Objective

Pre-hospital emergency medical services use clinical decision support systems (CDSS) to triage calls. Call handlers often supplement this by making free text notes covering key incident information. We investigate whether machine learning approaches using features from such free text notes can improve prediction of unconscious patients who require conveyance.

Materials and methods

We analysed a subset of all London Ambulance Service calls that were triaged through the Medical Priority Dispatch System (MPDS) as involving an unconscious or fainting patient in 2018. We use and compare two machine learning algorithms: random forest (RF) and gradient boosting machine (GBM). For each incident, we predict whether the patient will be conveyed to a hospital emergency department or equivalent using as features 1) the MPDS code, 2) the free text notes and 3) the two together. We evaluate model performance using the area under the curve (AUC) metric. Given the imbalance of outcomes (patient conveyed 71 %, not conveyed 29 %), we also consider sensitivity and specificity.

Results

Using only the MPDS code resulted in an AUC of 0.57. Using the text notes gave an improved AUC score of 0.63 and combining the two gave an AUC score of 0.64 (scores were similar for RF and GBM). GBM models scored better on sensitivity (0.93 vs 0.62 for RF in the combined model), but specificity was lower (0.17 vs. 0.56 for RF in the combined model).

Conclusions

Using information contained in the free text notes made by call handlers in combination with MPDS improves prediction of unconscious and fainting patients requiring conveyance to a hospital emergency department (or equivalent) when compared with machine learning models using MPDS codes only. This suggests there is some useful information in unstructured data captured by emergency call handlers that complements MPDS codes. Quantifying this gain can help inform emergency medical service policy when evaluating the decision to expand or augment existing CDSS.

中文翻译：

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使用紧急呼叫处理程序免费文本注释增强对病人运送的预测，以通知伦敦救护车服务公司无意识和昏厥的事件。

目的

院前紧急医疗服务使用临床决策支持系统（CDSS）对呼叫进行分类。呼叫处理程序通常通过提供覆盖关键事件信息的自由文本注释来补充此内容。我们调查了使用来自此类自由文本注释的功能的机器学习方法是否可以改善需要运输工具的昏迷患者的预测。

材料和方法

我们分析了2018年通过医疗优先分派系统（MPDS）进行分类的所有伦敦救护车服务呼叫的子集，其中涉及昏迷或昏厥的患者。我们使用并比较了两种机器学习算法：随机森林（RF）和梯度增强机器（GBM）。对于每个事件，我们使用以下特征（1）MPDS代码，2）自由文本注释和3）两者一起预测患者是否会被转送到医院急诊科或同等医院。我们使用曲线下面积（AUC）指标评估模型性能。鉴于结果的不平衡（患者占71％，未占29％），我们还考虑了敏感性和特异性。

结果

仅使用MPDS代码得出的AUC为0.57。使用文本注释可以得到0.63的改善的AUC分数，将两者结合可以得到0.64的AUC分数（RF和GBM的分数相似）。GBM模型的灵敏度得分较高（组合模型中RF的0.93比0.62），但特异性较低（组合模型中RF的0.17相对0.56）。

结论

与仅使用MPDS代码的机器学习模型相比，将呼叫处理程序提供的自由文本注释中包含的信息与MPDS结合使用可以改善对昏迷和昏厥患者的预测，这些患者需要转移到医院急诊科（或同等学历）。这表明紧急呼叫处理程序捕获的非结构化数据中有一些有用的信息，可以补充MPDS代码。量化此收益可以在评估扩展或扩充现有CDSS的决策时帮助告知紧急医疗服务策略。