Beitia Anton Oscar, Lowry Tina, Vreeman Daniel J, Loo George T, Delman Bradley N, Thum Frederick L, Slovis Benjamin H, Shapiro Jason S
Department of Emergency Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States.
Center for Biomedical Informatics, Regenstrief Institute, Indianapolis, IN, United States.
JMIR Med Inform. 2017 Dec 14;5(4):e49. doi: 10.2196/medinform.8765.
A health information exchange (HIE)-based prior computed tomography (CT) alerting system may reduce avoidable CT imaging by notifying ordering clinicians of prior relevant studies when a study is ordered. For maximal effectiveness, a system would alert not only for prior same CTs (exams mapped to the same code from an exam name terminology) but also for similar CTs (exams mapped to different exam name terminology codes but in the same anatomic region) and anatomically proximate CTs (exams in adjacent anatomic regions). Notification of previous same studies across an HIE requires mapping of local site CT codes to a standard terminology for exam names (such as Logical Observation Identifiers Names and Codes [LOINC]) to show that two studies with different local codes and descriptions are equivalent. Notifying of prior similar or proximate CTs requires an additional mapping of exam codes to anatomic regions, ideally coded by an anatomic terminology. Several anatomic terminologies exist, but no prior studies have evaluated how well they would support an alerting use case.
The aim of this study was to evaluate the fitness of five existing standard anatomic terminologies to support similar or proximate alerts of an HIE-based prior CT alerting system.
We compared five standard anatomic terminologies (Foundational Model of Anatomy, Systematized Nomenclature of Medicine Clinical Terms, RadLex, LOINC, and LOINC/Radiological Society of North America [RSNA] Radiology Playbook) to an anatomic framework created specifically for our use case (Simple ANatomic Ontology for Proximity or Similarity [SANOPS]), to determine whether the existing terminologies could support our use case without modification. On the basis of an assessment of optimal terminology features for our purpose, we developed an ordinal anatomic terminology utility classification. We mapped samples of 100 random and the 100 most frequent LOINC CT codes to anatomic regions in each terminology, assigned utility classes for each mapping, and statistically compared each terminology's utility class rankings. We also constructed seven hypothetical alerting scenarios to illustrate the terminologies' differences.
Both RadLex and the LOINC/RSNA Radiology Playbook anatomic terminologies ranked significantly better (P<.001) than the other standard terminologies for the 100 most frequent CTs, but no terminology ranked significantly better than any other for 100 random CTs. Hypothetical scenarios illustrated instances where no standard terminology would support appropriate proximate or similar alerts, without modification.
LOINC/RSNA Radiology Playbook and RadLex's anatomic terminologies appear well suited to support proximate or similar alerts for commonly ordered CTs, but for less commonly ordered tests, modification of the existing terminologies with concepts and relations from SANOPS would likely be required. Our findings suggest SANOPS may serve as a framework for enhancing anatomic terminologies in support of other similar use cases.
基于健康信息交换（HIE）的既往计算机断层扫描（CT）警报系统，可通过在下达检查医嘱时通知开单临床医生既往相关研究，减少不必要的CT成像。为实现最大效果，该系统不仅应针对既往相同的CT（根据检查名称术语映射到相同代码的检查）发出警报，还应针对相似的CT（根据不同检查名称术语代码但在相同解剖区域映射的检查）以及解剖学上相邻的CT（相邻解剖区域的检查）发出警报。在HIE中通知既往相同的研究，需要将本地机构的CT代码映射到检查名称的标准术语（如逻辑观察标识符名称和代码[LOINC]），以表明两个具有不同本地代码和描述的研究是等效的。通知既往相似或相邻的CT则需要将检查代码额外映射到解剖区域，理想情况下由解剖学术语进行编码。虽然存在多种解剖学术语，但此前尚无研究评估它们对警报用例的支持程度。
本研究旨在评估五种现有标准解剖学术语对基于HIE的既往CT警报系统中相似或相邻警报的支持适用性。
我们将五种标准解剖学术语（解剖学基础模型、医学临床术语系统命名法、RadLex、LOINC以及LOINC/北美放射学会[RSNA]放射学手册）与专门为我们的用例创建的解剖框架（用于邻近或相似性的简单解剖本体[SANOPS]）进行比较，以确定现有术语是否无需修改就能支持我们的用例。基于对我们目的的最佳术语特征评估，我们制定了一个序数解剖学术语效用分类。我们将100个随机的和100个最常用的LOINC CT代码样本映射到每个术语的解剖区域，为每个映射分配效用类别，并对每个术语的效用类别排名进行统计学比较。我们还构建了七个假设警报场景来说明术语之间的差异。
对于100个最常用的CT，RadLex和LOINC/RSNA放射学手册解剖学术语的排名均显著优于其他标准术语（P<.001），但对于100个随机CT，没有一个术语的排名显著优于其他术语。假设场景说明了在不进行修改的情况下，没有标准术语能支持适当的相邻或相似警报的情况。
LOINC/RSNA放射学手册和RadLex的解剖学术语似乎非常适合为常用CT的相邻或相似警报提供支持，但对于不太常用的检查，可能需要用SANOPS的概念和关系对现有术语进行修改。我们的研究结果表明，SANOPS可作为增强解剖学术语以支持其他类似用例的框架。
