AHA, FBI Urge Hospitals to Patch Critical Oracle Vulnerability Amid Active Exploits

October 2025 — San Diego, CA
Healthcare continues to grapple with a fundamental data problem: too little information in outpatient settings and too much in hospitals and academic centers. The result is a fragmented system that burdens both clinicians and patients with inefficiencies, delays, and incomplete records.

Predoc, a clinician-founded company, is addressing this challenge through an AI-powered platform for medical record retrieval and analysis, designed to make the right patient information available at the right time and in the right format.

According to founder Dr. Kaushal Kulkarni, the most promising use of AI in healthcare lies not in diagnostics but in automating the manual, pre-diagnostic work that consumes clinical staff time.

In one example, a value-based oncology network operating across four states used Predoc to streamline its data workflow. The results included:

• Record retrieval times reduced from weeks to hours or days.
• Chart review efforts reduced by over 75%, with customizable clinical summaries improving efficiency.
• Administrative costs lowered by more than 50%, while improving data completeness and care quality.

The broader implications are significant. The U.S. spends over $20 billion annually on health information management payroll, much of it tied to manual record handling. With AI-driven automation, tools like Predoc could help reduce this cost while alleviating clinician burnout and improving coordination of care.

Predoc’s work aligns with the Centers for Medicare & Medicaid Services’ Health Tech Ecosystem Initiative, which aims to enhance data interoperability and streamline record exchange. As AI matures, innovations in data management may become one of the most practical—and impactful—applications of the technology in healthcare.

Predoc Uses AI to Transform Health Information Management

October 2025 — San Diego, CA
Healthcare continues to grapple with a fundamental data problem: too little information in outpatient settings and too much in hospitals and academic centers. The result is a fragmented system that burdens both clinicians and patients with inefficiencies, delays, and incomplete records.

Predoc, a clinician-founded company, is addressing this challenge through an AI-powered platform for medical record retrieval and analysis, designed to make the right patient information available at the right time and in the right format.

According to founder Dr. Kaushal Kulkarni, the most promising use of AI in healthcare lies not in diagnostics but in automating the manual, pre-diagnostic work that consumes clinical staff time.

In one example, a value-based oncology network operating across four states used Predoc to streamline its data workflow. The results included:

• Record retrieval times reduced from weeks to hours or days.
• Chart review efforts reduced by over 75%, with customizable clinical summaries improving efficiency.
• Administrative costs lowered by more than 50%, while improving data completeness and care quality.

The broader implications are significant. The U.S. spends over $20 billion annually on health information management payroll, much of it tied to manual record handling. With AI-driven automation, tools like Predoc could help reduce this cost while alleviating clinician burnout and improving coordination of care.

Predoc’s work aligns with the Centers for Medicare & Medicaid Services’ Health Tech Ecosystem Initiative, which aims to enhance data interoperability and streamline record exchange. As AI matures, innovations in data management may become one of the most practical—and impactful—applications of the technology in healthcare.