School of Biomedical Informatics, the University of Texas Health Science Center at Houston, 7000 Fannin St., Houston, TX, 77030, USA.
BMC Med Inform Decis Mak. 2017 Jul 5;17(Suppl 2):75. doi: 10.1186/s12911-017-0467-8.
The most important knowledge in the field of patient safety is regarding the prevention and reduction of patient safety events (PSE) during treatment and care. The similarities and patterns among the events may otherwise go unnoticed if they are not properly reported and analyzed. There is an urgent need for developing a PSE reporting system that can dynamically measure the similarities of the events and thus promote event analysis and learning effect.
In this study, three prevailing algorithms of semantic similarity were implemented to measure the similarities of the 366 PSE annotated by the taxonomy of The Agency for Healthcare Research and Quality (AHRQ). The performance of each algorithm was then evaluated by a group of domain experts based on a 4-point Likert scale. The consistency between the scales of the algorithms and experts was measured and compared with the scales randomly assigned. The similarity algorithms and scores, as a self-learning and self-updating module, were then integrated into the system.
The result shows that the similarity scores reflect a high consistency with the experts' review than those randomly assigned. Moreover, incorporating the algorithms into our reporting system enables a mechanism to learn and update based upon PSE similarity.
In conclusion, integrating semantic similarity algorithms into a PSE reporting system can help us learn from previous events and provide timely knowledge support to the reporters. With the knowledge base in the PSE domain, the new generation reporting system holds promise in educating healthcare providers and preventing the recurrence and serious consequences of PSE.
患者安全领域最重要的知识是关于预防和减少治疗和护理过程中的患者安全事件（PSE）。如果不妥善报告和分析这些事件，否则可能会忽略它们之间的相似性和模式。因此，迫切需要开发一种能够动态测量事件相似性的 PSE 报告系统，从而促进事件分析和学习效果。
在这项研究中，实现了三种流行的语义相似性算法来衡量 366 个由美国医疗保健研究与质量局（AHRQ）分类法注释的 PSE 的相似性。然后，一组领域专家根据 4 分李克特量表对每种算法的性能进行评估。然后测量算法和专家量表之间的一致性，并与随机分配的量表进行比较。将相似性算法和分数作为一个自我学习和自我更新的模块，然后集成到系统中。
结果表明，相似性得分与专家审查的一致性高于随机分配的得分。此外，将算法纳入我们的报告系统可以使系统根据 PSE 相似性实现学习和更新的机制。
总之，将语义相似性算法集成到 PSE 报告系统中可以帮助我们从以前的事件中学习，并为报告者提供及时的知识支持。有了 PSE 领域的知识库，新一代报告系统有望教育医疗保健提供者，并防止 PSE 的再次发生和严重后果。
