Selection of patients for clinical trials: an interactive web-based system

Area of Science:

• Medical Informatics
• Clinical Trial Management
• Artificial Intelligence in Healthcare

Background:

• Clinical trial patient recruitment is crucial for evaluating new treatments.
• Traditional patient selection relies on manual analysis of medical records, which is time-consuming and prone to errors.
• Efficient patient selection is essential for the timely completion of clinical trials.

Purpose of the Study:

• To develop and evaluate an expert system designed to automate and improve the process of selecting participants for clinical trials.
• To enhance the efficiency of patient recruitment by leveraging artificial intelligence.
• To reduce the overall cost associated with patient selection by optimizing medical test ordering.

Main Methods:

• Development of an expert system incorporating patient data analysis and clinical trial criteria.
• Implementation of a module for suggesting and ordering necessary medical tests when initial data is insufficient.
• Design of a user-friendly interface for medical researchers to manage clinical trials and selection criteria.

Main Results:

• The expert system demonstrated an increase in the number of eligible patients identified for clinical trials.
• The system's test ordering functionality was shown to reduce the total cost of necessary medical examinations.
• A novel interface allows researchers to add new trials and criteria in 10-20 minutes, with novice users mastering it within an hour.

Conclusions:

• The developed expert system significantly improves the efficiency and effectiveness of patient selection for clinical trials.
• The system offers a cost-effective solution by optimizing medical testing procedures.
• The intuitive interface empowers medical researchers to manage trial recruitment independently, reducing reliance on technical support.