A recent survey from the National Association of Healthcare Revenue Integrity (NAHRI) showed that charge capture is the number one supporting function for healthcare revenue integrity programs.1 Yet, many providers continue to fall short in fully documenting and coding the clinical encounter, leading to missed revenue and potential compliance issues. Given the repetitive nature of charge capture processes, there are significant opportunities to apply advanced technologies like artificial intelligence (AI) to increase the function’s accuracy and reliability.
Recently, Change Healthcare’s Director of AI, Alex Ermolaev, spoke with Jason Williams, vice president for Product Management at Change Healthcare, about the complex issue of charge capture and how AI-enabled applications, including the company’s new Charge Capture Advisor solution, could hold the key to transformative improvement. Below is a snapshot of their conversation.
ALEX: What are some of the challenges involved in charge capture?
JASON: Charge capture is the documentation and coding process that runs parallel to patient care delivery in which healthcare providers and staff aim to accurately and completely describe the medical services provided. The resulting coding data are then translated by the provider’s chargemaster rates to create claims to bill payers and patients for services. While organizations are beginning to use technology to streamline the process, there is still a high potential for human error. Given the frenetic pace of healthcare delivery, it is not surprising providers can inadvertently miss documentation opportunities or make mistakes. Coding efforts present another chance for error as coding staff often are processing a high volume of care episodes within constrained timeframes.
Alex: How do missed or incorrect charges affect healthcare organizations?
Jason: Suboptimal charge capture is a high-impact issue for healthcare organizations. What if missing charges and associated reimbursement, combined with audit and recovery efforts, cost providers the equivalent of 1% of annual revenue? This would seem like a small percentage, but when applied across a multi-million dollar organization, it’s a significant number that’s hard to ignore. With the margin pressures that most providers are under, poor charge capture puts additional stress on operating sustainability. Moreover, there are regulatory risks if coding compliance isn’t what it should be.
Alex: How can artificial intelligence be used to improve the process?
Jason: Artificial intelligence, or AI for short, uses computers to do work that usually requires human intelligence, thinking, or cognition. When these machines are trained to handle certain tasks, they can be done as well or better than if humans performed them. So, for example, we can “train” computers to predict missing charges and make recommendations, helping organizations improve the accuracy and comprehensiveness of their claims, which translates into substantial revenue benefits.
Alex: How does Change Healthcare approach AI differently from other companies?
Jason: At Change Healthcare, we focus on the fundamentals that make AI more effective. Lots of companies are creating AI models, but their recall, precision, and performance are not all the same.
Effective AI requires three things: rich data from which machines can learn; informed experts who can design the ideal learning models; and optimal delivery systems that present meaningful information at the point of decision-making.
First, let’s look at the data piece. AI learns what you teach it, and it requires a lot of information to learn. Although a toddler can learn to recognize pets after being exposed to a few of them, AI may need to see thousands of images before it does as well as the toddler. Since Change Healthcare owns one of the most prominent data repositories in healthcare, housing information from most hospitals, physicians, payers, and other stakeholders, our AI solutions have access to a broader range of data and thus can learn more effectively.
Next, consider access to data scientists—the experts who know the best ways to train AI solutions from the real-life experiences the data represent. Change Healthcare has invested in an in-house team of data scientists who are skilled in machine learning, neural networks, reinforcement learning, and a handful of other technical concepts that help them choose the best methods for building high-performance AI models.
Finally, let’s explore delivery strategies. Predictions are only as good as their availability at the point of decision-making. Change Healthcare uses a design thinking discipline to determine how to get the AI results to the user, so he or she can act as close to real time as possible. This is not dissimilar to the way you receive information via your smartphone at a time and/or location that you can act on it.
Alex: How is Change Healthcare using AI to improve charge capture performance?
Jason: Change Healthcare is introducing a solution called Charge Capture Advisor, which is a cloud-enabled offering that uses Change Healthcare Artificial Intelligence to identify potentially missing charges in patient accounts prior to claims submission. Working with providers’ existing health information systems, the technology flags missed charges alongside providers’ existing coding and claim workflows. Charge prediction results are delivered in real time, at the point of decision-making, so the user can act quickly to resolve discrepancies, and ensure a more complete claim prior to submission.
Commonly predicted charges include missed or incorrect diagnostics, images or tests, or implants or supply codes. Overlooked or deficient documentation for drugs and injection administration can also be flagged. To identify these missed items, the AI has been “taught” to recognize when claim information doesn’t look like it normally would, based on the real-world experiences it’s been exposed to in the large data set.
Alex: How does using AI to enhance charge capture compare to other methods for solving the same problem? What has changed?
Jason: AI uses a statistical approach to make predictions as opposed to rules-based methods, which have been used to aid in charge capture for some time. Rules-based systems report potential missing charges based on flags triggered by certain conditions. Unfortunately, the rules or flags are often either too aggressive or too conservative, with aggressive rules flagging too many issues and conservative ones failing to detect critical missteps. Also, rules-based systems are time consuming and resource intensive to maintain due to constant chargemaster and clinical process updates, which require someone to adjust the relevant rule. Since an AI solution automatically learns over time based on charge predictions that are accepted or rejected, it will start to recognize updated patterns as well as any outliers as new data emerges—limiting the need for human intervention.
Alex: How can people learn more about Charge Capture Advisor?
Jason: For more information about how Change Healthcare is using AI to transform charge capture processes, go to the Charge Capture Advisor resource page.
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