推进新生儿查长期跟踪:集成基于史诗的注册表,仪表板和高效的工作流程
Katherine Raboin1, Debra Ellis1, Ginger Nichols1
1Connecticut Newborn Screening Network, Connecticut Children's, Hartford, CT 06106, USA.
International journal of neonatal screening
|April 23, 2024
概括
康涅狄格州新生儿查网络通过使用Epic EHR系统改善了查疾病的婴儿的长期随访护理. 这一战略增强了患者访问和护理服务,改善了健康结果.
科学领域:
- 公共卫生 公共卫生
- 医疗信息学 医疗信息学
- 儿科护理 儿科护理
背景情况:
- 新生儿查 (NBS) 识别严重疾病的婴儿.
- 有效的长期随访 (LTFU) 护理对于改善NBS患者的健康结果至关重要.
- 在采用人口健康工具,在电子健康记录 (EHR) 中跟踪NBS患者护理方面存在挑战.
研究的目的:
- 在NBS患者的Epic EHR系统中实施和评估LTFU注册表模型.
- 改善专业护理团队采用人口健康工具.
- 加强通过NBS诊断的婴儿的护理和跟踪结果.
主要方法:
- 使用Epic EHR建立和优化LTFU注册表.
- 在康涅狄格州儿童医院为幼儿群体实施LTFU模型.
- 通过健康信息交换捕获外部数据,并纳入基于证据的指导方针.
- 进行干预的定性和定量评估.
主要成果:
- 早期儿童队列在最新访问的百分比 (%UTD) 中表现出显著和可持续的改善.
- 在访问和特定条件的绩效指标的%UTD中观察到积极的趋势.
- 定性评估证实了在专用支持下实现实践行为改变的可行性.
结论:
- 康涅狄格州NBS网络的模型证明了基于EHR的人口健康工具的成功采用.
- 这种方法有效地跟踪护理交付,并解决NBS患者的差距.
- 该模型显示了通过新生儿查识别的个体改善长期健康结果的希望.
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