A Context-Sensitive Teleconsultation Infrastructure

描述(由申请人提供)：咨询适当的专家可以提高医疗保健的质量，特别是对患有复杂病例或慢性病的患者。对于大多数这类患者，专家使用成像研究(例如，MR、CT)来客观地记录疾病过程(例如，正在接受化疗的癌症患者)。然而，专家通常不是在所有社区都有，往往集中在学术/专业中心。因此，为了便于在专家不在当地的情况下对患者进行常规远程会诊：1)记录患者病情的图像必须合并到医疗记录中，以便进行适当的审查；以及2)远程会诊人员应该只接收医疗记录的相关部分，以简化会诊过程。该建议的重点是开发和测试基于以下内容的“上下文敏感”远程医疗基础设施：1)自动合并临床上下文(患者陈述和转诊医生假设)以集中会诊过程；2)从自然语言处理(NLP)结果的数据挖掘中获得知识库，绘制患者陈述以根据解剖区域和成像参数选择适当的成像研究；以及3)通过使用对比度可定制的图谱和刚体/可变形配准算法，在所获得的成像研究中自动选择关键解剖结构。总而言之，这些技术将允许在现实环境中对远程医疗的医疗记录进行上下文相关的自动摘要。拟议的技术将应用于神经学和肌肉骨骼领域，这两个领域是磁共振成像密集型领域。 将以专家作为参考标准进行技术评估，重点是衡量：1)以语料库为基础、由自然语言规划指导的知识库选择相关解剖结构的准确性；以及2)使用可定制的图谱配准方法描绘解剖结构的准确性。临床评估将在现实世界的远程会诊环境中进行，采用前/后研究设计，使用两个性能指标：1)会诊所需时间；2)对会诊质量的影响。

DESCRIPTION (provided by applicant): Consultation with appropriate specialists improves the quality of healthcare, particularly in patients with complicated cases or chronic illnesses. And for the majority of such patients, specialists use imaging studies (e.g., MR, CT) to objectively document the disease process (e.g., a cancer patient on chemotherapy). However, specialists are generally not available in all communities, tending to be concentrated in academic/specialty centers. Thus, to facilitate the routine use of teleconsultations for patients when specialists are not locally present: 1) the images captured to document the patient's condition must be incorporated into the medical record to enable proper review; and 2) the remote consultant should only receive pertinent parts of the medical record to streamline the consultation process. This proposal is focused on developing and testing a "context-sensitive" telehealth infrastructure based on: 1) automated incorporation of clinical context (patient presentation and referring physician hypothesis) to focus the consultation process; 2) a knowledge-base derived from data mining of natural language processing (NLP) results, mapping patient presentation to select an appropriate imaging study based on anatomical region and imaging parameters; and 3) automated selection of key anatomical structures in the acquired imaging study through the use of a contrast-customizable atlas and rigid body/deformable registration algorithms. Collectively, these technologies will allow context-sensitive, automated summarization of medical records for telehealth in a real-world environment. The proposed technologies will be implemented for neurological and musculoskeletal domains, two areas that are MR imaging intensive. Technical evaluation will be performed with experts serving as the reference standard and will focus on measuring: 1) the accuracy of the corpus based, NLP-guided knowledge-base in selecting relevant anatomical structures; and 2) the accuracy of anatomical structure delineation using the customizable atlas registration methods. Clinical evaluation will be conducted in a real-world teleconsultation environment in a before/after study design using two performance metrics: 1) the time required for consultations; and 2) the effect on the quality of the consultations.