在高死亡风险的住院患者中对护理的记录目标的分类:混合方法可行性研究
Catherine L Auriemma1,2,3, Anne Song4,5, Lake Walsh6
1Palliative and Advanced Illness Research (PAIR) Center, University of Pennsylvania, Philadelphia, PA, USA. Catherine.auriemma@pennmedicine.upenn.edu.
Journal of general internal medicine
|May 6, 2024
概括
从电子健康记录 (EHR) 中对患者的护理目标 (GOC) 进行分类是可行的. 文档可靠地捕捉了中国政府的变化,有助于严重疾病的沟通和目标一致的护理测量.
科学领域:
- 医疗信息学 医疗信息学
- 临床交流 临床沟通
- 严重疾病护理 严重疾病护理
背景情况:
- 从临床文档中对患者的护理目标 (GOC) 的分类对于改善严重疾病沟通的质量和衡量目标一致的护理至关重要.
- 从电子健康记录 (EHR) 中提取中国政府的可行性尚未确定.
研究的目的:
- 评估从EHR临床文档中对中国政府进行分类的可行性.
- 描述中国政府随着时间的推移而变化的频率和模式.
- 为了确定中国政府可靠分类的障碍.
主要方法:
- 对高6个月死亡风险的成年患者进行了回顾性,混合方法的图表审查.
- 两名医生编码人员独立审查了中国政府讨论的EHR笔记,并将目标分为四类:以舒适为重点,维持/改善功能,延长寿命或不清楚.
- 定性内容分析发现了分类的障碍.
主要成果:
- 在109名患者中,有85人 (78%) 进行了338次记录的中国政府讨论,具有相当大的评审者间可靠性 (科恩的卡帕 = 0.67).
- 最初的目标最常见的是"延长寿命" (44%) 或"维持/改善功能" (33%).
- 在目标改变的患者中 (66),大多数患者转向"以舒适为重点" (74%). 不明确的目标源于并发/有条件的目标,患者/家庭的不确定性和有限的文档.
结论:
- 电子健康记录中的临床注释可以可靠地将患者的GOC分为不同的临床相关类别.
- 这项研究支持使用EHR文档进行中国政府分类,为可扩展的自然语言处理应用铺平了道路.
- 未来的研究应该专注于利用自然语言模型,以提高这种方法在临床实践和严重疾病研究中的可扩展性.
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