How Artificial Intelligence can make U.S. Healthcare more efficient

This content is sponsored by Change Healthcare.

For the past several decades, patients, providers, and payers have lamented the health of the U.S. healthcare system.

Just this past fall, JAMA: The Journal of the American Medical Association, estimated that close to 30% of healthcare spending in the U.S. “may be considered waste.”¹

Over these same several decades, an important technology -- artificial intelligence -- also has grown in importance and impact. AI pervades our daily lives. It powers your Amazon shopping cart, the Alexa device in your living room, and suggests personalized movies and music on Netflix and Spotify.

AI, and its subset, machine learning, is imbued in our daily lives.So why not also in healthcare?

HEALTHCARE IS A NATURAL HOME FOR ARTIFICIAL INTELLIGENCE 

AI is the ability for a computer system to perform a task that an intelligent human could do. But AI needs massive amounts of data to work well. That’s something the healthcare industry possesses in spades.But you can’t just throw AI at a healthcare problem. You need to identify specific challenges, and demonstrate how data, interpreted by AI platforms and its subset, machine learning, can create value for patients, providers, and payers.

Whether or not an AI solution provides value depends upon the answers to these questions:

1. Is it feasible to use AI?
2. Is there enough volume of data to train a model?
3. Is the data of sufficient quality?
4. Can AI make the process run more quickly?
5. Can AI do so at a far lower cost?
6. And does the resultant model, once incorporated into an organization’s workflow, truly affect positive change?

Here arefour practical examples of AI products we’ve developed at Change Healthcarewhere the answer is “yes” to all six questions. These examples use our Claims Lifecycle Artificial Intelligence, a new capability being integrated into our Intelligent Healthcare Network™ and financial solutions, to help providers and payers optimize the claims processing lifecycle. 

1. Dual Eligibility. Change Healthcare rolled out Dual Enrollment Advocate™, which more effectively identifies and engages members who are eligible for both Medicare and Medicaid. Finding that population lowers cost and improves revenue for payers. 
   
   
2. Charge Capture.Our new Charge Capture Advisorcan help many of the hundreds of thousands of providers we work with in the U.S. use AI to detect missing charges before claims are filed with payers. That helps providers improve revenue and cash flow.
    
   
3. Imaging Order Streamlining. We’ve announced an AI upgrade to our CareSelectTMImagingsolution, helping physicians streamline imaging orders and comply with new and existing regulations. 
   
   
4. Denial Management. We introducedAssurance Reimbursement Management™ Denial Propensity Scoring and Revenue Performance Advisor Denial Prevention, which use Claims Lifecycle AI to enable providers to proactively identify problem claims that could result in denials, and remediate potential issues before the claims are filed.

You need each of the above six conditions to be present to change the cost-quality curve of a healthcare challenge. Just think about a single claim payment. What’s the role of data as you make a decision about a payment? How many different permutations of a decision can there be? For any one patient, how many diagnosis or procedure codes apply? The questions go on and on.

NOW IS THE RIGHT TIME FOR AI TO BE APPLIED TO HEALTHCARE 

But why can AI—finally—have such a positive impact on the U.S. healthcare system? There are several interrelated trends that have come together in the past few years. 

1. Digitization. Data now is digitized with regard to claims eligibility, transactions, and electronic health records. 
   
   
2. Cloud. Cloud computing platforms allow our industry to build AI systems in a secure, compliant, elastic, scalable, and economical way. 
   
   
3. Open Source and commercial AI/ML frameworks. The availability of commercial and open source frameworks enables organizations to build solutions more economically. 
   
   
4. Training. There are now sufficient numbers of data scientists and machine learning engineers coming into the labor force who can build these models. 
   

The confluence of these trends is what’s modernized so many industries: Netflix formovies, Facebook and Google for ads, and Tesla for autonomous cars.It’s high time for healthcare to reap the benefits of the confluence of these same forces. 

WHY WE ARE INVESTING IN ARTIFICIAL INTELLIGENCE 

Change Healthcare processes more than $1 trillion dollars worth of healthcare claims, based on some 14 billion transactions annually. When you have that much data, you’re embedded in the workflow of providers and payers. You have an opportunity to create value for customers by helping them deploy AI.

All players in the healthcare sector are looking for ways to reduce waste, improve efficiency, and deliver improved value to consumers.Change Healthcare does this by enhancing our own network, software and analytics,and services solutions with AI, leveraging the breadth and depth of the data that we process.

You can think of AI and machine learning as helping create solutions to common healthcare challenges. Think of the AI examples mentioned earlier: dual eligibility, charge capture, streamlining imaging orders, and denial management. These solutions make use of multiple streams of information.

The goal is this: to help Change Healthcare customers make the right payment for the right procedure at the right time. AI models make decisions faster, better, and cheaper than before.

Artificial intelligence will make healthcare more affordable, more accessible, and less characterized by friction. AI and machine learning will build value for every healthcare participant: payer, provider, and patient.

1http://bit.ly/2NjTZxu