A Context-Sensitive Teleconsultation Infrastructure

上下文敏感的远程会诊基础设施

• 批准号: 7115855
• 负责人: USHA SINHA
• 金额: $ 39.63万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-09-30 至 2008-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7115855
• 关键词: anatomy; clinical research; computer assisted diagnosis; computer assisted medical decision making; computer assisted patient care; computer data analysis; computer graphics /printing; computer system design /evaluation; diagnosis quality /standard; health care quality; health care referral /consultation; human data; image enhancement; image processing; magnetic resonance imaging; musculoskeletal disorder; nervous system disorder; statistics /biometry; telemedicine

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.

描述（由申请人提供）：与适当的专家进行咨询可以提高医疗保健质量，尤其是在患有复杂病例或慢性病的患者中。对于大多数此类患者，专家使用成像研究（例如MR，CT）客观地记录了疾病过程（例如，接受化学疗法的癌症患者）。但是，在所有社区中，通常都不可用专家，倾向于集中于学术/专业中心。因此，为了促进专家在本地不存在的情况时，常规使用远程培养的患者：1）捕获的记录患者状况的图像必须纳入病历中，以进行适当的审查； 2）远程顾问应仅收到病历中相关的部分以简化咨询过程。该提案的重点是基于以下方面开发和测试“上下文敏感”的远程医疗基础设施：1）自动化临床环境（患者介绍和参考医师假说）以集中咨询过程； 2）从自然语言处理（NLP）结果的数据挖掘得出的知识基础，绘制患者的介绍以根据解剖区域和成像参数选择适当的成像研究； 3）通过使用对比度可测量的地图集和刚体/可变形的注册算法，在获得成像研究中自动选择关键解剖结构。总的来说，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敏感的自动汇总。该提出的技术将用于神经和肌肉骨骼域，这是MR成像密集型的两个领域。 技术评估将与作为参考标准的专家进行，并将重点介绍：1）基于语料库的，基于NLP的知识碱的准确性在选择相关的解剖结构方面； 2）使用可自定义的ATLAS登记方法的解剖结构描述的准确性。临床评估将在研究之前/之后的研究设计中使用两个绩效指标在研究之前/之后进行临床评估：1）咨询所需的时间； 2）对咨询质量的影响。