It is believed that perfect clinical data is important to leverage operational analytics solutions and reports, which entails that all data collected should be accurate, complete, organized, and timely. But, it is true that data can be “perfect” and is it also true that perfect data can influence reports and integrated systems? Let us take a look.
The concerns about perfect clinical data are valid because such data ensures accuracy across systems when it is shared via integrated solutions; thus allowing you to report more confidently on your current operations. However, the sad part is that there is no such thing as perfect clinical data! This is because even though organizations strive for it, but achieving this standard of complete, accurate, organized, and timely data is very difficult at large research organizations. Things like lack of collection requirements, miscommunication, duplicate data entry, and user error can impact data quality. This makes achieving “perfect” data impossible. But, this doesn’t mean that you stop working to improve on your data collection processes. While you can achieve perfect data, you can at least come closest to it with the right tools, processes, and workflows. And for that, you need to start with evaluating your current data collection processes, clean up the existing data, and come up with more consistent organizational data practices. And, here are some tips to help you.
Evaluate your current data and data collection processes
The first most important step is to evaluate your current state of data and data collection processes. This includes what you currently collect on per study, per protocol, and per management basis; and also, what you do with the data you collect. Only after evaluating this can you introduce changes to the standard collection procedures, and address cleanup activities. Diving deep into the data you collect and understand what you do with your data will help you know the kind of data you actually need, so that you can only focus on such data collection in the future.
Identify the scope of your cleanup
If the data cleanup process seems too large and overwhelming because of the huge size of data you have, you can narrow down the scope of the project and prioritize the most critical areas that require improvement, to make it easier for you to begin. Some things that you can focus upon here are what you want to learn from your data, the accuracy and consistency of the current data you are collecting, the data you need to collect for better understanding your operations, and whether you must revise the existing collection or start with a completely new process.
Work out all the details
This step requires you to find out all the details that can help with the data cleanup process. This includes everything from the data fields that need to be completed to fill in the gaps within your data, and the relevant timeframe for data collection, to the key stakeholders who should be involved as a part of the project. These details will help build a framework that will focus on the elements to fulfill the necessary data requirements.
Once done with all of the above, you are ready to take the first step toward improving your data integrity!
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