Study of micro-signals: proposed analysis methodology based on data from the Lille Poison Control and Toxicovigilance Center.

IF 3.3
Tracy Lurant, Anne Garat, Emma Nemesien, Patrick Nisse, Ramy Azzouz, Djamel Zitouni
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Abstract

Introduction: This project aimed to enhance the Lille Poison Control and Toxicovigilance Center health surveillance and proactive response to intoxication-related issues by integrating a micro-signal analysis methodology.

Methods: We developed a methodology employing Poisson distribution and historical limits to identify unusual signals marked by significant variations, often obscured by case age, continuous information flow (due to the 24/7 operational nature of the service), or team rotations. A severity score was created to prioritize micro-signals based on case seriousness to facilitate rapid and appropriate interventions. Additionally, a dashboard was developed to visualize detected micro-signals as a heat map, improving accessibility and decision-making for poison center professionals.

Results: Testing the methodology with historical Lille Poison Control and Toxicovigilance Center data demonstrated its viability and the medical relevance of the severity score calculations. It highlighted the effect of national measures on the use of an anxiolytic that had not been detected by the teams at the time. In addition, when the values returned by the severity score are interpreted by class of substance, rather than individually, similar orders of magnitude are observed (e.g., mushrooms, anxiolytics in our case study).

Discussion: Our approach shows potential for improving patient care and responsiveness in toxicovigilance, particularly within the French Lille Poison Control and Toxicovigilance Center network. One limitation of the current dashboard is that it only carries out analysis by product. Future enhancements include the integration of a thesaurus which will also allow analysis by class.

Conclusion: This study integrated a micro-signal analysis method into a health surveillance system to detect hidden trends in poison center data, improving emergency response and substance management.

微信号研究:基于里尔毒物控制和毒物警戒中心数据提出的分析方法。
本项目旨在通过整合微信号分析方法,加强里尔毒物控制和毒物警戒中心对中毒相关问题的健康监测和主动响应。方法:我们开发了一种方法,利用泊松分布和历史限制来识别以显著变化为标志的异常信号,这些异常信号通常被病例年龄、连续信息流(由于服务的24/7运营性质)或团队轮换所掩盖。创建了一个严重程度评分,根据病例严重程度对微信号进行优先排序,以促进快速和适当的干预。此外,还开发了一个仪表板,将检测到的微信号可视化为热图,提高了中毒中心专业人员的可访问性和决策能力。结果:用历史里尔毒物控制和毒物警戒中心的数据测试该方法证明了其可行性和严重程度评分计算的医学相关性。它强调了国家措施对使用一种当时各小组未发现的抗焦虑药的影响。此外,当严重性评分返回的值按物质类别解释时,而不是单独解释,观察到类似的数量级(例如,蘑菇,我们的案例研究中的抗焦虑药)。讨论:我们的方法显示了改善病人护理和毒性警戒反应的潜力,特别是在法国里尔中毒控制和毒性警戒中心网络中。当前仪表板的一个限制是它只能按产品进行分析。未来的增强包括集成同义词库,它还允许按类进行分析。结论:本研究将微信号分析方法整合到健康监测系统中,发现中毒中心数据中的潜在趋势,提高应急响应和物质管理水平。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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