Clinical Documentation and Patient Care Using Artificial Intelligence in Radiation Oncology

被引:18
|
作者
Luh, Join Y. [1 ,2 ]
Thompson, Reid F. [2 ,3 ,4 ,5 ]
Lin, Steven [6 ]
机构
[1] Providence St Joseph Hlth, Dept Radiat Oncol, 2700 Dolbeer St, Eureka, CA 95501 USA
[2] Oregon Hlth & Sci Univ, Dept Radiat Med, Portland, OR 97201 USA
[3] Oregon Hlth & Sci Univ, Dept Biomed Engn, Portland, OR 97201 USA
[4] Oregon Hlth & Sci Univ, Dept Med Informat & Clin Epidemiol, Portland, OR 97201 USA
[5] Oregon Hlth & Sci Univ, Div Hosp & Specialty Med, VA Portland Healthcare Syst, Computat Biol Program, Portland, OR 97201 USA
[6] Stanford Univ, Primary Care & Populat Hlth, Palo Alto, CA 94304 USA
关键词
TIME;
D O I
10.1016/j.jacr.2019.05.044
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
Detailed clinical documentation is required in the patient-facing specialty of radiation oncology. The burden of clinical documentation has increased significantly with the introduction of electronic health records and participation in payer-mandated quality initiatives. Artificial intelligence (AI) has the potential to reduce the burden of data entry associated with clinical documentation, provide clinical decision support, improve quality and value, and integrate patient data from multiple sources. The authors discuss key elements of an AI-enhanced clinic and review some emerging technologies in the industry. Challenges regarding data privacy, regulation, and medicolegal liabilities must be addressed for such AI technologies to be successful. (C) 2019 Published by Elsevier on behalf of American College of Radiology
引用
收藏
页码:1343 / 1346
页数:4
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