智能分配医学影像报告，提升诊疗效率。
A Context-Aware Middleware for Medical Image Based Reports: An approach based on image feature extraction and association rules
- 基于图像特征与关联规则自动匹配最优医生
- 根据临床上下文动态推荐接收人员，减少人工调度时间
- 适用于医院、检验中心等多角色协作场景
本文提出一种面向医疗工作流组织与效率优化的上下文感知中间件。在医院、实验室及远程放射公司中，每位医师或技术人员专精于特定类型的诊断或分析，导致特定医学影像常需转发至特定医师或团队。这一转发过程耗时且低效——反复判断谁是最合适的人选，以及其当前是否可用，极为繁琐。为此，所提出的中间件能够处理并收集各医疗人员分析过的影像数据，结合已采集的数据与当前临床上下文，推断出最适合接收某张新到医学影像的医务人员。
原文摘要 · Abstract (English)
This work proposes a context-aware middleware for medical workflow organization and efficiency improvement. In hospitals, laboratories and teleradiology companies, each physician or technician is specialized in a specific kind of diagnosis or analysis. Therefore, certain types of medical images are often forwarded to a certain physician or a certain group. This forwarding is time consuming. That is, repeatedly deciding who would be the best physician, whether he is available at a certain moment given a certain context is exhaustive and may be very inefficient. Thus, the proposed middleware has the ability to process and collect data from images analyzed by each medical staff. Based on the collected data and current clinical context, the middleware is able to infer who would be the best fit staff to receive a certain incoming medical image.
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