The quality of data received can vary wildly. From a CRO’s interpretation of your data to in-house CDM’s who use other information to code a term, this highly regulated industry leaves a lot of room for error. The global effort to harmonize good quality data is an ongoing and sometimes uphill battle.
Every time a piece of information is entered into a database, every time a human must interpret the information presented, the quality of that data in and its original meaning can be potentially blurred. Most data entered onto a case report form gets coded in some way, shape, or form. A few elements of good quality data include Completeness, accuracy and diagnosis. For instance, is there meaning in the terms as it stands or do we need more information to code the term? Does the term presented accurately depict the medical event being reported? Is the diagnosis supported by the appropriate investigation? Was the Adverse Event reported caused by another event or illness?
To code a term as closely to its intended meaning, we require clear data to be entered. Any erroneous or unnecessary information that may seem appropriate to append to the term will most likely, in the end, cause confusion. What is clear to an investigator may or may not be clear to us when a coding time comes. As coders, we are not allowed to utilize additional information from other sources. In other words, the term must be meaningful on its own. The terms must also be free of ambiguous information such as vague terms, variable without definition, and too many medical concepts reported as a single event. Abbreviations should mostly be spelled out or used in their interpretation. Death, hospitalization, and disability are outcomes and are not usually considered to be adverse events.
Some of the benefits of getting good quality data entered into the system are that the accuracy of the diagnosis is available for important detection and evaluation of safety signals. The timeliness of information that may affect the clinical trial is impacted by the quality of data reported. Accurate data also helps in communicating findings to regulatory agencies, patients, and medical professionals. Some ways to achieve good quality data involve quality management processes, well thought out coding conventions, and robust vetted synonym lists.
The accuracy of the initial data entered into the system will ultimately have a big impact on the quality of analysis being performed. Always be aware of the gray areas of MedDRA coding and become familiar with MedDRA and all of its idiosyncrasies. In the end, there are ways to generate good quality data in and many ways to mitigate not so great data within the coding process itself.
