Predictive Modeling of Call Outcomes to Poison Control Center Recommendations

描述（由申请人提供）：美国每年发生超过400万起中毒事件，其中30万人住院治疗。在90年代，中毒死亡率增加了56%，现在是伤害相关死亡的第二大原因。全国61个中毒控制中心（PCCs）每年通过电话服务处理大约60%的中毒案件。在答复这些电话时，PCC工作人员评估因中毒而继发的不良医疗结果的可能性。他们的作用对于有效利用紧急医疗服务至关重要——对那些可以在现场治疗的人进行分类，并将那些可能需要紧急医疗护理的人转介给他们。此外，HRSA建议PCCs作为国家对生物恐怖主义事件的第一反应系统。中毒控制中心的服务依赖于中毒控制专家提供的准确、快速、高效的电话咨询。本申请的重点是为PCCs及其工作人员建立一个证据基础，以应对日益严重的全国中毒问题。使用来自区域PCC的数据，我们建议使用行为科学和基于信息学的方法开发和测试PCC建议的呼叫结果的多元模型。该研究的第一部分侧重于一个可改变的因素——在地区PCC通话期间发生的沟通过程。一千个电话将使用广泛使用的医疗通信编码系统进行编码。这些呼叫将根据暴露年龄和高峰（即高呼叫量的发生率）进行分层。在以关系为中心的护理框架的指导下，我们将进行路径分析，以测试特定沟通策略在先验选择的，不可修改的因素（例如，激增，严重程度）和呼叫结果之间发挥的中介作用。在该项目的第二部分，将基于常规收集的一年临床数据创建呼叫结果预测模型，并将其作为临床决策支持应用的潜在基础进行评估，以促进最佳的PCC呼叫结果。数据挖掘方法将用于识别编码和文本数据的模式，然后用于创建预测模型。最后，我们将综合臂1和臂2的研究结果，建立一个探索性混合模型。从第2组中确定的独特的不可修改的临床特征将被评估其与1000个通话记录中的通信模式和通话结果的预测关系。Arm 2的这些特性可能包括并扩展了Arm 1中使用的不可修改的先验变量。这种混合模型测试将潜在地允许我们通过使用来自大规模预测建模的信息来扩展通信干预策略（由时间密集型定量编码产生）的应用，最终目标是促进最佳的PCC呼叫结果，从而减少不利的健康影响。

DESCRIPTION (provided by applicant): Over 4 million poisoning episodes occur in the US annually with hospitalization occurring in 300,000. During the 90's, the death rate by poisoning increased by 56% and is now the second leading cause of injury-related deaths. The nation's 61 Poison Control Centers (PCCs) handle approximately 60% of all annual poisoning cases via telephone services. In responding to these phone calls, PCC staff assess the likelihood of adverse medical outcomes secondary to poisonings. Their role is critical in making efficient use of emergency health care services-triaging those individuals who can be managed on site and referring those who may need emergency medical care. Furthermore, HRSA recommended that PCCs serve as the nation's first response system to bioterrorism events. PCC services are dependent on the accurate, rapid, efficient telephone consultation provided by poison control specialists. This focus of this application is the development of an evidence base for PCCs and their staff to use in responding to the increasing national problem of poisoning. Using data from a regional PCC, we propose to develop and test multivariate models of call outcomes to PCC recommendations using behavioral science and informatics-based methodology. The first arm of the study focuses on a modifiable factor-the communication process that occurs during calls at a regional PCC. One thousand calls will be coded with a widely used medical communication coding system. These calls will be stratified based on exposee age and surge (i.e., incidence of high call volume). Guided by a relationship-centered care framework, we will conduct path analyses to test the mediational role specific communications strategies play between a priori selected, nonmodifiable factors (e.g., surge, severity,) and call outcomes. In the second arm of the project, predictive models of call outcomes, based on routinely collected clinical data for one year will be created and evaluated as a potential basis for clinical decision support applications to promote optimal PCC call outcomes. Data mining methods will be used to identify patterns of both coded and textual data and then used to create predictive models. Finally, we will synthesize the findings from Arms 1 and 2 into an exploratory hybrid model. Unique nonmodifiable clinical features identified from Arm 2 will be assessed for their predictive relationship to communication patterns and to call outcomes within the 1000 recorded calls. These Arm 2 features are likely to include and expand upon the nonmodifiable, a priori variables used in Arm 1. This hybrid model-testing will potentially allow us expand the application of communication intervention strategies (resulting from time-intensive quantitative coding) by the use of information derived from large scale predictive modeling with the ultimate goal of promoting optimal PCC call outcomes, and thus reducing adverse health effects.