My last installment explained how artificial intelligence (AI) assists search, confidence scores, and data reviews. This blog dives into how artificial intelligence helps review plans, provide statistical analysis to data, and robotic process automation.
Prior to starting data review, each team has its own review plan. The data review team has a data review plan, the safety team has a safety review plan, the analysis team has a statistical analysis plan, etc. These review plans are standard across studies with few changes. An automated program can create review plans based on the metadata information in a study. The review plan will result in a list of tasks, which can be assigned to different user groups.
Based on the previously assigned tasks, the program can automatically assign the tasks and prioritize the tasks for each individual user. This, combined with the prioritized data review, will enable users to prioritize their tasks and complete the data review and the tasks from the review plans.
Statistical Analysis of Clinical Data
Statisticians analyze clinical trial data to understand the efficacy and safety of a drug. Machine learning can be used to assist statisticians in analyzing the data and looking for anomalies, such as:
- Why is it that the subjects in certain age ranges at site 1 are reporting fever in visit 3?
- Are there other sites that are reporting fever for visit 3?
- Is this related to the clinical trial, or is there an outbreak in site 1 region that is causing this anomaly?
- Can I compare this data with publicly available data to check for outbreaks?
There is enough data to train the ML algorithms to help in reviewing the data. Understanding how to use this data and how to perform more-effective exploratory analysis will help statisticians better understand the efficacy and safety of a clinical trial.
Robotic Process Automation
Validation is a manually intensive and time-consuming process. Some companies are considering using robotic process automation for validating their review platforms when new functionalities are added to it or when new versions of the software are released. This will ensure that the available functionalities in the current system are not impacted by new changes and no regression issues are introduced in new versions.
To learn more about how AI – including machine learning – can be used by pharmaceutical and medical device companies to improve the clinical data review and cleansing process, you can download the guide here or submit the form below.