The startup develops machine learning tools that utilize anonymized patient data to predict the risk of post-operative complications. This software enhances clinical decision-making and optimizes pre-operative pathways for elective surgeries, improving patient care outcomes in healthcare settings.
Funding
$780K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Hospitals face challenges in efficiently managing pre-operative pathways for elective surgeries, leading to delays, cancellations, and suboptimal patient outcomes. Accurately assessing a patient's risk of post-operative complications is difficult, hindering effective resource allocation and informed clinical decision-making.
Solution
OPCI's OpenPredictor is a clinical decision support tool that leverages machine learning to predict a patient's risk of post-operative complications. By analyzing anonymized patient data, the software generates a risk level that aids medical professionals in optimizing pre-operative pathways. OpenPredictor integrates into existing hospital systems, providing clinicians with patient-specific risk reports to support informed decision-making. The platform streamlines waiting list management, enabling efficient patient allocation and reducing unnecessary delays. The AI is developed responsibly, adhering to UK Government AI regulatory principles, and includes explainability tools to ensure transparency.
Target Audience
The primary target audience includes healthcare providers, hospitals, and pre-operative assessment teams seeking to optimize pre-operative pathways, reduce surgical backlogs, and improve patient outcomes.
Features
- Machine learning model trained on anonymized patient data to predict post-operative complications.
- Integration with hospital systems for seamless access to patient-specific risk reports.
- Waiting list management tools for optimal patient allocation and resource utilization.
- Explainability tools to trace and explain the outcomes generated by the model.
- Developed in alignment with the UK Government’s AI Regulatory Principles.
- User interface designed for ease of use, aligned with the NHS Digital Service Manual.
- Utilizes Azure Machine Learning Studio (AML) for model training.
- Based on a polynomial logistic regression approach.