Zolnoori Maryam, Williams Mark D, Leasure William B, Angstman Kurt B, Ngufor Che
Mayo Clinic, Rochester, MN, United States.
JMIR Res Protoc. 2020 Oct 29;9(10):e18366. doi: 10.2196/18366.
Patient-centered registries are essential in population-based clinical care for patient identification and monitoring of outcomes. Although registry data may be used in real time for patient care, the same data may further be used for secondary analysis to assess disease burden, evaluation of disease management and health care services, and research. The design of a registry has major implications for the ability to effectively use these clinical data in research.
This study aims to develop a systematic framework to address the data and methodological issues involved in analyzing data in clinically designed patient-centered registries.
The systematic framework was composed of 3 major components: visualizing the multifaceted and heterogeneous patient-centered registries using a data flow diagram, assessing and managing data quality issues, and identifying patient cohorts for addressing specific research questions.
Using a clinical registry designed as a part of a collaborative care program for adults with depression at Mayo Clinic, we were able to demonstrate the impact of the proposed framework on data integrity. By following the data cleaning and refining procedures of the framework, we were able to generate high-quality data that were available for research questions about the coordination and management of depression in a primary care setting. We describe the steps involved in converting clinically collected data into a viable research data set using registry cohorts of depressed adults to assess the impact on high-cost service use.
The systematic framework discussed in this study sheds light on the existing inconsistency and data quality issues in patient-centered registries. This study provided a step-by-step procedure for addressing these challenges and for generating high-quality data for both quality improvement and research that may enhance care and outcomes for patients.
INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/18366.
以患者为中心的登记系统在基于人群的临床护理中对于患者识别和结局监测至关重要。尽管登记系统数据可实时用于患者护理，但这些相同的数据还可进一步用于二次分析，以评估疾病负担、评价疾病管理和医疗服务以及开展研究。登记系统的设计对在研究中有效使用这些临床数据的能力具有重大影响。
本研究旨在开发一个系统框架，以解决在临床设计的以患者为中心的登记系统中分析数据所涉及的数据和方法学问题。
该系统框架由3个主要部分组成：使用数据流程图可视化多方面且异质性的以患者为中心的登记系统、评估和管理数据质量问题以及识别用于解决特定研究问题的患者队列。
通过使用作为梅奥诊所成人抑郁症协作护理计划一部分而设计的临床登记系统，我们能够证明所提出框架对数据完整性的影响。通过遵循该框架的数据清理和完善程序，我们能够生成高质量的数据，这些数据可用于有关初级保健环境中抑郁症协调和管理的研究问题。我们描述了使用抑郁症成年患者登记队列将临床收集的数据转换为可行研究数据集所涉及的步骤，以评估对高成本服务使用的影响。
本研究中讨论的系统框架揭示了以患者为中心的登记系统中现有的不一致性和数据质量问题。本研究提供了一个逐步程序，用于应对这些挑战并生成高质量数据，以用于质量改进和研究，这可能会改善患者的护理和结局。
国际注册报告识别号（IRRID）：DERR1-10.2196/18366。
