Aiomic is developing Aiomic360, an AI-driven platform for abstracting, tracking, and analyzing postoperative complications to enhance surgical quality. The platform provides real-time risk assessments and personalized treatment recommendations, addressing the significant issue of preventable complications affecting over 15% of surgical patients.
Funding
Funding not disclosed
Founders
Product
Problem
Postoperative complications, including infections, blood clots, and organ failure, affect a significant percentage of surgical patients worldwide, leading to increased mortality and healthcare costs. Current methods for tracking and analyzing these complications are often inefficient, hindering efforts to identify root causes and implement preventative measures. A lack of real-time risk assessment tools limits the ability to personalize treatment and proactively manage patient care.
Solution
Aiomic is developing Aiomic360, an AI-driven software platform designed to abstract, track, analyze, and predict postoperative complications, enabling enhanced surgical quality and patient outcomes. The platform leverages AI to streamline data abstraction from medical records, providing a comprehensive overview of key performance indicators (KPIs) related to postoperative care. By identifying root causes at the institutional level, Aiomic360 facilitates data-driven improvements in surgical protocols and resource allocation. Real-time risk prediction models enable personalized treatment strategies, allowing clinicians to proactively address potential complications and improve patient safety.
Target Audience
The primary target audience includes hospitals, surgical centers, and healthcare providers seeking to reduce postoperative complications, improve patient outcomes, and optimize surgical quality.
Features
- AI-driven data abstraction tool for efficient processing of medical records
- Comprehensive dashboard displaying key performance indicators (KPIs) related to postoperative complications
- Advanced analytics to identify root causes and contributing factors at the institutional level
- Real-time risk prediction models for personalized treatment recommendations