Mayo Charles S, Kessler Marc L, Eisbruch Avraham, Weyburne Grant, Feng Mary, Hayman James A, Jolly Shruti, El Naqa Issam, Moran Jean M, Matuszak Martha M, Anderson Carlos J, Holevinski Lynn P, McShan Daniel L, Merkel Sue M, Machnak Sherry L, Lawrence Theodore S, Ten Haken Randall K
Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan.
Department of Radiation Oncology, University of California at San Francisco, San Francisco, California.
Adv Radiat Oncol. 2016 Oct 13;1(4):260-271. doi: 10.1016/j.adro.2016.10.001. eCollection 2016 Oct-Dec.
Although large volumes of information are entered into our electronic health care records, radiation oncology information systems and treatment planning systems on a daily basis, the goal of extracting and using this big data has been slow to emerge. Development of strategies to meet this goal is aided by examining issues with a data farming instead of a data mining conceptualization. Using this model, a vision of key data elements, clinical process changes, technology issues and solutions, and role for professional societies is presented. With a better view of technology, process and standardization factors, definition and prioritization of efforts can be more effectively directed.
尽管每天都有大量信息录入我们的电子医疗记录、放射肿瘤学信息系统和治疗计划系统，但提取和使用这些大数据的目标却迟迟未能实现。通过以数据耕耘而非数据挖掘的概念来审视问题，有助于制定实现这一目标的策略。利用这种模式，本文提出了关键数据元素、临床流程变化、技术问题与解决方案以及专业协会作用的设想。有了对技术、流程和标准化因素更清晰的认识，就能更有效地指导工作的定义和优先级确定。
