The Importance of Clean Clinical Data and Why It's Never a Straightforward Process
25th October 2022 By MARS Research Hub

Introduction: What is Clinical Data?

Clinical data is a type of health data that is used to describe the care and treatment of patients. This includes information on diagnosis, procedures, lab reports, medications, allergies and more. Clinical data can be stored in various formats including:

- Electronic medical records (EMRs)

- Patient charts

- Electronic health records (EHRs)

- Laboratory information systems (LISs)

- Radiology systems

What are the Different Types of Clinical Data?

Clinical data is a very broad term that encompasses a lot of different types of data. There are many different types of clinical data and the following are just some examples.

Clinical data can be broken down into two major categories: Structured and Unstructured Data. Structured Data is any type of clinical data that has been organized in a way that makes it easily accessible by computers. Examples include lab results, medical records, and patient histories. Unstructured Data is any type of clinical data that has not been formatted to meet the needs of computers. This may include handwritten notes or audio recordings from doctor visits.

Why It's Important to Have Clean Clinical Data?

Clinical data is the lifeblood of clinical research. It is important to have clean clinical data in order to ensure that we are getting accurate results.

Clinical data needs to be clean and accurate for a study to be reliable and valid. Clinical data can become dirty if there are errors in the recording or coding process. Dirty clinical data can lead to inaccurate conclusions about a drug's safety or effectiveness.

The first step in generating clean data is making sure that all the information coming in is properly coded and recorded. The next step is making sure that it has been validated by an appropriate person before it gets entered into the database. Finally, researchers should make sure they have a system for monitoring changes made to the database over time so they can keep track of what has been done and what needs to be done next.

How to Get Rid of Dirty Clinical Data?

Clinical data are never clean. It is a messy and complicated process to manage clinical data. It is a challenge for researchers and clinicians to get their hands on clean data for research and treatment purposes. The good news is that there are clinical data management services that can help with this problem. These services use tools like R programming for clinical data management to help make the process easier. 

The first step in clinical data management is understanding the process. Clinical Data Management starts with clinicians gathering the data and then trying to figure out which data is relevant for a study or for treatment purposes. 

Clinical Data Management then moves on to organizing the data and cleaning it up from errors in transcription, duplicate entries, inconsistent formats and dates, and missing information. 

The final step is analyzing the data gained from these steps of clinical data management so that it can be used in research or treatment purposes and issuing queries in a regulated framework.