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A Systematic Framework for Analyzing Observation Data in Patient-Centered Registries: Case Study for Patients With Depression

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, evaluati...

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Autores principales: Zolnoori, Maryam, Williams, Mark D, Leasure, William B, Angstman, Kurt B, Ngufor, Che
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7661226/
https://www.ncbi.nlm.nih.gov/pubmed/33118958
http://dx.doi.org/10.2196/18366
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author Zolnoori, Maryam
Williams, Mark D
Leasure, William B
Angstman, Kurt B
Ngufor, Che
author_facet Zolnoori, Maryam
Williams, Mark D
Leasure, William B
Angstman, Kurt B
Ngufor, Che
author_sort Zolnoori, Maryam
collection PubMed
description 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
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spelling pubmed-76612262020-11-19 A Systematic Framework for Analyzing Observation Data in Patient-Centered Registries: Case Study for Patients With Depression Zolnoori, Maryam Williams, Mark D Leasure, William B Angstman, Kurt B Ngufor, Che JMIR Res Protoc Protocol 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 JMIR Publications 2020-10-29 /pmc/articles/PMC7661226/ /pubmed/33118958 http://dx.doi.org/10.2196/18366 Text en ©Maryam Zolnoori, Mark D Williams, William B Leasure, Kurt B Angstman, Che Ngufor. Originally published in JMIR Research Protocols (http://www.researchprotocols.org), 29.10.2020. https://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Research Protocols, is properly cited. The complete bibliographic information, a link to the original publication on http://www.researchprotocols.org, as well as this copyright and license information must be included.
spellingShingle Protocol
Zolnoori, Maryam
Williams, Mark D
Leasure, William B
Angstman, Kurt B
Ngufor, Che
A Systematic Framework for Analyzing Observation Data in Patient-Centered Registries: Case Study for Patients With Depression
title A Systematic Framework for Analyzing Observation Data in Patient-Centered Registries: Case Study for Patients With Depression
title_full A Systematic Framework for Analyzing Observation Data in Patient-Centered Registries: Case Study for Patients With Depression
title_fullStr A Systematic Framework for Analyzing Observation Data in Patient-Centered Registries: Case Study for Patients With Depression
title_full_unstemmed A Systematic Framework for Analyzing Observation Data in Patient-Centered Registries: Case Study for Patients With Depression
title_short A Systematic Framework for Analyzing Observation Data in Patient-Centered Registries: Case Study for Patients With Depression
title_sort systematic framework for analyzing observation data in patient-centered registries: case study for patients with depression
topic Protocol
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7661226/
https://www.ncbi.nlm.nih.gov/pubmed/33118958
http://dx.doi.org/10.2196/18366
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