探索低严重性评级医院事件报告对机器学习的适用性
Rebecca Miriam Jedwab1, Leonard Hoon, Caroline Luu
1Author Affiliations : Nursing and Midwifery Informatics, EMR and Informatics Program, Monash Health, Clayton (Dr Jedwab); Applied Artificial Intelligence Institute, Deakin University, Geelong (Dr Hoon); Nursing and Midwifery, Western Health, Melbourne (Ms Luu); and Victorian Health Complaints Commissioner, School of Nursing and Midwifery, Deakin University, Geelong (Dr Redley).
在医疗保健中,低严重程度的事件报告往往被忽视. 这项研究发现,目前的报告缺乏机器学习识别危害预防干预措施所需的结构化数据.
科学领域:
- 医疗保健的质量和安全
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
背景情况:
- 电子事件报告系统对于改善医疗保健质量至关重要.
- 低严重性事件报告众多,并且由于资源和认知限制,很少被分析.
研究的目的:
- 评估低严重性事件报告对机器学习应用的适用性.
- 通过数据分析,识别潜在的预防伤害干预措施.
主要方法:
- 定性描述性研究,分析一年的低严重性事件报告数据集.
- 事件报告文档工作流程的建模.
- 与护理和医疗保健质量和安全专家一起探索调查结果.
主要成果:
- 报告存在数据重复,主观性,过多的选项和缺乏上下文的问题.
- 这些问题使得数据不适合当前的机器学习分析.
- 专家强调需要提高数据质量.
结论:
- 目前的低严重性事件报告数据并没有针对机器学习进行优化.
- 建议重新设计涉及最终用户的系统,以提高数据的意义.
- 未来的工作将集中在机器学习适合性分析上.
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