InSPECt: Interactive Surveillance Portal for Evaluating Clinical support

InSPECt：用于评估临床支持的交互式监视门户

• 批准号: 8730217
• 负责人: ALLISON B MCCOY
• 金额: $ 12.51万
• 依托单位: TULANE UNIVERSITY OF LOUISIANA
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-30 至 2016-09-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8730217
• 关键词: Adopted; Clinical; Clinical Decision Support Systems; Community Hospitals; Disease; Dose; Drug Hypersensitivity; Electronic Health Record; Evaluation; Event; Fatigue; Gold; Health Personnel; Healthcare; Human Resources; Individual; Informatics; Institution; Manuals; Measures; Methods; Metric; Modeling; Monitor; Online Systems; Outcome; Patients; Pharmaceutical Preparations; Process; Provider; Reporting; Research; Staging; Statistical Models; base; biomedical informatics; computerized; design; health information technology; improved; meetings; novel; patient safety; research clinical testing; response; therapy design; tool

DESCRIPTION (provided by applicant): Many healthcare providers are adopting clinical decision support (CDS) systems to improve patient safety and meet Meaningful Use requirements. Computerized alerts that prompt clinicians about drug- allergy, drug-drug, and drug-disease warnings or provide dosing guidance are most commonly implemented. Alert overrides can hinder improved patient outcomes, and detailed evaluation of the appropriateness of alerts and clinician responses is necessary; however, traditional methods require manual chart reviews, which are labor intensive and difficult to replicate for all alerts across institutions. This proposal focuses on developing nove methods for evaluating and improving CDS alerts that build upon traditional informatics approaches. Aim 1 begins by applying previously described models for predicting alert overrides then improves these models by adding covariates based on measures not previously assessed within biomedical informatics. Aim 2 expands the use of web-based monitoring tools, developing an interactive dashboard for evaluating CDS alert and response appropriateness that incorporates the models developed in Aim 1. It is hypothesized that these methods will allow informatics personnel to identify and improve poorly performing alerts, thus reducing alert fatigue and increasing patient safety.

描述（由申请人提供）： 许多医疗服务提供者正在采用临床决策支持（CDS）系统，以提高患者安全性并满足有意义使用要求。提示临床医生药物过敏、药物-药物和药物-疾病警告或提供给药指导的计算机化警报是最常见的。警报覆盖可能会阻碍改善患者结局，并且有必要对警报和临床医生响应的适当性进行详细评估;然而，传统方法需要手动图表审查，这是劳动密集型的，并且难以跨机构复制所有警报。该提案的重点是开发基于传统信息学方法的评估和改进CDS警报的新方法。目标1首先应用先前描述的模型来预测警报覆盖，然后通过添加基于先前在生物医学信息学中未评估的措施的协变量来改进这些模型。目标2扩大了基于网络的监测工具的使用，开发了一个互动仪表板，用于评估CDS警报和反应的适当性，其中包括目标1中开发的模型。据推测，这些方法将允许信息人员识别和改善性能差的警报，从而减少警报疲劳，提高患者安全性。