Department of Medical Informatics, Amsterdam UMC location University of Amsterdam, Amsterdam, The Netherlands.
Amsterdam Public Health, Digital Health, Methodology, Quality of Care, Amsterdam, The Netherlands.
Appl Clin Inform. 2023 May;14(3):455-464. doi: 10.1055/a-2067-5310. Epub 2023 Apr 1.
Medical data can be difficult to comprehend for patients, but only a limited number of patient-friendly terms and definitions are available to clarify medical concepts. Therefore, we developed an algorithm that generalizes diagnoses to more general concepts that do have patient-friendly terms and definitions in SNOMED CT. We implemented the generalizations, and diagnosis clarifications with synonyms and definitions that were already available, in the problem list of a hospital patient portal.
We aimed to assess the extent to which the clarifications cover the diagnoses in the problem list, the extent to which clarifications are used and appreciated by patient portal users, and to explore differences in viewing problems and clarifications between subgroups of users and diagnoses.
We measured the coverage of diagnoses by clarifications, usage of the problem list and the clarifications, and user, patient and diagnosis characteristics with aggregated, routinely available electronic health record and log file data. Additionally, patient portal users provided quantitative and qualitative feedback about the clarification quality.
Of all patient portal users who viewed diagnoses on their problem list ( = 2,660), 89% had one or more diagnoses with clarifications. In addition, 55% of patient portal users viewed the clarifications. Users who rated the clarifications ( = 108) considered the clarifications to be of good quality on average, with a median rating per patient of 6 (interquartile range: 4-7; from 1 very bad to 7 very good). Users commented that they found clarifications to be clear and recognized the clarifications from their own experience, but sometimes also found the clarifications incomplete or disagreed with the diagnosis itself.
This study shows that the clarifications are used and appreciated by patient portal users. Further research and development will be dedicated to the maintenance and further quality improvement of the clarifications.
医学数据对于患者来说可能难以理解，但能够澄清医学概念的患者友好术语和定义数量有限。因此，我们开发了一种算法，将诊断概括为更通用的概念，这些概念在 SNOMED CT 中确实有患者友好的术语和定义。我们在医院患者门户的问题列表中实现了这些概括以及同义词和定义的诊断澄清。
我们旨在评估澄清在多大程度上涵盖了问题列表中的诊断，患者门户用户使用和欣赏澄清的程度，以及探索用户和诊断亚组在查看问题和澄清方面的差异。
我们使用聚合的、常规可用的电子健康记录和日志文件数据来衡量诊断被澄清的程度、问题列表和澄清的使用情况以及用户、患者和诊断特征。此外，患者门户用户提供了有关澄清质量的定量和定性反馈。
在查看其问题列表上的诊断的所有患者门户用户中（n = 2660），89%的患者有一个或多个诊断有澄清。此外，55%的患者门户用户查看了澄清。对澄清进行评分的用户（n = 108）平均认为澄清质量良好，每位患者的中位数评分为 6（四分位距：4-7；1 分表示非常差，7 分表示非常好）。用户评论说，他们发现澄清很清楚，并从自己的经验中认识到澄清，但有时也发现澄清不完整或不同意诊断本身。
本研究表明，澄清得到了患者门户用户的使用和赞赏。进一步的研究和开发将致力于澄清的维护和进一步质量改进。
