Today, Data Kinetic introduces a suite of industry-focused applications, designed for integration with existing healthcare systems. These applications allow customers to run models on their preferred platforms within their environment. These multi-modal models are cold-start meaning that no training data is shared, and customers can build their own AI assets, while supporting existing workflows and practice requirements.
This approach provides healthcare providers with the capability to use private models securely within their own infrastructure, aligning with their specific operational requirements.
At Data Kinetic we believe in supporting our healthcare specialists with the tools and secure data technology to reduce burnout, support more insights, and ensure privacy for patients.
Introducing the Data Kinetic Healthcare Suite:
CMS Fraud Detection
Data Kinetic's CMS Fraud Detection system has been developed using over 480 models and 9 graph variations, creating a comprehensive statistical model with over 4,000 parameters. This model, which is deployed within the healthcare provider's infrastructure, demonstrates a high accuracy rate of 94% in detecting fraudulent behaviors. It analyzes over 400 characteristics and relationships, providing a rationale for each transaction identified as fraudulent. The impact of this system is significant, achieving more than 94% accuracy on diverse datasets.
Social Determinants of Health (SDH)
The SDH application focuses on the impact of non-medical factors like lifestyle and socioeconomic status, which account for 30-55% of health outcomes. By training machine learning models on data sets that include income, healthcare, education, and other factors, we predict vaccination rates, and other community health indicators and offer insights into the factors impacting these rates.
Hospital Patient Volume Forecasting
Using historical patient records, admission and discharge data, and real-time data streams to predict patient volumes with advanced AI. This application is vital in optimizing resource allocation, care plan adjustments, and emergency preparedness. It's designed to predict patient counts across multiple facilities and departments, necessitating the creation of 24 models for each combination.
Hospital Length of Stay Prediction and Staffing Optimization
Accurate prediction of hospital stay lengths is essential for optimizing resource utilization and reducing healthcare costs. Our solution addresses the skewed nature of hospital Length of Stay (LoS) data, focusing on scheduling challenges in hospital theaters. It integrates factors like the Glasgow Coma Scale (GCS), albumin levels, white blood cell count, mean arterial pressure, and bilirubin levels to enhance prediction accuracy. The DK App combines 17 regime factors, including correlated conditions, historical data, and CMS insights.
Automated Visual Pathology: Tumor Detection
Our approach in Automated Visual Pathology leverages advanced machine learning techniques to automate tumor growth assessment, overcoming challenges posed by the large file sizes of whole slide images.
Detecting Adverse Drug Impacts from Social Media Conversations
Given that side effects of medicines and vaccines may emerge in larger and diverse patient populations, our AI and Data Analytics application provides near real-time insights into drug safety monitoring. This involves ingesting unstructured medical text and applying NLP models to extract information about adverse drug events (ADEs).
Medical Supply Chain
Understanding the complexity of today’s global supply chains, our AI application enhances efficiency and accuracy in demand forecasting. This involves hierarchical clustering on time series correlations and employs diverse models from ML and Neural Networks for varied conditions and programs.
To learn more about Data Kinetic’s Healthcare suite, visit datakinetic.com/healthcare
