Domain

Clinical Informatics

Type

Conceptual or Process Model/Framework

Theme

effectiveness; quality

Start Date

7-6-2014 1:15 PM

End Date

7-6-2014 2:45 PM

Structured Abstract

Introduction

Effectiveness and outcomes research may utilize administrative, clinical, and patient reported outcomes data. The data may be captured all electronically or may require a combination of database queries, data abstraction, and surveys. Planning and documentation are essential to ensure that the variety of data sources used in effectiveness and outcomes research can be assembled into an analytic data set and that the methods used are clearly documented for reporting.

Background/Context

Data management typically includes handling, monitoring, and controlling data as it is collected, processed, combined, and interpreted. Data management processes may impact both the expense of a study and the validity and precision of the estimates of effect in the analysis. Effectiveness and outcomes research has additional challenges over classic prospective clinical trials due to the complexity of using secondary data sources designed for clinical and administrative use. Recent papers have outlined these challenges and advocated for additional data management tools, definitions of data quality, and standardized reporting of data sources, data origins, and processing. However, no papers have provided practical tools for planning and documenting the data management processes to allow for reporting and reuse of analytic data sets. This framework begins to address those needs.

Innovation

We created a pragmatic framework that a researcher can use to plan and implement data management in effectiveness and outcomes research. This framework is based on project management theory and the principles found in the US government regulations for data management in clinical trials research, but is targeted towards those study types commonly encountered by effectiveness researchers: non-FDA regulated studies. The framework has two components: a visual outline of outputs (deliverables), and a comprehensive set of customizable tools for planning and executing data management. These pragmatic tools include checklists, tables, diagrams, and examples. They provide researchers with a template that can be adapted based on the unique needs of their study. Together these tools lead a researcher through a standardized, documented approach to planning data management.

Discussion

Our team has learned the importance of planning and documenting data management practices throughout many complex projects. Although data management documentation is not required by most journals, it is critical to analyze the strengths and limitations of research data. Further, there are no shortcuts, and research budgets are rarely adequate. This framework can be used to facilitate the understanding of the complexity and cost of using secondary data for effectiveness and outcomes research. Finally, this framework can be extended to quality improvement and other projects essential for building a learning health system.

Next Steps

In the future, these tools should be coupled with a comprehensive written guide to planning data management for comparative and outcomes research. In addition, we want to collaborate with other researchers to refine and extend the tools. Finally, we would like these tools to enable and educate researchers new to the field of comparative and outcomes research.

Acknowledgements

N/A

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License.

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Jun 7th, 1:15 PM Jun 7th, 2:45 PM

A Pragmatic Framework for Planning Data Management in Effectiveness and Outcomes Research

Introduction

Effectiveness and outcomes research may utilize administrative, clinical, and patient reported outcomes data. The data may be captured all electronically or may require a combination of database queries, data abstraction, and surveys. Planning and documentation are essential to ensure that the variety of data sources used in effectiveness and outcomes research can be assembled into an analytic data set and that the methods used are clearly documented for reporting.

Background/Context

Data management typically includes handling, monitoring, and controlling data as it is collected, processed, combined, and interpreted. Data management processes may impact both the expense of a study and the validity and precision of the estimates of effect in the analysis. Effectiveness and outcomes research has additional challenges over classic prospective clinical trials due to the complexity of using secondary data sources designed for clinical and administrative use. Recent papers have outlined these challenges and advocated for additional data management tools, definitions of data quality, and standardized reporting of data sources, data origins, and processing. However, no papers have provided practical tools for planning and documenting the data management processes to allow for reporting and reuse of analytic data sets. This framework begins to address those needs.

Innovation

We created a pragmatic framework that a researcher can use to plan and implement data management in effectiveness and outcomes research. This framework is based on project management theory and the principles found in the US government regulations for data management in clinical trials research, but is targeted towards those study types commonly encountered by effectiveness researchers: non-FDA regulated studies. The framework has two components: a visual outline of outputs (deliverables), and a comprehensive set of customizable tools for planning and executing data management. These pragmatic tools include checklists, tables, diagrams, and examples. They provide researchers with a template that can be adapted based on the unique needs of their study. Together these tools lead a researcher through a standardized, documented approach to planning data management.

Discussion

Our team has learned the importance of planning and documenting data management practices throughout many complex projects. Although data management documentation is not required by most journals, it is critical to analyze the strengths and limitations of research data. Further, there are no shortcuts, and research budgets are rarely adequate. This framework can be used to facilitate the understanding of the complexity and cost of using secondary data for effectiveness and outcomes research. Finally, this framework can be extended to quality improvement and other projects essential for building a learning health system.

Next Steps

In the future, these tools should be coupled with a comprehensive written guide to planning data management for comparative and outcomes research. In addition, we want to collaborate with other researchers to refine and extend the tools. Finally, we would like these tools to enable and educate researchers new to the field of comparative and outcomes research.