针对良好的健康数据建模 (GHDM) 的建议 - - 专家采访结果
Lena Elgert1, Jendrik Richter2, Matthias Katzensteiner3
1Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Hannover, Germany.
制定明确的健康数据建模建议对于有效的数据管理至关重要. 本研究确定了管理,建模和标准等关键领域,以指导未来的健康数据项目.
科学领域:
- 医疗信息学 医疗信息学
- 数据科学数据科学数据科学
- 公共卫生 公共卫生
背景情况:
- 有效的数据模型对于健康数据的收集,聚合,整合和分析至关重要.
- 缺乏公开可用的建模建议,阻碍了项目中系统的健康数据管理.
研究的目的:
- 对健康数据建模进行调查和提出建议.
- 解决可访问的健康数据建模指南中的差距.
主要方法:
- 专家采访的定性内容分析.
- 探索影响健康数据建模的框架条件.
主要成果:
- 为健康数据建模建议生成了八个假设.
- 确定了关键类别:治理,建模和标准.
- 框架条件包括角色,国际合作,教育和政治影响.
结论:
- 这些发现为更广泛的数据收集计划提供了基础.
- 结果有助于制定健康数据建模的实际建议.
- 强调治理,标准和在健康数据倡议中的合作的重要性.
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