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Transforming data into usable insights with a healthcare AI platform

Healthcare doesn’t effectively use the data gathered. More than 1.2 billion clinical documents are created each year, only to sit in storage without being utilized to improve care. Make use of your risk adjustment and quality measurement data with a healthcare AI technology platform.

The Apixio Platform

Every year, 1.2 billion clinical documents are generated in the U.S., but most of this data goes unused in the provision of healthcare. Without the right technology to transform PDF and image documents into machine-readable text, and well-trained models to mine these documents for relevant patient and clinical insights, organizations aren’t making the most of their data.

At Apixio, we recognized an opportunity for artificial intelligence to unlock unstructured text and help plans and providers use the data they already have to improve risk adjustment, quality measurement, and clinical documentation. The Apixio Platform is the foundational technology suite that allows us to surface targeted information from healthcare documents to inform operations and care delivery.

Step 1: Data Acquisition by InfoStream

We use a secure extraction process to pull and encrypt PDF, image, and EHR documents such as patient charts, medical and pharmacy claims, eligibility data, and more. These documents are then securely transmitted to our Data Loader for processing and analysis.

Step 2: Data Processing

Our ETL workflow loads, validates, and processes documents so they can be run through our proprietary machine learning models. During this stage, we use our optical character recognition (OCR) pipeline to translate the data in image files into machine-readable text.

Step 3: Data Analysis

Processed data is stored in individual Apixio Patient Object Models (APOMs), which include patient diseases, medications, procedures, biometric values, and derived information from prior analyses. We then apply proprietary data classifiers and predictive models to generate insights.

Step 4: Results Sorting

Insights from our machine learning models are sorted and sent through configurable workflows to our applications. Results are tuned per client, per project to ensure maximum relevancy.

Step 5: Application Review

Results are served up for review in our risk adjustment, quality measurement, and prospective solutions. Feedback from expert users is fed back into our machine learning algorithms to continually refine our approach and improve results.

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Our flexible data pipeline can ingest a variety of different file types, including PDFs, TIFs, JPGs, and EHR extracts.
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Our OCR pipeline has ingested millions of PDFs and images, learning how to efficiently process documents and make computational adjustments to optimize digital text outputs.
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Text classifiers:
Data is run through our machine learning models, called classifiers, to extract relevant information. We use ensembles to combine related pieces of information into usable insights.
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Training data:
We use training data to teach our models what to look for when analyzing patient documents.
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Results API:
Patient events are bundled into packages to be routed to our applications.
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Patient events:
Our models generate a set of patient events tailored to the specific solution and client needs.
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The same set of patient documents can generate insights routed to different applications to inform specific tasks.
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Apixio’s Quality Identifier is an AI-powered quality measure abstraction solution.
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Apixio’s HCC Identifier is an AI-powered coding & QA solution for MA and Commercial risk programs.
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Apixio’s HCC Auditor is an AI-powered audit & compliance solution for RAPS submissions.
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Apixio’s Prospective Insights is an AI-powered condition suspecting solution.
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Apixio Patents

At Apixio, we’ve been developing innovative approaches to addressing some of healthcare’s most challenging data problems since 2009. Our team has been issued a number of patents for the proprietary AI techniques and workflows used to deliver our industry-leading healthcare analytics solutions. These patents fall into three primary categories:

  • Medical Information Navigation: Systems that collect, clean, and normalize data from a variety of sources and store them in an aggregated clinical patient phenotype. These systems automatically map patient data into medical ontologies, index and rank the data, and make it accessible in search and workflow applications.
  • Medical Coding Systems: Risk adjustment and care quality applications that use data to optimize coding workflows, monitor coder performance, and detect overcoding. Additionally, these systems also support the creation of a coder marketplace.
  • Patient Data Utilization in Clinical Setting: Maintenance of longitudinal patient phenotypes and utilization of these phenotypes for clinical applications.

Issued Patents

Patent TitleU.S. Patent Number
Medical Information Analysis with De-Identification and Re-Identification8,898,798
Event Stream Platforms Which Enable Applications9,032,513; 9,639,662
Intent-Based Clustering of Medical Information9,043,901
Optimizing and Routing Health Information9,703,927
Medical Referral Analytics10,061,894
Medical Information Navigation Engine (MINE)10,176,541; 10,319,467
Mining Aggregated Clinical Documentation Using Concept Associations10,453,574
Efficient Handling of Medical Documentation10,482,999
Customized Annotation of Medical Information10,580,520
Sorting Findings to Medical Coders10,600,504
Determination of Patient True State for Personalized Medicine10,629,303
Determination of Patient True State for Risk Management10,614,915
Coding Health Records Using Weighted Belief Networks10,614,913
Testimonial
Capital Blue Cross Health Plan

The efficiencies Apixio brings to our reviews are just incredible. Their machine learning technology leads you to the right information you need for coding.

Andrew Bloschichak MD, MBA
Senior Medical Director, Government Programs - Capital Blue Cross
Testimonial
network health

The coding team was wonderful to work with, and incorporated our specific coding guidelines when working on our project. When they noticed a trend in what we were rejecting, they shared a Coding Clinic we were unaware of for clarification on how to code the scenarios.

Josh Weisbrod
VP of Risk Adjustment
Testimonial
Scripps logo

By using Apixio, we’re improving our auditing bandwidth and enhancing the ability of our coders to focus on other chart audits and other projects that we couldn’t do before.

JoAnn Hayden
Scripps Health Plan Services
Case Study

Capital Blue Cross

Fast Facts: Second-Pass Coding 33,500 charts reviewed +5% RAF score increase between…

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Case Study

How a Veteran Coder Used Apixio’s HCC Identifier to Eliminate Data Entry Error and Double Productivity

Fast Facts Increased productivity from 3 charts per hour to 10 charts…

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Case Study

Sigma Health Plan

Fast Facts 70,000 lives reviewed 983 new HCC codes 98.5% coding accuracy…

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