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Blog   |   3.4.19

Using AI to Solve Healthcare’s Data Problem

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In our last installment, we explored the most prominent problem in healthcare today—unused and incomplete patient data leading to ineffective care, rising healthcare costs, access problems, and provider burnout. This problem is getting worse as more clinical documents are generated every year, and hiring more people to work with this data isn’t making things any better.

The only sustainable solution to healthcare’s massive data problem is technology—specifically, artificial intelligence (AI). By using intelligent algorithms to derive insights from patient charts, healthcare organizations can get the most out of their existing data and better understand where they have knowledge gaps that put patient care and their financial viability at risk.

There’s been a lot of buzz in the healthcare industry about AI. But what exactly is it? How does it apply to healthcare operations? And how does Apixio use this computational approach in its solutions for payers and providers? This article explores.

An introduction to AI

At a broad level, artificial intelligence is the simulation of human intelligence by technology. Certain forms of AI have become so prominent in everyday life that we often forget they are AI. Your email’s spam filter, personalized news feeds, and virtual assistants like Siri and Alexa are all common forms of AI technology.

AI relies on models that use intricate sequences to achieve the desired result. A few of the most popular AI models include:

  • Decision Tree: A binary tree with a “yes” or “no” decision at each split that directs the model toward the final result. Spam filtering is an example of decision tree AI, with your email platform sending each message through a series of splits to determine whether or not it belongs in your inbox.
  • Linear Regression: A technique used to predict the value of a quantitative variable. This model is commonly used in salary estimation, house price prediction, and sports statistic projection using past performance to determine likely future figures.
  • K-Nearest Neighbors (KNN): A machine learning model that classifies differing data points based on their similarities. KNN is used in credit scoring, handwriting detection, and image recognition.

The Apixio Platform: How it works

The Apixio Platform utilizes a four-step process to turn healthcare documents such as medical charts, claims, RAPS files, and lab results into insights that ultimately result in a clearer picture of patient health and better overall care. By applying a suite of AI techniques, our platform is able to transform raw, unstructured text into targeted information health plans and provider groups can use to inform critical risk adjustment, clinical documentation, quality measurement, and care management activities.

Step 1: Data Acquisition

We start by gathering a complete set of documentation for the target patient group. Everything from clinical records and images to billing claims are pulled and encrypted via our secure extraction process. These items are then transmitted to our Data Loader and imported into our cloud-based platform to be processed and analyzed. This first step is crucial in ensuring we gather all existing patient data in order to create the most complete picture of patient health.

Step 2: Data Processing

Once data has been transmitted to the Apixio Platform, it’s processed and validated to ensure the data is accurate, complete, and usable moving forward. We execute hundreds of separate validation checks against imported datasets to make sure we get this right. The Apixio Data Coordinator is capable of processing hundreds of millions of clinical notes in just hours—an incredible boost in efficiency and accuracy when compared to manual validation practices. Any image-based files such as PDFs are processed by our Optical Character Recognition (OCR) pipeline and enhanced so that even poorly scanned text is made machine-readable.

Step 3: Data Analysis

From here, files are sent into our processing pipeline. Imported data is stored in the Apixio Patient Object Model (APOM), which contains all available information about the individual’s healthcare. This can include the patient’s conditions, medications, procedures, and derived information from prior analyses.

Step 4: Data Insights

The data has been gathered, processed, and analyzed. Now it’s time to put it to work. During this step, machine learning techniques are used to extract a variety of signals and to answer specific questions about individual patients. Insights are then delivered via various applications to users to support quicker and more effective decision-making given the complete picture of a patient’s previous history and current health status. Feedback from Apixio’s users is also used to optimize our algorithms for continually greater accuracy and performance.

This four-step process solves for the primary drivers of healthcare’s data problem—lack of data access, expensive and time-consuming processing, and inaccurate information.

AI’s role in improving healthcare

Artificial intelligence is poised to play an unparalleled role in improving the world of healthcare. Converting unstructured data into actionable information, combining all relevant data into one large set that can be used to train models, and applying machine learning techniques to analyze and find trends within healthcare datasets is the key to developing a complete and accurate picture of a patient’s previous and current health. AI also gives healthcare organizations the power to:

  • Process and validate healthcare data faster and more accurately
  • Analyze data and create care summaries for individual patients
  • Evaluate and improve care quality using industry-standard measure sets
  • Create accurate and actionable insights from care summaries
  • Give medical professionals better access to more accurate information so they can spend more time with patients and drastically reduce misdiagnoses
  • Reduce healthcare spending on unnecessary or even harmful services
  • Provide a better patient experience that’s tailored to individual health needs and risk factors

Artificial intelligence is now being leveraged throughout the healthcare industry to turn unstructured data into more efficient, accurate, and quality care. Our mission is to use AI to help healthcare organizations contain costs and provide a better patient experience by powering more informed decision-making at every level. We’ve already seen AI’s impact on risk adjustment coding; today, we’re paving the way for additional AI solutions to improve quality measurement and care delivery.

Want to experience AI in action? Request a demo of one of Apixio’s AI-powered applications and start putting your data to good use.

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