斯卡恩紧急医疗 (SEM) 队伍
Ulf Ekelund1, Bodil Ohlsson2, Olle Melander2
1Emergency medicine, Department of Clinical Sciences Lund, Lund University, Department of Emergency Medicine, Skåne University Hospital, Lund, Sweden. ulf.ekelund@med.lu.se.
概括
斯科恩紧急医疗队列为开发AI驱动的临床决策支持系统提供了一个强大的平台. 这些真实世界的数据有助于患者管理,诊断和减少急诊室拥挤的研究.
科学领域:
- 紧急医疗 紧急医疗
- 医疗信息学 医疗信息学
- 临床决策支持系统 临床决策支持系统
背景情况:
- 欧盟的急诊室 (ED) 面临着越来越多的患者数量,每年有超过1亿次访问,同比增长2-3%.
- 改进的患者管理策略可以显著缓解ED拥挤,减少诊断测试的使用,减少住院病人的病床占用,并降低整体医疗保健支出.
研究的目的:
- 建立斯卡内紧急医疗 (SEM) 队列,这是一个全面的数据集,用于开发和验证临床决策支持系统 (CDSS).
- 利用人工智能 (AI),机器学习 (ML) 和传统的统计方法来推进CDSS研究.
主要方法:
- 该SEM队列包括325,539个独特的患者和630,275个ED访问从2017年1月1日到2018年12月31日,在瑞典斯卡内的八个ED.
- 收集的数据包括患者社会人口统计,病史,当前药物,首席投诉,诊断测试结果,处置和结果,如诊断,治疗,成本和死亡率,直至访问后3年.
主要成果:
- SEM队列为CDSS研究提供了丰富的,现实世界的数据集.
- 该数据集能够对患者旅程,诊断途径和治疗结果进行全面分析.
结论:
- SEM队列是临床决策支持系统合作研究的宝贵平台.
- 它广泛的现实数据和近乎完整的短期随访有助于研究流行病学,患者管理,诊断,预后,ED拥挤,资源分配和社会医学.
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