医院信息系统中的数据质量:分析德国一家地区医院30年患者数据的经验教训
Stefan Förstel1, Markus Förstel2, Markus Gallistl3
1Department of Industrial Engineering and Health, Technical University Amberg-Weiden, Hetzenrichter Weg 15, Weiden in der Oberpfalz, 92637, Bavaria, Germany; Department Artificial Intelligence in Biomedical Engineering, Technische Fakultät, Friedrich-Alexander Universität Erlangen-Nürnberg, Carl-Thiersch-Straße 2b, Erlangen, 91052, Bavaria, Germany.
International journal of medical informatics
|October 2, 2024
概括
医院信息系统 (HIS) 的数据质量问题影响了可用性. 这项研究通过识别和解决不一致性,改善了大量患者数据集,使其更可靠地进行分析.
科学领域:
- 医疗保健信息学 医疗保健信息学
- 数据科学在医学中的数据科学
- 健康数据管理 管理健康数据
背景情况:
- 医院信息系统 (HIS) 对现代医疗保健至关重要,改善了患者护理和效率.
- 数字化转型增加了数据量,需要强大的数据管理和质量保证.
- 确保HIS中的数据完整性对于可靠的医疗保健运营和研究至关重要.
研究的目的:
- 为了解决德国一家地区医院HIS的患者识别器数据质量问题.
- 提高管理患者数据输入的准确性和一致性.
- 提高HIS数据在临床和分析应用中的实用性.
主要方法:
- 从HIS中编程清理和分析超过200万个患者记录.
- 调查的重点是患者的入院,出院和地理数据.
- 除了数据分析技术,还利用了专家采访.
主要成果:
- 大约25%的患者数据集由于错误和不一致而无法使用.
- 一个全面的数据清理过程显著改善了数据集的实用性.
- 确定了关键的数据质量问题:可变的模糊性和系统预期和实际使用之间的差异.
结论:
- 提高医疗保健信息系统中的数据质量至关重要.
- 从HIS提取的数据需要在机器学习应用程序之前仔细审查和清理.
- 改进的数据质量提高了HIS数据的可靠性和可用性,用于临床和分析目的.
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