Information about radiation dose and risks in connection with radiological examinations: what patients would like to know

European radiology(2015)

引用 24|浏览8
暂无评分
摘要
Objectives To find out patients’ wishes for the content and sources of the information concerning radiological procedures. Methods A questionnaire providing quantitative and qualitative data was prepared. It comprised general information, dose and risks of radiation, and source of information. Two tables demonstrating different options to indicate the dose or risks were also provided. Patients could give one or many votes. Altogether, 147 patients (18–85 years) were interviewed after different radiological examinations using these devices. Results 95 % (139/147) of the patients wished for dose and risk information. Symbols (78/182 votes) and verbal scale (56/182) were preferred to reveal the dose, while verbal (83/164) and numerical scale (55/164) on the risk of fatal cancer were preferred to indicate the risks. Wishes concerning the course, options and purpose of the examination were also expressed. Prescriber (3.9 on a scale 1–5), information letter (3.8) and radiographer (3.3) were the preferred sources. Patients aged 66–85 years were reluctant to choose electronic channels. Conclusions Apart from general information, patients wish for dose and risk information in connection with radiological examinations. The majority preferred symbols to indicate dose and verbal scales to indicate risks, and the preferred source of information was the prescriber or information letter. Key points • 95 % of patients expect information on the dose and risks of radiation. • Symbols and verbal scale are preferred to indicate the dose. • Verbal and numerical scales are preferred to indicate fatal cancer risk. • Patients expect information on course, options and purpose of examination. • Prescriber, information letter and radiographer are popular sources of the overall information.
更多
查看译文
关键词
Communication,Informed consent,Patient safety,Radiation,ionizing,Radiology
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要