An erroneous trend that is sometimes observed while planning research in healthcare is starting a research project before ensuring that the data quality management processes are in place, which makes a perfect case of ‘putting the cart before the horse’.
It tempts one to propose the question — “what is research without data?” Research without data is simply a hypothesis. It is an idea or theory that has not been tested or supported by any evidence. Data provides the foundation for research by providing empirical evidence that can be used to test hypotheses and theories. Through the analysis of data, researchers can identify patterns, relationships, and trends that can help to explain or predict behaviour, outcomes, or phenomena. Therefore, data is a critical component of research.
Project planning is an essential aspect of any health research project, and there are several key things that researchers should consider before starting a project. However, dedication is often given more to defining the research question, choosing an appropriate study design, identifying potential funding sources, developing a budget, ensuring all necessary ethical approvals are obtained, and developing a data collection plan, including data sources and sampling strategies. Midway the research team may realise that they had not paid much attention to data management or perhaps did not include it in their project strategy to a significant extent.
Related: Data-driven future of healthcare
If data quality management is not applied before conducting health research, it can lead to inaccurate, inconsistent, or incomplete data. Poor quality data can lead to incorrect conclusions, invalid results, and flawed recommendations. Therefore, it is essential to ensure that data quality management processes are in place before starting any research project.
So, how can researchers, project companies and project manager ensure to include data quality management to their project strategies to a significant extent? By applying data quality management before conducting health research, researchers can identify potential data quality issues, correct them, strengthen data privacy and ensure that the data is accurate, reliable, consistent, and complete.
Healthcare companies must realise that there is a valid need to define data quality standards that align with their research goals while planning and strategising on the research project with project managers. Proper documentation of the process for data integration, sharing, security, privacy, and literacy must be implemented. This will help ensure that the data is accurate, secure, complete, and consistent.
The other step includes conducting regular data quality assessments. Companies should conduct regular data quality assessments to identify and address any issues with data quality. This may involve using statistical techniques to identify outliers and anomalies and comparing data to external sources to verify accuracy.
Involving stakeholders in data quality management such as data collectors, analysts, and end-users, in data quality management activities to ensure that everyone understands the importance of data quality and their role in ensuring it.
Companies should also provide training to staff on data quality management, including the importance of data quality, how to ensure data quality and the consequences of poor data quality.
At the managerial level, project managers need to develop data quality objectives and data management plans, establish quality metrics, include blueprints for data profiling at intervals, implement data quality control, and checkmate the team for compliance.
If these actions are implemented, companies can avoid the pitfalls of "putting the cart before the horse" and ensure that data quality management is a fundamental part of their health research projects. This can lead to more accurate research results, better-informed decision-making, and ultimately, improved patient outcomes.
