Overview
Princeton Blue’s Clinical Complaint Management solution uses Appian AI to improve complaint handling in clinical trials. Appian AI automates complaint review, validation, and categorization, while allowing for human intervention when necessary, reducing processing time and improving accuracy in managing clinical complaints.
The clinical complaint process often faces several significant hurdles:
- Time-intensive manual work: Reviewing, validating and recording incoming complaints requires substantial human effort, leading to potential delays in processing.
- Information gaps: Complaints frequently lack all necessary information when they are reported. This requires time-consuming back-and-forth communication with site personnel.
- Time Consuming Document Processing: Skilled personnel spend considerable time on reading and analyzing documents, limiting their availability for more complex problem-solving.
- Error-prone data entry: Manual data entry and information transfer between systems increase the risk of errors in complaint records.
- Scalability issues: As clinical trials grow in number of sites, patients and complexity, managing an increasing volume of complaints often becomes a bottleneck.
- Delayed response times: The manual nature of the process can result in slower response times to site personnel, potentially affecting participant satisfaction and retention.
- Limited real-time visibility: Tracking the status of complaints across multiple trials and sites can be difficult, hindering effective management and reporting. Princeton Blue’s Clinical Complaint Management solution leverages Appian AI technology to enhance the efficiency and accuracy of complaint handling in clinical trials. The system automates the initial processing of complaints by analyzing incoming emails, extracting relevant data, and categorizing issues. When information is incomplete, the AI interacts with site personnel to gather additional details, ensuring thorough documentation without manual intervention.
Key Features & Functionality