Predictive Modeling of Call Outcomes to Poison Control Center Recommendations

对中毒控制中心建议的呼叫结果进行预测建模

• 批准号: 7320772
• 负责人: Lee A Ellington
• 金额: $ 37.38万
• 依托单位: UNIVERSITY OF UTAH
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-07-20 至 2010-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7320772
• 关键词: Age; Attention; Behavioral Sciences; Bioterrorism; Caring; Cessation of life; Characteristics; Classification; Clinical; Clinical Data; Code; Communication; Complex; Consultations; Data; Death Rate; Development; Emergency Situation; Event; Food; Gender; Goals; Health; Health Services; Healthy People 2010; Hospitalization; Hybrids; Incidence; Individual; Informatics; Injury; Institute of Medicine (U.S.); Intervention; Marketing; Measures; Mediating; Medical; Methodology; Methods; Modeling; Nature; Numbers; Nurses; Outcome; Patients; Pattern; Play; Poison; Poison Control Centers; Poisoning; Predictive Factor; Procedures; Process; Public Health; Recommendation; Records; Reporting; Research; Research Personnel; Role; Roter; Route; Safety; Secondary to; Services; Severities; Site; Specialist; Specific qualifier value; System; Systems Analysis; Telephone; Testing; Time; Toxic effect; Training; Triage; United States Health Resources and Services Administration; Upper arm; base; data mining; evidence based guidelines; experience; medical specialties; national surveillance; poison control; predictive modeling; programs; psychosocial; response; tool

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.

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（PCC）通过电话服务处理大约60%的年度中毒案件。在回应这些电话时，PCC工作人员评估中毒继发不良医疗后果的可能性。他们的作用是至关重要的，有效地利用紧急医疗服务，分流那些个人谁可以在现场管理，并介绍那些谁可能需要紧急医疗服务。此外，美国人权事务局建议，预防和控制中心应作为国家应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。该应用程序的重点是为PCCs及其工作人员开发证据库，以应对日益严重的全国中毒问题。使用区域PCC的数据，我们建议开发和测试呼叫结果的多变量模型PCC建议使用行为科学和基于信息学的方法。研究的第一个分支集中在一个可修改的因素-在区域PCC呼叫期间发生的通信过程。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。这些呼叫将根据患者年龄和激增（即，高呼叫量发生率）。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因素（例如，浪涌、严重性）和呼叫结果。在该项目的第二个分支中，将根据一年来定期收集的临床数据创建和评估呼叫结果的预测模型，作为临床决策支持应用的潜在基础，以促进最佳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，我们将把第1组和第2组的研究结果综合成一个探索性的混合模型。将评估从组2中识别出的独特的不可改变的临床特征与通信模式和1000次记录呼叫中呼叫结局的预测关系。这些第2组特征可能包括并扩展第1组中使用的不可修改的先验变量。这种混合模型测试将有可能使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PCC呼叫结果，从而减少对健康的不利影响。