve the expected results of accurate diagnosis and effective treatment. Older adults are more susceptible to diseases and comorbidities than younger adults, and they exhibit unique and complex clinical characteristics that increase the difficulty of clinical diagnosis and treatment. Older adults also use more emergency medical resources than people in other age groups. Many studies have reviewed the causes of EDRVs among general ED patients; however, few have focused on older adults, although this is the age group with the highest rate of EDRVs. OBJECTIVE: This aim of this study is to establish a model for predicting unscheduled EDRVs within a 72-hour period among patients aged 65 years and older. In addition, we aim to investigate the effects of the influencing factors on their unscheduled EDRVs. METHODS: We used stratified and randomized data from Taiwan’s National Health Insurance Research Database and applied data mining techniques to construct a prediction model consisting of patient, disease, hospital, and physician characteristics. Records of ED visits by patients aged 65 years and older from 1996 to 2010 in the National Health Insurance Research Database were selected, and the final sample size was 49,252 records. RESULTS: The decision tree of the prediction model achieved an acceptable overall accuracy of 76.80%. Economic status, chronic illness, and length of stay in the ED were the top three variables influencing unscheduled EDRVs. Those who stayed in the ED overnight or longer on their first visit were less likely to return. This study confirms the results of prior studies, which found that economically underprivileged older adults with chronic illness and comorbidities were more likely to return to the ED. CONCLUSIONS: Medical institutions can use our prediction model as a reference to improve medical management and clinical services by understanding the reasons for 72-hour unscheduled EDRVs in older adult patients. A possible solution is to create mechanisms that incorporate our prediction model and develop a support system with customized medical education for older patients and their family members before discharge. Meanwhile, a reasonably longer length of stay in the ED may help evaluate treatments and guide prognosis for older adult patients, and it may further reduce the rate of their unscheduled EDRVs.
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spelling pubmed-83671312021-08-24 Predicting Unscheduled Emergency Department Return Visits Among Older Adults: Population-Based Retrospective Study Chen, Rai-Fu Cheng, Kuei-Chen Lin, Yu-Yin Chang, I-Chiu Tsai, Cheng-Han JMIR Med Inform Original Paper BACKGROUND: Unscheduled emergency department return visits (EDRVs) are key indicators for monitoring the quality of emergency medical care. A high return rate implies that the medical services provided by the emergency department (ED) failed to achieve the expected results of accurate diagnosis and effective treatment. Older adults are more susceptible to diseases and comorbidities than younger adults, and they exhibit unique and complex clinical characteristics that increase the difficulty of clinical diagnosis and treatment. Older adults also use more emergency medical resources than people in other age groups. Many studies have reviewed the causes of EDRVs among general ED patients; however, few have focused on older adults, although this is the age group with the highest rate of EDRVs. OBJECTIVE: This aim of this study is to establish a model for predicting unscheduled EDRVs within a 72-hour period among patients aged 65 years and older. In addition, we aim to investigate the effects of the influencing factors on their unscheduled EDRVs. METHODS: We used stratified and randomized data from Taiwan’s National Health Insurance Research Database and applied data mining techniques to construct a prediction model consisting of patient, disease, hospital, and physician characteristics. Records of ED visits by patients aged 65 years and older from 1996 to 2010 in the National Health Insurance Research Database were selected, and the final sample size was 49,252 records. RESULTS: The decision tree of the prediction model achieved an acceptable overall accuracy of 76.80%. Economic status, chronic illness, and length of stay in the ED were the top three variables influencing unscheduled EDRVs. Those who stayed in the ED overnight or longer on their first visit were less likely to return. This study confirms the results of prior studies, which found that economically underprivileged older adults with chronic illness and comorbidities were more likely to return to the ED. CONCLUSIONS: Medical institutions can use our prediction model as a reference to improve medical management and clinical services by understanding the reasons for 72-hour unscheduled EDRVs in older adult patients. A possible solution is to create mechanisms that incorporate our prediction model and develop a support system with customized medical education for older patients and their family members before discharge. Meanwhile, a reasonably longer length of stay in the ED may help evaluate treatments and guide prognosis for older adult patients, and it may further reduce the rate of their unscheduled EDRVs. JMIR Publications 2021-07-28 /pmc/articles/PMC8367131/ /pubmed/34319244 http://dx.doi.org/10.2196/22491 Text en ©Rai-Fu Chen, Kuei-Chen Cheng, Yu-Yin Lin, I-Chiu Chang, Cheng-Han Tsai. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 28.07.2021. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Medical Informatics, is properly cited. The complete bibliographic information, a link to the original publication on https://medinform.jmir.org/, as well as this copyright and license information must be included.
spellingShingle Original Paper
Chen, Rai-Fu
Cheng, Kuei-Chen
Lin, Yu-Yin
Chang, I-Chiu
Tsai, Cheng-Han
Predicting Unscheduled Emergency Department Return Visits Among Older Adults: Population-Based Retrospective Study
title Predicting Unscheduled Emergency Department Return Visits Among Older Adults: Population-Based Retrospective Study
title_full Predicting Unscheduled Emergency Department Return Visits Among Older Adults: Population-Based Retrospective Study
title_fullStr Predicting Unscheduled Emergency Department Return Visits Among Older Adults: Population-Based Retrospective Study
title_full_unstemmed Predicting Unscheduled Emergency Department Return Visits Among Older Adults: Population-Based Retrospective Study
title_short Predicting Unscheduled Emergency Department Return Visits Among Older Adults: Population-Based Retrospective Study
title_sort predicting unscheduled emergency department return visits among older adults: population-based retrospective study
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8367131/
https://www.ncbi.nlm.nih.gov/pubmed/34319244
http://dx.doi.org/10.2196/22491
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