Automation Potential in: Clinical Data Management
Automation Impact Evaluation
Poor quality clinical data can be one of the most costly aspects in any clinical trial. Not only does the review and clean-up slow down time to market and increase development costs, but “dirty” data can stall or even kill a new drug application (NDA).
Consistently receiving poor quality data from sites can seriously damage a sponsor’s or CRO’s relationship with a site/ investigator, and can lead to a site no longer working with a sponsor or CRO, even though that site might have the ideal patient population, location, equipment, and/or expertise that the sponsor or CRO needs.
The sponsors of a clinical trial record all the activities. It includes multiple sites in a master data repository, which is known as the trial master file (TMF). The document and data are still entered manually in the TMF structures. Sponsors that have multiple CROs have to facilitate multiple TMFs, which are not integrated properly. This limits the insight drawn from a TMF.
On top of that, extra resources must be trained and allotted to maintain and verify each system. RPA would reduce these efforts by automatically uploading the data and documents into the TMF. This could reduce 90% of the time spent on data entry, thus making significant savings per clinical trial a year.
RPA is able to collate information from disparate systems and combine and aggregate data. This enables the seamless consistency of records and may be especially useful if there is a change of operating system or CRM. By eliminating the possibility of human error through automation, every task is accounted for and becomes traceable. This is very useful for Clinical Data Management cases. We have already seen banking, financial services and insurance sectors currently employ this technology to administer data-intensive work.