PDF(Pubmed)
Abstract：
UNASSIGNED: Numerous complex physiological models derived from intracranial pressure (ICP) monitoring data have been developed. More recently, techniques such as machine learning are being used to develop increasingly sophisticated models to aid in clinical decision-making tasks such as diagnosis and prediction. Whilst their potential clinical impact may be significant, few models based on ICP data are routinely available at a patient\'s bedside. Further, the ability to refine models using ongoing patient data collection is rare. In this paper we identify and discuss the challenges faced when converting insight from ICP data analysis into deployable tools at the patient bedside.
UNASSIGNED: To provide an overview of challenges facing implementation of sophisticated ICP models and analyses at the patient bedside.
UNASSIGNED: A narrative review of the barriers facing implementation of sophisticated ICP models and analyses at the patient bedside in a neurocritical care unit combined with a descriptive case study (the CHART-ADAPT project) on the topic.
UNASSIGNED: Key barriers found were technical, analytical, and integrity related. Examples included: lack of interoperability of medical devices for data collection and/or model deployment; inadequate infrastructure, hindering analysis of large volumes of high frequency patient data; a lack of clinical confidence in a model; and ethical, trust, security and patient confidentiality considerations governing the secondary use of patient data.
UNASSIGNED: To realise the benefits of ICP data analysis, the results need to be promptly delivered and meaningfully communicated. Multiple barriers to implementation remain and solutions which address real-world challenges are required.
UNASSIGNED: To provide an overview of challenges facing implementation of sophisticated ICP models and analyses at the patient bedside.
UNASSIGNED: A narrative review of the barriers facing implementation of sophisticated ICP models and analyses at the patient bedside in a neurocritical care unit combined with a descriptive case study (the CHART-ADAPT project) on the topic.
UNASSIGNED: Key barriers found were technical, analytical, and integrity related. Examples included: lack of interoperability of medical devices for data collection and/or model deployment; inadequate infrastructure, hindering analysis of large volumes of high frequency patient data; a lack of clinical confidence in a model; and ethical, trust, security and patient confidentiality considerations governing the secondary use of patient data.
UNASSIGNED: To realise the benefits of ICP data analysis, the results need to be promptly delivered and meaningfully communicated. Multiple barriers to implementation remain and solutions which address real-world challenges are required.
摘要：
■已经开发了许多从颅内压（ICP）监测数据得出的复杂生理模型。最近,诸如机器学习之类的技术被用于开发越来越复杂的模型，以帮助诊断和预测等临床决策任务。虽然它们的潜在临床影响可能很大，很少有基于ICP数据的模型在患者床边常规可用。Further,使用正在进行的患者数据收集来优化模型的能力很少.在本文中，我们确定并讨论了将ICP数据分析的洞察力转换为患者床边可部署的工具时面临的挑战。
■概述在患者床边实施复杂的ICP模型和分析所面临的挑战。
对神经重症监护病房患者床边实施复杂ICP模型和分析所面临的障碍进行叙述性回顾，并结合该主题的描述性案例研究（CHART-ADAPT项目）。
■发现的主要障碍是技术性的，分析，和诚信相关。例子包括：数据收集和/或模型部署的医疗设备缺乏互操作性；基础设施不足，阻碍了对大量高频患者数据的分析；对模型缺乏临床信心；以及道德，信任,管理二次使用患者数据的安全性和患者机密性考虑。
■为了实现ICP数据分析的好处，结果需要迅速交付并有意义地传达。实施仍然存在多种障碍，需要解决现实世界挑战的解决方案。
■概述在患者床边实施复杂的ICP模型和分析所面临的挑战。
对神经重症监护病房患者床边实施复杂ICP模型和分析所面临的障碍进行叙述性回顾，并结合该主题的描述性案例研究（CHART-ADAPT项目）。
■发现的主要障碍是技术性的，分析，和诚信相关。例子包括：数据收集和/或模型部署的医疗设备缺乏互操作性；基础设施不足，阻碍了对大量高频患者数据的分析；对模型缺乏临床信心；以及道德，信任,管理二次使用患者数据的安全性和患者机密性考虑。
■为了实现ICP数据分析的好处，结果需要迅速交付并有意义地传达。实施仍然存在多种障碍，需要解决现实世界挑战的解决方案。
