Comparative Analysis of Three Predictive Models of Performance Indicators with Results-Based Management: Cancer Data Statistics in a National Institute of Health

SIMPLE SUMMARY: Statistical predictive models using one of the most important strategies, known as results-based management (RBM), are relevant for improving the quality of medical services and could be used with cancer data statistics to monitor and evaluate children with cancer. We provided a comparative analysis of three predictive models that are considered robust in the literature. We also provided a tool to identify new medical cases that confirm a forecast increase or decrease in certain performance indicators and can convert data into useful information for decision-making; we propose to apply this approach to cancer data statistics at one of the National Institutes of Health in Mexico City. Our findings show that from an RBM-based perspective, predictive models are a valid and reliable instrument to forecast medical performance indicator results and can be applied to monitor and evaluate children with cancer. ABSTRACT: 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 showing p < 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.