Introducing Data Kinetic’s Healthcare Suite

Today, Data Kinetic introduces a suite of industry‑focused applications designed for seamless integration with existing healthcare systems. These applications allow customers to run models on their preferred platforms within their own environment. All models are cold‑start, meaning no training data is shared; customers build their own AI assets while maintaining current workflows and practice requirements.

This approach lets healthcare providers deploy private models securely inside their infrastructure, perfectly aligned with operational, compliance, and privacy needs. At Data Kinetic we believe in equipping healthcare specialists with the secure data technology and tools they need to reduce burnout, surface deeper insights, and keep patient data private.

Introducing the Data Kinetic Healthcare Suite

CMS Fraud Detection

Data Kinetic’s CMS Fraud Detection system combines 480+ models across nine graph variations, producing a comprehensive statistical model with 4 000+ parameters. Deployed on‑premises, it analyses more than 400 characteristics and relationships for each claim, provides an explainable rationale, and achieves ~94 % accuracy on diverse datasets.

Social Determinants of Health (SDH)

Non‑medical factors such as lifestyle and socioeconomic status account for an estimated 30 ‑ 55 % of health outcomes. Our SDH application trains ML models on data covering income, healthcare access, education, and more to predict vaccination rates and other community‑health indicators—and highlights the drivers behind them.

Hospital Patient Volume Forecasting

By combining historical admission/discharge records with real‑time data streams, this app forecasts patient volumes, optimising resource allocation, care‑plan adjustments, and emergency preparedness. It scales across multiple facilities and departments, typically creating 24 models per location/department combination.

Hospital Length‑of‑Stay Prediction & Staffing Optimisation

Accurately predicting Length of Stay (LoS) is crucial for resource utilisation and cost control. Our solution tackles skewed LoS data—inclusive of theatre‑scheduling constraints—by integrating factors like GCS, albumin, WBC, mean arterial pressure, and bilirubin. The DK app blends 17 regime factors (correlated conditions, historical trends, CMS insights, and more) to refine predictions.

Automated Visual Pathology – Tumour Detection

Leveraging advanced ML techniques, our visual‑pathology workflow automates tumour‑growth assessment on whole‑slide images, overcoming the challenges posed by multi‑gigabyte file sizes.

Detecting Adverse Drug Impacts from Social Media

Side‑effects often emerge only in large, diverse populations. Our AI analytics pipeline ingests unstructured medical text from public conversations and applies cutting‑edge NLP to surface adverse drug events in near real time.

Medical Supply Chain Optimisation

Today’s global supply chains are complex. This application performs hierarchical clustering on time‑series correlations and employs a mix of ML and neural‑network models to produce highly accurate demand forecasts under varied conditions and programmes.