Multimedia Abstract Generation of Intensive Care Data: The Automation of Clinical Processes Through AI Methodologies
Multimedia Abstract Generation of Intensive Care Data: The Automation of Clinical Processes Through AI Methodologies
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DOI：
10.1007/s00268-009-0319-5
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发表时间：
2010-04-01
影响因子：
2.6
通讯作者：
Rose, Sydney E.
中科院分区：
文献类型：
--
作者：
Jordan, Desmond;Rose, Sydney E.
在心脏手术患者的围手术期，由于沟通失败而导致的医疗错误是巨大的。当护理人员换班或手术患者在医院内改变位置时，关键信息会丢失或被误解。在对信息需求和护理人员工作流程进行基线认知研究后，我们实施了一种由智能代理、医疗逻辑模块和文本生成器组成的先进临床决策支持工具（称为“推理引擎”），将个体患者的原始医疗数据元素总结为程序里程碑、疾病严重程度和护理疗法。该系统生成两个显示：1）连续护理、重症监护数据多媒体摘要生成（MAGIC）——一个专家系统，可以以多模式格式自动生成心脏病患者手术过程的医生简报； 2) 孤立的时间点，“推理引擎”aEuro“系统，提供患者临床状态的实时、高级、总结性描述。在我们的研究中，系统的准确性和有效性是根据临床医生在工作场所的表现来判断的。为了测试自动医生简报，在患者到达之前在重症监护病房中审查了患者的术中过程“MAGIC”。然后根据实际的医生简报以及在一组患者中给出的情况进行判断，其中为了测试患者临床状态的实时表现，通过问卷和过程评估来判断系统推断的工作流程和态势感知，提供了 200% 的更多信息，提高了态势感知能力。这项研究表明，通过人工智能方法实现临床流程的自动化产生了积极的结果。
Medical errors from communication failures are enormous during the perioperative period of cardiac surgical patients. As caregivers change shifts or surgical patients change location within the hospital, key information is lost or misconstrued. After a baseline cognitive study of information need and caregiver workflow, we implemented an advanced clinical decision support tool of intelligent agents, medical logic modules, and text generators called the "Inference Engine" to summarize individual patient's raw medical data elements into procedural milestones, illness severity, and care therapies. The system generates two displays: 1) the continuum of care, multimedia abstract generation of intensive care data (MAGIC)-an expert system that would automatically generate a physician briefing of a cardiac patient's operative course in a multimodal format; and 2) the isolated point in time, "Inference Engine"aEuro"a system that provides a real-time, high-level, summarized depiction of a patient's clinical status. In our studies, system accuracy and efficacy was judged against clinician performance in the workplace. To test the automated physician briefing, "MAGIC," the patient's intraoperative course, was reviewed in the intensive care unit before patient arrival. It was then judged against the actual physician briefing and that given in a cohort of patients where the system was not used. To test the real-time representation of the patient's clinical status, system inferences were judged against clinician decisions. Changes in workflow and situational awareness were assessed by questionnaires and process evaluation. MAGIC provides 200% more information, twice the accuracy, and enhances situational awareness. This study demonstrates that the automation of clinical processes through AI methodologies yields positive results.