How AI Automates Incident Reporting & Prevents Patient Harm in Hospitals: Featuring Case Study

AI can streamline the reporting process by:

• Automating data collection: AI-powered systems can extract relevant information from electronic health records (EHRs), medical devices, and other sources.
• Identifying patterns and anomalies: AI algorithms can analyze vast amounts of data to detect trends, outliers, and potential safety risks that might be missed by human reviewers.
• Providing real-time alerts: When AI identifies a potential safety issue, it can immediately alert relevant healthcare professionals, allowing for prompt intervention.

The hospital’s clinicians were spending thousands of hours manually extracting data from unstructured medical records for mandatory reporting and quality improvement.

Each patient’s case was buried in pages of clinical notes, making it difficult to identify key risk factors, harm events, and process failures in real-time.

This manual process was not only time-consuming but also costly—one hospital could spend up to $5 million per year on extracting and analyzing data.

Even with this investment, reports often arrived weeks after patient discharge, making it difficult to take timely action to prevent harm.

We developed an AI solution that automates the entire process of data extraction and healthcare incident reporting.

The AI pulls relevant facts from unstructured medical records in real-time, generating verifiable quality metrics and identifying potential patient harm events before they become critical.

Key Benefits

• Automated Reporting: By using AI to extract data automatically, the hospital saved thousands of clinical hours. The system provided instant, accurate reports for clinical registries and value-based reimbursement contracts.
• Preventing Harm: The AI helped identify and highlight process failures that contributed to patient harm, such as sepsis, hospital-acquired infections, and pressure ulcers. This allowed the hospital to take immediate action, improving patient safety and outcomes.
• Real-Time Quality Improvement: Instead of waiting weeks for post-discharge data, the hospital could measure the impact of its quality improvement projects in real-time. This enabled them to make informed decisions and course-correct faster.

By automating their incident reporting processes with our AI solution, the hospital not only saved millions in labor costs but also significantly reduced avoidable patient harm.

Clinicians could focus on providing better patient care instead of spending hours on manual reporting, and the hospital was able to take proactive measures to improve patient safety.