Healthcare Products

Machinify's rapidly expanding portfolio of AI apps help plans, payers and providers take their business operations to new heights of execution and performance excellence. Built atop the powerful Machinify platform, the applications bring a modern, AI-first perspective to bear on optimizing decisions and increasing productivity in a variety of healthcare administrative functions.

AI Apps

AI Apps are applications built on top of membership, claims, contracts and medical-records data, to enable optimized decision-making for business processes revolving around these data sets in a continuous, real-time fashion. The Apps portfolio includes PAtriot for automated PA, COBalt for COB, Sentinels for payment integrity, NORAD for intelligent claims management, Perfector for high accuracy code edits, Tailor for automated claims repricing, GoldenEye for medical record and provider contract NLP, and Machinify Universe for single source of truth.

AI Server & Runtime

The AI Server & Runtime is responsible for providing various forms of real-time decision-support around claims, contracts and clinical data. From tight integration into claim adjudication system for pre-pay decisioning, to supporting client-facing applications with real-time data access needs, the runtime provides a unified path for accessing relevant data and decisions in a service-oriented fashion.

Data Repository & AI Cloud

The Data Repository & AI Cloud provides a cloud-native ingestion and storage system bringing together both structured and unstructured data using a modern cluster-computing architecture. Examples of data being brought together include semi-structured claims information, business process state from transactional databases, historical data in lakes and warehouses, as well as images and documents representing clinical records and contracts.

Data Abstraction & Access

The Data Abstraction & Access layer transforms raw data into the useful shapes necessary to optimize key decisions. Examples of such transformation includes the extraction of key terms from contracts, automated structure extraction from clinical records, as well as the application of machine learning models to claims to assess the likelihood of various forms of errors and overbilling.