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Transforming data into usable insights with healthcare AI

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 data with healthcare AI technology.

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 capture, care quality, 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, 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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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
Testimonial
hill icon

In the past we relied on manual review of our charts. This meant lost manpower and physician time in tedious processes.

Implementing HCC Identifier has allowed us to mine EHR and scanned chart data for valid, risk adjusting conditions with improved transparency and efficiency. 

Britt Moroles
Senior Manager, Government Programs
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
Case Study

Apixio & Magna Health Plan

Fast Facts 40,633 lives reviewed 2,755 HCC deletes found 95.3% agreement with…

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Case Study

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

Fun Stats 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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