持续改善医疗保健管理数据质量的挑战和机会
Yili Zhang1, Jennifer A Callaghan-Koru2, Güneş Koru3
1Innovation Center for Biomedical Informatics, Georgetown University, Washington, DC 20007, United States.
医疗保健管理数据质量问题阻碍了COVID-19追踪和成本控制等任务的利用. 提高数据质量需要通过培训和标准化解决缺陷特征,当前流程和挑战.
科学领域:
- 医疗信息学 医疗信息学
- 数据管理数据管理
- 公共卫生管理公共卫生管理.
背景情况:
- 医疗保健管理数据中的数据质量问题限制了其有效使用.
- 问题范围从COVID-19接触者追踪到医疗保健成本控制.
研究的目的:
- 描述医疗保健管理数据质量改进的当前做法.
- 探索持续提高数据质量的挑战和机会.
主要方法:
- 使用半结构面试与国家卫生机构的数据管理员进行定性方法.
- 使用框架方法分析医疗补助申请和退款数据.
主要成果:
- 在缺陷特征,流程/人员问题,挑战和机遇方面确定了16个主题.
- 数据缺陷是可变的,模糊的,难以解决的,通常需要跨组织的努力.
- 临时的手动流程带来了挫折;挑战包括通信,遗留系统和数据验证.
- 改进的机会包括培训,工具支持和数据定义标准化.
结论:
- 调查结果为旨在成为学习健康组织的机构提供了见解.
- 通过有针对性的质量改进,可以实现有效和高效的数据资产利用.
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