Probability-based models are developed using information from a variety of datasets to predict daily surgical volumes weeks in advance. The quest was motivated by the need to make real-time adjustments to staff capacity and reallocation of the operating room block time based on predicted future demand. We test the notion that more data always leads to better predictions. Four probabilistic prediction models are presented, each parameterized based on real data and information from different sources. We hypothesize that the accuracy of the prediction improves by incorporating additional information. Models are tested for a surgical service at a large hospital using data of 20 months (January 19, 2015–August 31, 2016). We find that incorporating additional information may not improve prediction accuracy if that information is prone to data errors. However, deploying analytical data treatment to ameliorate these errors leads to better predictions. We also compare the predictive ability of the probability-based models to neural network–based models and find that the neural network models do not outperform simpler models. Managers should critically review the accuracy of the data used in decision-making. While a greater amount of inherently error-free information is the best, analytics can enhance the utility of error-prone data.

中文翻译：

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使用提供者未来可用性的概率估计来预测每日手术量

使用来自各种数据集的信息开发基于概率的模型，以提前数周预测每日手术量。该任务的动机是需要根据预测的未来需求实时调整员工容量和重新分配手术室阻塞时间。我们测试了更多数据总能带来更好预测的概念。提出了四个概率预测模型，每个模型都基于来自不同来源的真实数据和信息进行参数化。我们假设通过结合额外的信息来提高预测的准确性。使用 20 个月（2015 年 1 月 19 日至 2016 年 8 月 31 日）的数据在一家大型医院对模型进行了外科服务测试。我们发现，如果该信息容易出现数据错误，则合并附加信息可能不会提高预测准确性。然而，部署分析数据处理来改善这些错误会导致更好的预测。我们还将基于概率的模型与基于神经网络的模型的预测能力进行了比较，发现神经网络模型的性能并不优于更简单的模型。管理人员应严格审查决策中使用的数据的准确性。虽然更多的本质上无错误的信息是最好的，但分析可以增强容易出错的数据的效用。