Graphics help patients distinguish between urgent and non-urgent deviations in laboratory test results.

JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION(2017)

引用 72|浏览25
暂无评分
摘要
Objective: Most electronic health record systems provide laboratory test results to patients in table format. We tested whether presenting such results in visual displays (number lines) could improve understanding. Materials and Methods: We presented 1620 adults recruited from a demographically diverse Internet panel with hypothetical results from several common laboratory tests, first showing near-normal results and then more extreme values. Participants viewed results in either table format (with a "standard range" provided) or one of 3 number line formats: a simple 2-color format, a format with diagnostic categories such as "borderline high" indicated by colored blocks, and a gradient format that used color gradients to smoothly represent increasing risk as values deviated from standard ranges. We measured respondents' subjective sense of urgency about each test result, their behavioral intentions, and their perceptions of the display format. Results: Visual displays reduced respondents' perceived urgency and desire to contact health care providers immediately for near-normal test results compared to tables but did not affect their perceptions of extreme values. In regression analyses controlling for respondent health literacy, numeracy, and graphical literacy, gradient line displays resulted in the greatest sensitivity to changes in test results. Discussion: Unlike tables, which only tell patients whether test results are normal or not, visual displays can increase themeaningfulness of test results by clearly defining possible values and leveraging color cues and evaluative labels. Conclusion: Patient-facing displays of laboratory test results should use visual displays rather than tables to increase people's sensitivity to variations in their results.
更多
查看译文
关键词
decision making,education of patients,electronic health record,computer graphics,clinical laboratory information systems
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