Peerawong Thanarpan, Chaichulee Chaichulee, Sangsupawanich Pasuree
Department of Clinical Research and Medical Data Science, Faculty of Medicine, Prince of Songkla University, Songkhla, THA.
Department of Radiology, Faculty of Medicine, Prince of Songkla University, Songkhla, THA.
Cureus. 2024 Oct 31;16(10):e72752. doi: 10.7759/cureus.72752. eCollection 2024 Oct.
The purpose is to predict the volume of patients treated daily with radiotherapy using the autoregressive integrated moving average (ARIMA) model.
In this retrospective study, data from the billing records detailing daily radiotherapy treatment sessions were extracted from the Hospital Information System and analyzed. The study included all patients treated from January 2004 to December 2022. The analysis was divided into two parts: First, the data were summarized using descriptive statistics. Second, time series forecasting with the implementation of an ARIMA model for estimating patient volumes. For the ARIMA modeling process, the Akaike Information Criterion (AIC) was used for classical model optimization. The Mean Absolute Percentage Error (MAPE) was used for evaluating between different models. Residual analysis was performed in each model using the Ljung-Box test, Jarque-Bera test, and heteroskedasticity test to identify autocorrelation, normal distribution, and variances that could undermine the reliability of the model.
A total of 895,808 radiotherapy sessions were included in the study. The median number of radiotherapy sessions per day was 181 (150, 205). A clear transition to more modern radiotherapy equipment, particularly the Truebeam accelerator, was observed, indicating a growing dependency on advanced techniques such as volumetric-modulated arc therapy (VMAT), stereotactic body radiation therapy (SBRT), and stereotactic radiosurgery (SRS). The best ARIMA model predicted an increase in demand, projecting an average daily patient volume of 279.40 by 2030.
The study highlights the need for advanced forecasting methodologies in healthcare resource planning and emphasizes the importance of considering environmental and external factors for effective and accurate resource allocation strategies.
使用自回归积分移动平均（ARIMA）模型预测每日接受放射治疗的患者数量。
在这项回顾性研究中，从医院信息系统中提取了详细记录每日放射治疗疗程的计费数据并进行分析。该研究纳入了2004年1月至2022年12月期间接受治疗的所有患者。分析分为两个部分：第一，使用描述性统计对数据进行总结。第二，通过实施ARIMA模型进行时间序列预测，以估计患者数量。在ARIMA建模过程中，使用赤池信息准则（AIC）进行经典模型优化。使用平均绝对百分比误差（MAPE）评估不同模型之间的差异。在每个模型中使用Ljung-Box检验、Jarque-Bera检验和异方差检验进行残差分析，以识别可能破坏模型可靠性的自相关、正态分布和方差。
该研究共纳入895,808个放射治疗疗程。每日放射治疗疗程的中位数为181（150, 205）。观察到向更现代的放射治疗设备，特别是Truebeam加速器的明显转变，这表明对容积调强弧形放疗（VMAT）、立体定向体部放疗（SBRT）和立体定向放射外科（SRS）等先进技术的依赖日益增加。最佳ARIMA模型预测需求将增加，预计到2030年每日平均患者数量将达到279.40。
该研究强调了在医疗资源规划中采用先进预测方法的必要性，并强调了考虑环境和外部因素对于制定有效且准确的资源分配策略的重要性。
