In the healthcare sector, information is the most important aspect, and the human body in particular is the major source of data production: as a result, the new challenge for world healthcare is to take advantage of these huge amounts of data de-structured among themselves. In order to benefit from this advantage, technology offers a solution called Big Data Analysis that allows the management of large amounts of data of a different nature and coming from different sources of a “computerized” healthcare, as there are considerable changes made by the input of digital technology in all major health areas.

Clinical intelligence consists of all the analytical methods made possible through the use of computer tools, in all the processes and disciplines of extraction and transformation of crude clinical data into significant insights, new purposes and knowledge that provide greater clinical efficacy and best health pronouncements about past performance, current operations and future events. It can therefore be stated that clinical intelligence, through patient data analysis, will become a standard operating procedure that will address all aspects of care delivery.

The purpose of this paper is to present clinical intelligence approaches through Data Mining and Process Mining, showing the differences between these two methodologies applied to perform “real process” extraction to be compared with the procedures in the corporate compliance template (the so called “Model 231”) by “conformance checking”.

在医疗保健领域，信息是最重要的方面，尤其是人体是数据产生的主要来源：因此，世界医疗保健面临的新挑战是要利用这些庞大的数据结构他们自己。为了受益于此优势，技术提供了一种称为“大数据分析”的解决方案，该解决方案允许管理性质不同且来自“计算机化”医疗保健不同来源的大量数据，因为输入进行了相当多的更改所有主要健康领域的数字技术。

临床情报包括通过使用计算机工具，在将原始临床数据的提取和转化为重要见解，新目的和新知识的所有过程和学科中可能使用的所有分析方法，这些见解，新目的和知识可提供更高的临床疗效和对过去的最佳健康声明绩效，当前运营和未来事件。因此可以说，通过患者数据分析，临床智能将成为解决护理交付各个方面的标准操作程序。

本文的目的是通过数据挖掘和过程挖掘来介绍临床情报方法，展示这两种用于执行“真实过程”提取的方法之间的差异，以便与公司合规性模板中的程序进行比较（所谓的“模型” 231”）中的“符合性检查”。