JMIR Publications

ABSTRACT

Background:

Patient-centered registries are essential for patient selection and identification of vulnerable population that can be used to assess disease burden, evaluation of disease management and healthcare services, and conduction of comparative effectiveness, cost-effectiveness, and outcomes research. Appropriate design and implementation of patient-centered registry is therefore critical.

Objective:

This study aimed to develop a data analysis framework to address data and methodological issues involved in analyzing data in patient-centered registries.

Methods:

The systematic framework is composed of three major components: visualizing multi-faceted and heterogeneous patient-centered registry using Data Flow Diagram; assessing and managing data quality issues; and identifying patient cohorts for addressing specific research questions.

Results:

We demonstrated the usability of the proposed framework using the IBH (Integrated behavior health) patient-centered registry implemented at Mayo Clinic, which built on the collaborative care model to manage and accelerate the process of treatment for patients diagnosed with moderate to severe depression. By following the data cleaning and refining procedures of the framework, we generated high-quality data of answering important research questions about the coordination and management of depression in a primary care setting. Specifically, we selected a cohort of patients for testing hypotheses related to comparative effectiveness of care models of depression.

Conclusions:

The systematic framework discussed in this study shed light on the existing inconsistency and data quality issues in patient-centered registries, and provided a step-by-step procedure for addressing these challenges and for generating high quality data to enhance care and outcomes for patients.