Clinical trials serve as the backbone for driving medical advancements and breakthrough treatments. However, they come with significant challenges.
Recruiting and retaining patients for trials is difficult because it requires specific inclusion and exclusion criteria that have to be analyzed across various data sets. Elastic and generative AI can help early in the trial process by analyzing patient information, such as eligibility criteria, demographic information, and medical history, to identify eligible participants more efficiently than traditional recruitment practices.
The Elasticsearch Platform is well suited for clinical trials because it can use generative AI to rapidly analyze and interpret data patterns and trends on trial progress, patient responses, and any adverse issues in real time.
For example, if a patient’s health metrics deviate from the norm, Elasticsearch can trigger an alert to notify trial administrators, who can immediately intervene if necessary. The models displayed in Kibana can estimate patients’ responses to treatment, adverse effects, and the likelihood of success.
By effectively forecasting metrics like patient enrollment or potential bottlenecks, administrators can optimize trial resources and ensure that trials are completed successfully.