Eng Donna, Ospelt Emma, Miyazaki Brian, McDonough Ryan, Indyk Justin A, Wolf Risa, Lyons Sarah, Neyman Anna, Fogel Naomi R, Basina Marina, Gallagher Mary Pat, Ebekozien Osagie, Alonso G Todd, Jones Nana-Hawa Yayah, Lee Joyce M
Pediatric Endocrinology, Helen DeVos Children's Hospital, Michigan State University College of Human Medicine, Grand Rapids, MI, USA.
Quality Improvement and Population Health, T1D Exchange, Boston, MA, USA.
J Diabetes Sci Technol. 2024 Jan;18(1):30-38. doi: 10.1177/19322968231214539. Epub 2023 Nov 23.
Systematic and comprehensive data acquisition from the electronic health record (EHR) is critical to the quality of data used to improve patient care. We described EHR tools, workflows, and data elements that contribute to core quality metrics in the Type 1 Diabetes Exchange Quality Improvement Collaborative (T1DX-QI).
We conducted interviews with quality improvement (QI) representatives at 13 T1DX-QI centers about their EHR tools, clinic workflows, and data elements.
All centers had access to structured data tools, nine had access to patient questionnaires and two had integration with a device platform. There was significant variability in EHR tools, workflows, and data elements, thus the number of available metrics per center ranged from four to 17 at each site. Thirteen centers had information about glycemic outcomes and diabetes technology use. Seven centers had measurements of additional self-management behaviors. Centers captured patient-reported outcomes including social determinants of health (n = 9), depression (n = 11), transition to adult care (n = 7), and diabetes distress (n = 3). Various stakeholders captured data including health care professionals, educators, medical assistants, and QI coordinators. Centers that had a paired staffing model in clinic encounters distributed the burden of data capture across the health care team and was associated with a higher number of available data elements.
The lack of standardization in EHR tools, workflows, and data elements captured resulted in variability in available metrics across centers. Further work is needed to support measurement and subsequent improvement in quality of care for individuals with type 1 diabetes.
从电子健康记录(EHR)中进行系统且全面的数据采集对于用于改善患者护理的数据质量至关重要。我们描述了有助于1型糖尿病交换质量改进协作组(T1DX-QI)核心质量指标的EHR工具、工作流程和数据元素。
我们采访了13个T1DX-QI中心的质量改进(QI)代表,了解他们的EHR工具、临床工作流程和数据元素。
所有中心都可使用结构化数据工具,9个中心可使用患者问卷,2个中心与设备平台集成。EHR工具、工作流程和数据元素存在显著差异,因此每个中心可用指标的数量在每个站点从4个到17个不等。13个中心有血糖结果和糖尿病技术使用的信息。7个中心对其他自我管理行为进行了测量。各中心收集了患者报告的结果,包括健康的社会决定因素(n = 9)、抑郁(n = 11)、向成人护理的过渡(n = 7)和糖尿病困扰(n = 3)。包括医疗保健专业人员、教育工作者、医疗助理和QI协调员在内的各种利益相关者收集了数据。在临床诊疗中采用配对人员配置模式的中心将数据采集负担分散到了医疗团队中,并且与更多可用数据元素相关联。
EHR工具、工作流程和所采集数据元素缺乏标准化导致各中心可用指标存在差异。需要进一步开展工作以支持对1型糖尿病患者护理质量的测量及后续改进。