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.