Background

Motor vehicle accidents (MVA) represent a significant burden on health systems globally. Tens of thousands of people are injured in Australia every year and may experience significant disability. Associated economic costs are substantial. There is little literature on the health service utilization patterns of MVA patients. To fill this gap, this study has been designed to investigate temporal patterns of psychology and physiotherapy service utilization following transport-related injuries.

Method

De-identified compensation data was provided by the Australian Transport Accident Commission. Utilization of physiotherapy and psychology services was analysed. The datasets contained 788 psychology and 3115 physiotherapy claimants and 22,522 and 118,453 episodes of service utilization, respectively. 582 claimants used both services, and their data were preprocessed to generate multidimensional time series. Time series clustering was applied using a mixture of hidden Markov models to identify the main distinct patterns of service utilization. Combinations of hidden states and clusters were evaluated and optimized using the Bayesian information criterion and interpretability. Cluster membership was further investigated using static covariates and multinomial logistic regression, and classified using high-performing classifiers (extreme gradient boosting machine, random forest and support vector machine) with 5-fold cross-validation.

Results

Four clusters of claimants were obtained from the clustering of the time series of service utilization. Service volumes and costs increased progressively from clusters 1 to 4. Membership of cluster 1 was positively associated with nerve damage and negatively associated with severe ABI and spinal injuries. Cluster 3 was positively associated with severe ABI, brain/head injury and psychiatric injury. Cluster 4 was positively associated with internal injuries. The classifiers were capable of classifying cluster membership with moderate to strong performance (AUC: 0.62–0.96).

Conclusion

The available time series of post-accident psychology and physiotherapy service utilization were coalesced into four clusters that were clearly distinct in terms of patterns of utilization. In addition, pre-treatment covariates allowed prediction of a claimant's post-accident service utilization with reasonable accuracy. Such results can be useful for a range of decision-making processes, including the design of interventions aimed at improving claimant care and recovery.

中文翻译：

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用于时间序列分析和多种医疗服务参与分类的多通道混合模型：交通事故后心理和理疗利用模式的应用

背景

机动车事故 (MVA) 是全球卫生系统的重大负担。在澳大利亚，每年有数万人受伤，并可能出现严重的残疾。相关的经济成本是巨大的。关于 MVA 患者卫生服务利用模式的文献很少。为了填补这一空白，本研究旨在调查交通相关伤害后心理和物理治疗服务利用的时间模式。

方法

去识别化的赔偿数据由澳大利亚交通事故委员会提供。分析了物理治疗和心理服务的利用。这些数据集分别包含 788 名心理学和 3115 名理疗索赔人以及 22,522 和 118,453 次服务利用事件。582 名索赔人使用了这两种服务，他们的数据经过预处理以生成多维时间序列。使用混合隐马尔可夫模型应用时间序列聚类来识别服务利用的主要不同模式。使用贝叶斯信息标准和可解释性来评估和优化隐藏状态和集群的组合。使用静态协变量和多项逻辑回归进一步研究了集群成员资格，

结果

从服务使用时间序列的聚类中获得了四个索赔人聚类。服务量和成本从集群 1 逐渐增加到 4。集群 1 的成员身份与神经损伤呈正相关，与严重的 ABI 和脊柱损伤呈负相关。第 3 组与严重的 ABI、脑/头部损伤和精神损伤呈正相关。第 4 组与内伤呈正相关。分类器能够以中等至强的性能（AUC：0.62-0.96）对集群成员进行分类。

结论

事故后心理和物理治疗服务利用的可用时间序列合并为四个集群，这些集群在使用模式方面明显不同。此外，预处理协变量允许以合理的准确度预测索赔人的事故后服务利用率。这样的结果可用于一系列决策过程，包括旨在改善索赔人护理和康复的干预措施的设计。