Department of Research, Hospital Regional de Alta Especialidad del Bajío, León, Guanajuato, México.
Programa de Biotecnología, Universidad de Guanajuato, Celaya, Guanajuato, México.
PLoS One. 2021 Mar 8;16(3):e0248277. doi: 10.1371/journal.pone.0248277. eCollection 2021.
Data on hospital discharges can be used as a valuable instrument for hospital planning and management. The quantification of deaths can be considered a measure of the effectiveness of hospital intervention, and a high percentage of hospital discharges due to death can be associated with deficiencies in the quality of hospital care.
To determine the overall percentage of hospital discharges due to death in a Mexican tertiary care hospital from its opening, to describe the characteristics of the time series generated from the monthly percentage of hospital discharges due to death and to make and evaluate predictions.
This was a retrospective study involving the medical records of 81,083 patients who were discharged from a tertiary care hospital from April 2007 to December 2019 (first 153 months of operation). The records of the first 129 months (April 2007 to December 2017) were used for the analysis and construction of the models (training dataset). In addition, the records of the last 24 months (January 2018 to December 2019) were used to evaluate the predictions made (test dataset). Structural change was identified (Chow test), ARIMA models were adjusted, predictions were estimated with and without considering the structural change, and predictions were evaluated using error indices (MAE, RMSE, MAPE, and MASE).
The total percentage of discharges due to death was 3.41%. A structural change was observed in the time series (March 2009, p>0.001), and ARIMA(0,0,0)(1,1,2)12 with drift models were adjusted with and without consideration of the structural change. The error metrics favored the model that did not consider the structural change (MAE = 0.63, RMSE = 0.81, MAPE = 25.89%, and MASE = 0.65).
Our study suggests that the ARIMA models are an adequate tool for future monitoring of the monthly percentage of hospital discharges due to death, allowing us to detect observations that depart from the described trend and identify future structural changes.
医院出院数据可用作医院规划和管理的有价值工具。死亡数量的量化可以被视为医院干预效果的衡量标准，而因死亡而导致的高比例医院出院可能与医院护理质量的缺陷有关。
确定一家墨西哥三级保健医院自开业以来因死亡而导致的医院出院的总体百分比，描述从每月因死亡而导致的医院出院百分比生成的时间序列的特征，并进行预测。
这是一项回顾性研究，涉及 2007 年 4 月至 2019 年 12 月（运营的前 153 个月）期间从一家三级保健医院出院的 81083 名患者的病历。使用前 129 个月（2007 年 4 月至 2017 年 12 月）的记录进行分析和模型构建（训练数据集）。此外，使用最后 24 个月（2018 年 1 月至 2019 年 12 月）的记录来评估所做的预测（测试数据集）。识别结构变化（Chow 检验），调整 ARIMA 模型，在考虑和不考虑结构变化的情况下估计预测，并使用误差指标（MAE、RMSE、MAPE 和 MASE）评估预测。
因死亡而导致的出院总数的百分比为 3.41%。在时间序列中观察到结构变化（2009 年 3 月，p>0.001），并且调整了带有和不带有结构变化的 ARIMA(0,0,0)(1,1,2)12 带漂移模型。误差指标倾向于不考虑结构变化的模型（MAE=0.63、RMSE=0.81、MAPE=25.89%和 MASE=0.65）。
我们的研究表明，ARIMA 模型是未来监测因死亡而导致的医院出院每月百分比的一种合适工具，使我们能够检测到偏离描述趋势的观察结果，并识别未来的结构变化。
