Data-Driven Guidance for Timing Repeated Inpatient Laboratory Tests

这项研究的长期目标是开发一种数据驱动的工具来指导临床决策 重复一次住院化验。目前的提议是尝试这一目标的最高风险因素，即 是开发机器学习方法来估计对给定测试运行给定测试的最佳下一次时间 考虑到患者的临床病史、所进行的任何治疗以及当前的临床状况。 临床医生经常高估一项测试的重复频率，或者为了方便起见，他们会安排测试 每天都要重复。这浪费了资源，增加了护理成本。同样，也可以很容易地 低估了重复检查的频率，这可能会导致不理想的护理，并增加发病率 和死亡率。多年来，已经尝试了许多干预措施来减少反复发作的频率 测试，但它们通常使用主观的、专家特定的fi规则，这些规则为某些特定的 临床场景。这些尝试值得称赞，但它们是解决问题的相当直截了当的工具，因为 它们不能适应特殊的fiC和患者的病理生理状态和治疗方案的不同需要， 而且它们不会影响运行不够频繁的测试。 提供数据驱动的定时指导的关键是从数据中估计SPECIfic的速率 观察到的结果将变得陈旧，这意味着它已经足够陈旧，估计的当前值太不确定，无法 用于决策。从数学上讲，对测试重复进行排序的最佳时间恰好是最 最近的价值达到了这种令人不快的不确定性水平。在住院患者中，最新的 由于许多因素，测试的值会随着时间的推移而变得陈旧，因此， 下一个测试。虽然事后才知道如何判断测试的最佳重复时间是多少， 目前还不清楚对下一个未来样本的预测准确度有多高。 该项目评估了在机构规模上提供这种预测性指导的技术可行性， 以下是fic的目标规格： 目标1：(准确性)开发和评估个性化计时指导的计算准确性 在中等规模的数据条件下。我们将开发模型，为125个最多- 重复住院患者数字实验室测试，并在中等规模的数据条件下评估其准确性。 目标2：(可扩展性和实用性)确定计算可扩展性和个性化时间需求- ING指导。我们将开发和评估扩展计时所需的速度与近似之间的权衡 模型到完整的机构数据大小。然后我们将评估历史订单，以量化将通过以下方式实现的收益 在我们最好的模式下使用个性化的计时指导。

The long-term goal of this research is to develop a data-driven tool to guide the clinical decision of when to repeat an inpatient laboratory test. The current proposal is to attempt the highest-risk element of this goal, which is to develop the machine-learning approach for estimating the optimal next time to run a given test on a given patient, taking into account the patient's clinical history, any treatments given, and current clinical status. Clinicians often overestimate how frequently a test should be repeated, or for convenience they order tests to be repeated every day. This wastes resources and increases the cost of care. Similarly, it can also be easy to underestimate how often a test should be repeated, which can lead to suboptimal care, with increased morbidity and mortality. There have been many interventions attempted over the years to reduce the frequency of repeated tests, but they generally use subjective, expert-specified rules that set minimum testing frequencies for certain clinical scenarios. These attempts are laudable, but they are rather blunt instruments for the problem, because they cannot adapt to the specific and varying needs of a patient's pathophysiologic state and treatment regimen, and they don't affect tests that aren't being run frequently enough. The key to providing data-driven timing guidance is the ability to estimate from data the rate at which a specific observed result will go stale, meaning that it is old enough that the estimated current value is too uncertain to be used for decision making. The mathematically optimal time to order a test repeat is exactly when the most recent value reaches that level of undesirable uncertainty. In hospitalized patients, the rate at which the latest value of a test goes stale changes over time due to many factors, and therefore, so does the optimal time for the next test. And while it is known how to tell in hindsight what the optimal repeat time would have been for a test, it is unknown how accurately it can be predicted for the next, future sample. This project assesses the technical feasibility of providing this predictive guidance at institutional scale, with the following specific aims: Aim 1: (Accuracy) Develop and assess the computational accuracy of personalized timing guidance under moderate-scale data conditions. We will develop models to provide timing guidance for the 125 most- repeated inpatient numeric lab test, and assess their accuracy under moderate-scale data conditions. Aim 2: (Scalability and Utility) Determine the computational scalability and need for personalized tim- ing guidance. We will develop and assess the speed vs. approximation trade off needed to scale up the timing models to full institutional data size. We will then assess historical orders to quantify the gain to be achieved by using personalized timing guidance under our best model.