Predictive Modeling of Call Outcomes to Poison Control Center Recommendations

描述(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。上世纪90年代，S的中毒死亡率上升了56%，现已成为伤害死亡的第二大死因。全国61个中毒控制中心(PCCs)通过电话服务处理了每年约60%的中毒案件。在回复这些电话时，PCC工作人员评估中毒继发的不良医疗后果的可能性。他们在有效利用紧急卫生保健服务方面的作用至关重要--对那些可以现场管理的人进行分流，并转介那些可能需要紧急医疗护理的人。此外，人权事务高级专员还建议将初级预防控制中心作为国家对生物恐怖主义事件的第一反应系统。PCC服务依赖于毒物控制专家提供的准确、快速、高效的电话咨询。这一应用的重点是开发一个证据库，供化学品控制中心及其工作人员使用，以应对日益严重的全国中毒问题。利用地区性PCC的数据，我们建议使用行为科学和基于信息学的方法来开发和测试PCC建议的呼叫结果多变量模型。这项研究的第一个方面侧重于一个可修改的因素--在地区PCC通话期间发生的沟通过程。1000个呼叫将使用广泛使用的医疗通信编码系统进行编码。这些电话将根据接触者的年龄和激增(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因素(例如，激增、严重性)和呼叫结果之间所起的中介作用。在该项目的第二个分支中，将根据一年的常规收集的临床数据创建和评估呼叫结果预测模型，作为临床决策支持应用程序促进最佳PCC呼叫结果的潜在基础。数据挖掘方法将用于识别编码数据和文本数据的模式，然后用于创建预测模型。最后，我们将把手臂1和手臂2的发现综合成一个探索性的混合模型。将评估ARM 2中确定的独特的不可修改的临床特征，以确定它们与通信模式和1000个录音通话中的通话结果的预测关系。ARM 2的这些功能可能包括和扩展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大通信干预策略(由时间密集型量化编码产生)的应用，最终目标是促进最佳的PCC呼叫结果，从而减少对健康的不利影响。

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.