Information Technology Research Department, Santiago Compostela University, Santiago, Spain.
Knowledge Management and Engineering Research Group, Universidade Atlântica, Barcarena, Portugal.
J Med Syst. 2019 Jan 12;43(2):41. doi: 10.1007/s10916-019-1162-3.
Conservative practices, such as manual registry have limited scope regarding preoperative, intraoperative and postoperative decision making, knowledge discovery, analytical techniques and knowledge integration into patient care. To maximize quality and value, perioperative care is changing through new technological developments. In this context, knowledge management practices will enable future transformation and enhancements in healthcare services. By performing a data science and knowledge management research in the perioperative department at Hospital Dr. Nélio Mendonça between 2013 and 2015, this paper describes its principal results. This study showed perioperative decision-making improvement by integrating data science tools on the perioperative electronic system (PES). Before the PES implementation only 1,2% of the nurses registered the preoperative visit and after 87,6% registered it. Regarding the patient features it was possible to assess anxiety and pain levels. A future conceptual model for perioperative decision support systems grounded on data science should be considered as a knowledge management tool.
保守的做法,如手动登记,在术前、术中、术后的决策、知识发现、分析技术和知识整合到患者护理方面,其范围有限。为了最大限度地提高质量和价值,围手术期护理正在通过新的技术发展而改变。在这种情况下,知识管理实践将使医疗服务在未来发生转变和改进。通过在 2013 年至 2015 年期间在 Hospital Dr. Nélio Mendonça 的围手术期部门进行数据科学和知识管理研究,本文描述了其主要结果。这项研究通过在围手术期电子系统 (PES) 上集成数据科学工具,改善了围手术期决策。在 PES 实施之前,只有 1.2%的护士登记了术前访视,而实施后有 87.6%的护士进行了登记。至于患者特征,可以评估焦虑和疼痛程度。应该考虑基于数据科学的围手术期决策支持系统的未来概念模型作为知识管理工具。