Arnott Smith Catherine
Syracuse University, USA.
AMIA Annu Symp Proc. 2003;2003:614-8.
To determine the effect on clinical information retrieval of structuring typical clinical documents in XML, according to the general guidelines of Health Level Seven's Clinical Document Architecture.
One thousand clinical documents of eight frequently occurring types were deidentified and marked up in XML for access using a Web browser. Fifty information-seeking tasks were posed to subjects. The tasks were comprised of two typical clinical question types-individual patient results reporting and cohort identification. A control group of physician subjects could perform only free-text, keyword searching. The treatment group's interface permitted field-based searching of particular sections within each document. Differences in precision and other measures of search success across and between question types were investigated for statistical significance.
No statistically significant differences were found between the control and treatment conditions in mean time elapsed or the mean number of records in the final result set. In fact, tasks performed in the treatment condition required a mean number of more steps in the search sequence to a degree that was statistically significant. Tasks performed in the treatment condition had a statistically significant lower rate of mean precision. There was no statistically significant difference between the means of relevance of the individual patient and cohort identification tasks.
These findings are in line with Tange et al. who found that coarser granularity of clinical narrative gave better results. The results of this experiment also have implications for automatic text processing. Complex tag sets cannot ultimately resolve problems of unstandardized structure; the lack of existing structure within clinical documents is itself a significant limitation.
根据卫生信息标准化组织（Health Level Seven）临床文档架构的一般指南，确定以可扩展标记语言（XML）构建典型临床文档对临床信息检索的影响。
对8种常见类型的1000份临床文档进行去识别处理，并以XML格式进行标记，以便通过网络浏览器访问。向受试者提出了50项信息检索任务。这些任务包括两种典型的临床问题类型——个体患者结果报告和队列识别。对照组的医生受试者只能进行自由文本关键词搜索。治疗组的界面允许对每份文档中的特定部分进行基于字段的搜索。研究了不同问题类型之间以及问题类型内部搜索成功率在精确率和其他指标方面的差异，以确定其统计学意义。
在平均耗时或最终结果集中的平均记录数方面，对照组和治疗组之间未发现统计学上的显著差异。事实上，在治疗组条件下执行的任务在搜索序列中平均需要更多步骤，达到了统计学显著水平。在治疗组条件下执行的任务平均精确率在统计学上显著较低。个体患者任务和队列识别任务的相关性均值之间没有统计学上的显著差异。
这些发现与坦格等人的研究结果一致，他们发现临床叙述的粒度越粗，结果越好。本实验的结果对自动文本处理也有启示。复杂的标签集最终无法解决结构不规范的问题；临床文档中缺乏现有的结构本身就是一个重大限制。
