Risk factors for cancellation after dispatch of rapid response cars for prehospital emergency care: a single‐center, case–control study

Inoue, Juri; Hirano, Yohei; Fukumoto, Yuichi; Kudo, Taku; Usami, R.; Kondo, Yutaka; Matsuda, Shigeru; Tanaka, Hiroshi

doi:10.1002/ams2.684

Cited by 2 publications

(3 citation statements)

References 21 publications

(33 reference statements)

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“…The following information were retrieved from the database: date; age, and sex of the patient; reason for RRC request; location of the scene; occurrence of cancellation; reason for cancellation; and the hospital to which the patient was transported. The details of the database and rapid car system at our institute have been described previously 10) .…”

Section: Data Sources and Ethical Considerationsmentioning

confidence: 99%

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Development and Validation of a Machine Learning Model to Predict Post-dispatch Cancellation of Physician-staffed Rapid Car

KAWASAKI,

HIRANO,

KONDO

et al. 2024

Juntendo Medical Journal

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Objectives: This study aimed to develop and validate a machine learning prediction model for post-dispatch cancellation of physician-staffed rapid car.Materials: Data were extracted from the physician-staffed rapid response car database at our Hospital between April 2017 and March 2019. Methods: After obtaining 2019 cases, we divided the dataset into a training set for developing the model and a test set for validation using stratified random sampling with an 8 : 2 allocation ratio. We selected random forest as the machine-learning classifier. The outcome was the post-dispatch cancellation of a rapid car. The model was trained using predictor variables, including 18 different reasons for rapid car request, age and gender of a patient, date (month), and distance from the hospital.Results: This machine learning model predicted the occurrence of post-dispatch cancellation of rapid cars with an accuracy of 75.5% [95% confidence interval (CI): 71.0-79.6], sensitivity of 81.5% (CI: 75.0-86.9), specificity of 70.8% (CI: 64.4-76.6), and an area under the receiver operating characteristic value of 0.83 (CI: 0.79-0.87). The important features were distance from the hospital to the scene, age, suspicion of non-witnessed cardiac arrest, farthest geographic area, and date (months). Conclusions:We developed a favorable machine learning model to predict post-dispatch cancellation of rapid cars in a local district. This study suggests the potential of machine-learning models in improving the efficiency of dispatching physicians outside hospitals.

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Section: Data Sources and Ethical Considerationsmentioning

confidence: 99%

“…Few reports [7][8][9][10] have identified the factors responsible for post-dispatch cancellation. Understanding these factors is crucial as it would contribute to optimization of the dispatch criteria for prehospital emergency services.…”

Section: Introductionmentioning

confidence: 99%

Development and Validation of a Machine Learning Model to Predict Post-dispatch Cancellation of Physician-staffed Rapid Car

KAWASAKI,

HIRANO,

KONDO

et al. 2024

Juntendo Medical Journal

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“…Studies in the field of disaster medicine and pre- hospital emergency medicine involves those on the establishment of business continuing plans (BCPs) for disasters. Other studies include research on the effectiveness of Rapid Response Car 5) , the establishment of an emergency medical care system in large theme parks, and research on the effectiveness of electronic triage during disasters, which was adopted by CREST.…”

Section: Our Various Research Areas (From Clinical Research To Basic ...mentioning

confidence: 99%

Fudan Zenshin, Kyumeikyukyu ; <sub>~</sub>Now JIN again<sub>~</sub>

Tanaka

2022

Juntendo Medical Journal

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Summary I graduated from Osaka University in 1982 and joined the Department of Traumatology, Osaka University Medical School. Patients with severe injuries and illnesses were brought in every day. Staff brushed up their skills on site, taught each other, and engaged in friendly competition for research. We had many frustrating moments when we could not save lives. Since then, the needs of emergency medicine have changed, and the scope of practice of emergency physicians has expanded to include pre-hospital emergency care, primary health care, intensive care, and disaster medicine. I was transferred to Juntendo University Urayasu Hospital in September 2007. Soon after my assignment, Urayasu Hospital was in the spotlight due to the emergency hospitalization of the All-Japan soccer coach and the Chinese frozen dumpling incident. It is said that emergency medicine is a mirror of society. I myself have experienced many disasters and incidents. It has been 15 years since I was assigned to this hospital, and I have 62 colleagues at Urayasu Hospital. They have all acquired various medical specialties, and some are emergency medicine specialists. In 2019, we hosted the 47th Annual Meeting of the Japanese Association for Acute Medicine. The theme of the conference is “Fudan Zenshin, Kyumeikyukyu (constant advancement, emergency medical services)”. Emergency medical care is the starting point of “medicine” and is the ultimate source of life preservation for all citizens. We emergency physicians will continue to provide lifesaving medical care to patients without giving up until the very end, to keep the light of life from going out.

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Risk factors for cancellation after dispatch of rapid response cars for prehospital emergency care: a single‐center, case–control study

Cited by 2 publications

References 21 publications

Development and Validation of a Machine Learning Model to Predict Post-dispatch Cancellation of Physician-staffed Rapid Car

Development and Validation of a Machine Learning Model to Predict Post-dispatch Cancellation of Physician-staffed Rapid Car

Fudan Zenshin, Kyumeikyukyu ; <sub>~</sub>Now JIN again<sub>~</sub>

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