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首页> 外文期刊>International journal of information systems in the service sector >Predicting Ambulance Diverson
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Predicting Ambulance Diverson

机译：预测救护车转移

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摘要

The high utilization level of emergency departments in hospitals across the United States has resulted in the serious and persistent problem of ambulance diversion. This problem is magnified by the cascading effect it has on neighboring hospitals, delays in emergency care, and the potentialfor patients 'clinical deterioration. We provide a predictive tool that would give advance warning to hospitals of the impending likelihood of diversion. We hope that with a predictive instrument, such as the one described in this article, hospitals can take preventive or mitigating actions. The proposed model, which uses logistic and multinomial regression, is evaluated using real data from the Emergency Management System (EM Systems) and 911 call data from Firstwatch~®for the Metropolitan Ambulance Services Trust (MAST) of Kansas City, Missouri. The information in these systems that was significant in predicting diversion includes recent 911 calls, season, day of the week, and time of day. The model illustrates the feasibility of predicting the probability of impending diversion using available information. We strongly recommend that other locations, nationwide and abroad, develop and use similar models for predicting diversion.
机译：美国各地医院急诊室的高利用率导致了严重而持续的救护车转移问题。它对邻近医院的连锁反应，急诊护理的延误以及患者临床恶化的可能性，使这一问题更加严重。我们提供了一种预测工具，可以提前警告医院可能发生的转移用途。我们希望借助一种预测手段（如本文所述的手段），医院可以采取预防或缓解措施。所提出的模型使用了逻辑和多项式回归，并使用了来自应急管理系统（EM Systems）的真实数据和来自密苏里州堪萨斯市大都会救护服务信托（MAST）的Firstwatch®的911呼叫数据进行了评估。这些系统中对转移预测至关重要的信息包括最近的911电话，季节，星期几和一天中的时间。该模型说明了使用可用信息预测即将发生转移的可能性的可行性。我们强烈建议国内外其他地区开发和使用类似的模型来预测转移。

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