Hospitals commonly project demand for their services by combining their historical share of regional demand with forecasts of total regional demand. Hospital-specific forecasts of demand that provide prediction intervals, rather than point estimates, may facilitate better managerial decisions, especially when demand overage and underage are associated with high, asymmetric costs. Regional forecasts of patient demand are commonly available as a Poisson random variable, e.g., for the number of people requiring hospitalization due to an epidemic such as COVID-19. However, even in this common setting, no probabilistic, consistent, computationally tractable forecast is available for the fraction of patients in a region that a particular institution should expect. We introduce such a forecast, DICE (Demand Intervals from Consistent Estimators). We describe its development and deployment at an academic medical center in California during the `second wave' of COVID-19 in the Unite States. We show that DICE is consistent under mild assumptions and suitable for use with perfect, biased, unbiased regional forecasts. We evaluate its performance on empirical data from a large academic medical center as well as on synthetic data.
翻译:医院对医院服务需求的一般预测通常将区域需求的历史份额与对区域总需求的预测结合起来,对医院的需求作出具体预测,提供预测间隔,而不是点估计,可能有助于作出更好的管理决定,特别是当需求过大和未成年人与高、不对称费用相关时,区域对病人需求的预测通常作为Poisson随机变量提供,例如,由于COVID-19等流行病而需要住院治疗的人数。然而,即使在这一共同背景下,对某一机构应当预期的区域的病人的一小部分也没有概率、一致性、可计算可移动的预测。我们采用了这样的预测,即DICE(来自一致的模拟器的Demand Intervals)。我们描述在加利福尼亚州COVID-19-19“第二波”期间病人需求的开发和部署情况。我们显示,DICE始终处于温和的假设之下,适合使用完美、偏差、公正的区域预测。我们评价它从一个大型学术医疗中心获得的经验数据以及合成数据的绩效。