In a strategic initiative, a prominent hospital chain in the United States embarked on a transformative journey by transitioning from manual quality assurance (QA) processes to advanced AI-driven automation.
The goal was to streamline radiology reporting, improve patient care coordination, and harness valuable insights through trend analytics. Factspan played a pivotal role in this endeavor, employing cutting-edge technologies to automate report analysis, capture critical patient data more frequently, and establish structured incidental reporting based on the American College of Radiology Keywords (ACR).
The project integrated Language Learning Models (LLM), Azure AI, Snowflake, SQL, Python, and Tableau.
- Instant Access: GenAI-powered automated reporting ensured that patient reports became instantly accessible, eliminating delays in retrieving crucial information.
- Enhanced Efficiency: The project significantly reduced the risk of missing important patient data and observations, enabling healthcare providers to address patients’ needs immediately and effectively.
- Cost Savings: Improved efficiency is estimated to save approximately $1.2 million to $2.4 million annually for processing a million reports.
- Holistic Insights: Beyond radiology, insights from incidental findings and trend analyses benefited various healthcare branches. This enriched the understanding of healthcare trends and facilitated prompt coordination with the pharmacy team for enhanced patient care.
This transformation underscores the fusion of technology and strategy to revolutionize healthcare operations, ultimately delivering better patient care and cost-efficiency.