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Contact Info

Email hi@mediushealth.org
Phone +123 456 7890
Office Hours Sat - Wed : 8:00 - 4:00

Unravel the mystery ofHealth Data

Unleashing the power of Medius’ multi-cloud artificial intelligence platform to deliver greater accuracy and coverage on various health intelligence and risk predictions using just a few health variables.

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Unravel the mystery of health data

The HealthcareFinancing™ landscape is rapidly changing and undergoing an important evolution. Over the next few years, artificial intelligence will be embedded to solve complex problems throughout the value chain from risk prediction to care management.

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Leverage new and unconventional data sources

Turning health data into intelligence opens up innovative solutions to unsolved problems and creates new use cases to continually evolve customer experience. The pandemic has shifted all existing statistical datasets requiring new and unconventional data sources to guide the way forward.

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Built from the ground up

Medius' Health AI Stack is built from the ground up using advanced machine learning technologies and algorithms that employ geographically specific datasets and an evolving medical knowledge graph to build a holistic picture of the population. Organisations can seamlessly plug in our pre-built AI model APIs and services in existing operational workflows or build new use-cases and applications to serve customers.

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Shape the future together

At Medius, we believe in empowering the ecosystem, becoming an AI powerhouse for some of the world’s leading life and health insurance carriers, re-insurers and health providers to create a fundamental shift in an industry that longs for more data-driven, efficient and customer-centric philosophies, but more imperatively proposing simulated executables by comprehending deep insight using expansive health care data ecosystem to ensure sustainable growth and market defensibility.

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Our Platform

Enterprise-grade Artificial Intelligence Platform for Health Providers and Payers

Using just a few health data co-ordinates, Medius’ multi-cloud artificial intelligence platform aggregates, and mines massive datasets to create all relevant health risk and underwriting insights within seconds, with accuracy and coverage that can help providers and payers with their health and life needs assessments, distribution, operations efficiency, and cost optimization needs.

UW Studio™

Medius’ proprietary UW Studio™ is a complete underwriting platform delivering the AI revolution that has fundamentally altered the insurance landscape whereby multiple science-based underwriting ensemble models are deployed to address the business and technical requirements by reproducing humanistic deductive actuarial reasoning with clinical accuracy. Transforming heuristic underwriting rules would unravel new levels of sophisticated analytics and machine learning based decision support.

Embedded Digital Onboarding

Medius’ platform eases the customer pain points associated with buying and selling of Health insurance, all our designs are channelled by the customer’s needs. Our white-label ready solutions are designed to create a better customer experience. Medius’ adaptable technology solution allows for faster response and adjustments to continuously changing customer and business needs Embedding new insurance lines or streamlining you existing insurance offering, Medius’ architecture can help launch to market fast.

Embedded Health Intelligence

There are billions of mobile phone users, as long as we put healthcare people need on their devices. We can make day-to-day healthcare highly accessible and promote continuity of care. Utilisation of advanced technology, can alleviate the constrained health workforce, unequal distribution of health care resources which leads to overburdened institutions, and eventually increase quality of care. Need for unwanted treatments can often be avoided through monitoring and early intervention.

AI Studio™

Get started with out-of-the-box pre-built models that come pre-trained and ready for use. Capturing the most common predictive use-cases, these pre-built models help generate AI based insights for use in key workflows immediately. The Medius AI Studio enables key business stakeholders such as analytics professionals to test the pre-built models in a controlled sandbox with minimum effort, and experiment with various data combinations. All pre-built models are served through standard APIs in production environments.

44000

Hours of Clinical Validation

1200000

Patient Records

50000

Medical Journals

Peer Reviewed Research

Predicting missing and noisy links via neighbourhood preserving graph embeddings in a clinical knowlegebase (19th IEEE ICMLA 2020)

A Dictionary-based Oversampling Approach to Clinical Document Classification on Small and Imbalanced Dataset (19th IEEE/ACM WI-IAT 2020)

Continuous Improvement of Medical Diagnostic Systems with Large Scale Patient Vignette Simulation (29th ACM CIKM 2020)

Understanding patient complaint characteristics using contextual clinical BERT embeddings (42nd IEEE EMBC 2020)

All Papers

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Translating AI to ROI

How it works

Initial Discovery

Medius Health will explore various use cases with numerous stakeholders to create a comprehensive AI Strategy to operationalise AI solutions across client value chain.

Problem Framing

Finalise the use case, develop the problem hypothesis, and design the solution approach with an agile delivery mindset

Data Exploration

Assess data quality and perform data engineering operations such as analysis of data distributions, balancing, missing value imputation, outlier handling and more, to prepare datasets prior to modelling.

Model Development, and Pre-scale Validation

Either calibrate Medius’s pre-built AI models using client data for customization or develop new models while incorporating the domain knowledge and client guidelines, and assess the efficacy of the model as a pre-scale phase.

Achieve Enterprise Scale

Productionise the proven AI models using APIs across pre-determined products, and channels

Evaluate and Iterate

Constantly evaluate and improve models using feedback using MLOps configuration to track performance in the long-term.