Department of Anesthesiology, Section on Critical Care Medicine, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA; Outcomes Research Consortium, Cleveland Clinic, Cleveland, OH, USA.
Department of Anesthesiology, Pain Management and Perioperative Medicine, Henry Ford Health System, Detroit, MI 48202, USA; Outcomes Research Consortium, Cleveland Clinic, Cleveland, OH, USA.
Best Pract Res Clin Anaesthesiol. 2019 Jun;33(2):229-245. doi: 10.1016/j.bpa.2019.06.005. Epub 2019 Jul 23.
The postoperative ward is considered an ideal nursing environment for stable patients transitioning out of the hospital. However, approximately half of all in-hospital cardiorespiratory arrests occur here and are associated with poor outcomes. Current monitoring practices on the hospital ward mandate intermittent vital sign checks. Subtle changes in vital signs often occur at least 8-12 h before an acute event, and continuous monitoring of vital signs would allow for effective therapeutic interventions and potentially avoid an imminent cardiorespiratory arrest event. It seems tempting to apply continuous monitoring to every patient on the ward, but inherent challenges such as artifacts and alarm fatigue need to be considered. This review looks to the future where a continuous, smarter, and portable platform for monitoring of vital signs on the hospital ward will be accompanied with a central monitoring platform and machine learning-based pattern detection solutions to improve safety for hospitalized patients.
术后病房被认为是稳定患者出院过渡的理想护理环境。然而,大约一半的院内心肺骤停都发生在这里,且预后不良。目前医院病房的监测实践要求进行间歇性生命体征检查。生命体征的细微变化通常至少在急性事件发生前 8-12 小时就会出现,持续监测生命体征可以进行有效的治疗干预,并有可能避免即将发生的心肺骤停事件。似乎很诱人将连续监测应用于病房中的每个患者,但需要考虑到固有的挑战,如伪影和报警疲劳。这篇综述展望了未来,在未来,医院病房的生命体征监测将采用一个连续、更智能、便携式的平台,并配备中央监测平台和基于机器学习的模式检测解决方案,以提高住院患者的安全性。