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，以指导临床决策， 重复住院实验室检查。目前的建议是尝试这一目标中风险最高的部分， 是开发机器学习方法，用于估计下一次在给定的环境中运行给定测试的最佳时间。 患者，考虑患者的临床病史、给予的任何治疗和当前临床状态。 临床医生经常高估一个测试应该重复的频率，或者为了方便他们安排测试 每天都要重复这浪费了资源，增加了护理成本。同样，它也可以很容易地 低估了重复检测的频率，这可能导致护理不佳，发病率增加 and mortality.多年来，人们尝试了许多干预措施，以减少重复发生的频率。 测试，但他们通常使用主观的，专家指定的艾德规则，设置最低测试频率为某些 临床场景。这些尝试是值得称赞的，但它们是解决问题的相当钝的工具，因为 它们不能适应患者的病理生理状态和治疗方案的特定和变化的需求， 而且它们不会影响运行频率不够高的测试。 提供数据驱动的时间指导的关键是能够从数据中估计特定时间的速率， 观察到的结果将过时，这意味着它已经足够旧，估计的当前值太不确定， 用于决策。数学上的最佳时间，以命令测试重复正是当最 最近的价值达到了不希望的不确定性水平。在住院患者中， 由于许多因素，测试的值会随着时间的推移而变化，因此，测试的最佳时间也会随着时间的推移而变化。 下一个测试虽然事后知道如何判断测试的最佳重复时间， 不知道对于下一个将来的样本，它能被预测得多精确。 该项目评估了在机构范围内提供这种预测性指导的技术可行性， 具体目标如下： 目标1：（准确性）开发和评估个性化定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个最重要的- 重复的住院数字实验室测试，并评估其准确性在中等规模的数据条件下。 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。 的指导。我们将开发和评估扩展时间所需的速度与近似值之间的权衡 模型到完整的机构数据大小。然后，我们将评估历史订单，以量化通过以下方式实现的收益 在我们的最佳模式下使用个性化的时间指导。

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.