Open AccessA Systematic Framework for Analyzing Observation Data in Patient-Centered Registries: Case Study for Patients With Depression
Author(s) -
Maryam Zolnoori,
Mark D. Williams,
William Leasure,
Kurt B. Angstman,
Che Ngufor
Publication year - 2020
Publication title -
jmir research protocols
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.378
H-Index - 9
ISSN - 1929-0748
DOI - 10.2196/18366
Subject(s) - medicine , data quality , data collection , collaborative care , health care , identification (biology) , data science , service (business) , computer science , family medicine , primary care , statistics , botany , economy , mathematics , economic growth , economics , biology
Background 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. Objective 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. Methods 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. Results 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. Conclusions 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