The metadata component of clinical trials is one of the most dynamic areas of the clinical records organization. It evolves based on the trial’s characteristics, which include the type and number of participants, time duration of the trial, medical issues of the study, trial details including investigators, site of the trial, patient characteristics including demographic and health history, treatment, adverse events, and laboratory information. With so many sources of data, it is often difficult for data management teams to derive a comprehensive amount or set of metadata that meets the requirements of each case. It can also be time consuming and error prone. This leads to both the absence of metadata and the invalidation of data that is unsuitable for certain purposes. Automated metadata plays a significant role in the effective running of clinical trials
How Are Clinical Trials Run?
Clinical trials have two major components. The first one concerns the dosing schedule of treatments. In most case, the schedule is predicated on the requirements of the FDA and/or the clinical trial regulatory organizations. Meta-data must be generated concerning the dosing schedule, including the number of days each drug should be given and the number of doses per day that should be administered.
Another major component of clinical trials is the investigator profile, which provides background information about the investigators who will be conducting the trial as well as the medical facilities where the trial is conducted. It is important for the data to match with the clinical trials in terms of protocol and dosing schedule and the use of pharmacists and other staff in the medical facility. There are instances, however, where the dosing schedule may need to differ from the protocol since the trial involves multiple sites. In such cases, the use of templates to generate the dosing schedule and data can be of great help. Automated metadata has been used consistently in these types of trials and tests.
Many clinical trials incorporate biochemical and quality assessment tests. These can provide valuable information about the trial participants. However, due to their sensitive nature, these tests require careful selection before including them in the database. Some tests use stringent criteria to identify suitable candidates for the clinical trial while others rely on interviews, questionnaires or feedback from the trial investigators themselves. For both types of tests, the accurate data collection is crucial to the conduct of the study.
Surveys are also necessary for the success of any clinical study. They allow researchers to determine what research question to ask, how to collect the appropriate data and whether the right questions are asked at different points during the study. The use of questionnaires can reduce the potential occurrence of omitted variable bias. Although they do not have a great impact on the treatment effects, they are still considered as important data management tools. Especially when surveys are used to obtain descriptive data on the demography, substance use and other parameters of the participants, they represent a considerable source of bias correction and statistical analysis error. Furthermore, although they may not represent a significant amount of the total bias in the sample, they represent a major source of missing data and are therefore difficult to analyse.
The most widely used and efficient way of collecting descriptive and predictive data about clinical trials is the use of meta-search engines. These powerful databases allow researchers to retrieve all the needed meta-information for a specific database in a matter of seconds. They perform both automatic and manual data extraction from clinical trials. The best part about these tools is that they guarantee that the meta-data are consistent between all the databases. They also perform a random validation checks to make sure the results presented in the meta-search results are not invalid. Clearly based on the information we have discussed automated metadata has a key role to play within the clinical trials industry.