Predictive models play a crucial role in RBMs to analyze performance indicator results to manage unexpected events and make timely decisions to resolve them. Their use in Mexico is deficient, and monitoring and evaluation are among the weakest pillars of the model. In response to these needs, the aim of this study was to perform a comparative analysis of three predictive models to analyze 10 medical performance indicators and cancer data related to children with cancer. To accomplish these purposes, a comparative and retrospective study with nonprobabilistic convenience sampling was conducted. The predictive models were exponential smoothing, autoregressive integrated moving average, and linear regression. The lowest mean absolute error was used to identify the best model. Linear regression performed best regarding nine of the ten indicators, with seven showingp< 0.05. Three of their assumptions were checked using the Shapiro–Wilk, Cook’s distance, and Breusch–Pagan tests. Predictive models with RBM are a valid and relevant instrument for monitoring and evaluating performance indicator results to support forecasting and decision-making based on evidence and must be promoted for use with cancer data statistics. The place numbers obtained by cancer disease inside the main causes of death, morbidity and hospital outpatients in a National Institute of Health were presented as evidence of the importance of implementing performance indicators associated with children with cancer.
预测模型在基于结果的管理中发挥着关键作用，通过分析绩效指标结果来应对突发事件并及时制定解决方案。然而在墨西哥，预测模型的应用尚不充分，监测与评估是该模型中最薄弱的环节之一。为应对这些需求，本研究旨在通过比较三种预测模型，分析10项医疗绩效指标及儿童癌症相关数据。研究采用非概率便利抽样的回顾性比较方法，选取指数平滑法、自回归积分滑动平均模型和线性回归三种预测模型，并以最小平均绝对误差作为模型优劣的评判标准。结果显示，线性回归在十项指标中的九项表现最佳，其中七项指标具有统计学意义（p<0.05）。研究通过夏皮罗-威尔克检验、库克距离检验和布鲁奇-帕甘检验验证了模型的三项基本假设。研究表明，基于结果管理的预测模型是监测评估绩效指标结果的有效工具，能为循证预测和决策提供支持，值得在癌症数据统计中推广应用。研究还以某国立卫生研究院的统计数据为例，展示了癌症在死亡原因、发病率和门诊量中的排序，这为实施儿童癌症相关绩效指标的重要性提供了实证依据。
肿瘤(癌症)患者之家