Development of a relational database to capture and merge clinical history with the quantitative results of radionuclide renography.
Development of a relational database to capture and merge clinical history with the quantitative results of radionuclide renography.
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DOI：
10.2967/jnmt.111.101477
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发表时间：
2012-12
影响因子：
1.3
通讯作者：
Taylor AT
中科院分区：
文献类型：
--
作者：
Folks RD;Savir-Baruch B;Garcia EV;Verdes L;Taylor AT
我们的目标是设计和实现一个临床病史数据库，该数据库能够链接到我们的99mTc-巯基乙酰三甘氨酸（MAG 3）肾脏扫描定量结果数据库，并为医生或我们的软件决策支持系统导出数据摘要。对于数据库开发，我们使用了一个商业程序。其他软件是以交互式数据语言开发的。使用商业程序的内部增强处理MAG3研究。关系数据库有3个部分：所有肾脏扫描的列表（RENAL数据库）、一组具有定量处理结果的患者（Q2数据库）和Q2中包含从医院信息系统手动转录的临床数据的患者子集（CLINICAL数据库）。为了测试观察者之间的差异，第二名医生转录员审查了医院信息系统中随机选择的50名患者，并将2项临床数据项制成表格：肾积水和当前支架的存在。CLINICAL数据库分阶段开发，包含342个字段，包括人口统计学信息、临床病史和多达11个放射学程序的结果。脚本算法用于可靠地匹配Q2和CLINICAL中存在的记录。然后，交互式数据语言程序将来自两个数据库的数据组合成XML（可扩展标记语言）文件，供决策支持系统使用。构建并保存文本文件以供医生查看。RENAL包含2，222条记录，Q2包含456条记录，CLINICAL包含152条记录。观察者间变异性检验发现，2名观察者之间存在或不存在输尿管支架的匹配率为95%（κ = 0.52），基于住院和临床访视的叙述性总结的肾盂积水匹配率为75%（κ = 0.41），基于成像报告的肾盂积水匹配率为92%（κ = 0.84）。我们已经开发了一个关系数据库系统，将MAG3图像处理的定量结果与从医院信息系统获得的临床记录相结合。我们还开发了一种格式化临床病史的方法，供医生审查并导出到决策支持系统。我们确定了几个陷阱，包括这样一个事实，即由知识渊博的转录员从医院信息系统中提取的重要文本信息可能会显示出大量的观察者间差异，特别是当记录检索是基于叙述性临床记录时。
Our objective was to design and implement a clinical history database capable of linking to our database of quantitative results from 99mTc-mercaptoacetyltriglycine (MAG3) renal scans and export a data summary for physicians or our software decision support system. For database development, we used a commercial program. Additional software was developed in Interactive Data Language. MAG3 studies were processed using an in-house enhancement of a commercial program. The relational database has 3 parts: a list of all renal scans (the RENAL database), a set of patients with quantitative processing results (the Q2 database), and a subset of patients from Q2 containing clinical data manually transcribed from the hospital information system (the CLINICAL database). To test interobserver variability, a second physician transcriber reviewed 50 randomly selected patients in the hospital information system and tabulated 2 clinical data items: hydronephrosis and presence of a current stent. The CLINICAL database was developed in stages and contains 342 fields comprising demographic information, clinical history, and findings from up to 11 radiologic procedures. A scripted algorithm is used to reliably match records present in both Q2 and CLINICAL. An Interactive Data Language program then combines data from the 2 databases into an XML (extensible markup language) file for use by the decision support system. A text file is constructed and saved for review by physicians. RENAL contains 2,222 records, Q2 contains 456 records, and CLINICAL contains 152 records. The interobserver variability testing found a 95% match between the 2 observers for presence or absence of ureteral stent (κ = 0.52), a 75% match for hydronephrosis based on narrative summaries of hospitalizations and clinical visits (κ = 0.41), and a 92% match for hydronephrosis based on the imaging report (κ = 0.84). We have developed a relational database system to integrate the quantitative results of MAG3 image processing with clinical records obtained from the hospital information system. We also have developed a methodology for formatting clinical history for review by physicians and export to a decision support system. We identified several pitfalls, including the fact that important textual information extracted from the hospital information system by knowledgeable transcribers can show substantial interobserver variation, particularly when record retrieval is based on the narrative clinical records.