What AI Can and Can’t Yet Do in Payment Integrity

It’s important to be realistic about what AI can and can’t do currently.

Artificial intelligence (AI) has been a buzzword in payment integrity for years. Companies are increasingly using AI and data mining to identify healthcare savings. This technology can be helpful in the right scenarios.

Data versus Nuance

AI, which includes technologies like machine learning, works well in certain settings involving relatively straightforward claims data. It is good at analyzing data, recognizing patterns and outliers, compiling statistics, and making calculations in scenarios where the appropriate code assignment is obvious and there are no gray areas. But not every scenario is strictly data-driven. Some situations need to be evaluated and interpreted, and that means they require human experience and intelligence.

When a patient’s chart is reviewed, clinical decisions governing diagnosis and treatment are made by a physician. This includes the creation of notes verifying what actually happened and precisely for what the payor is paying. Physicians are required to understand and document the situation, defining the evaluation methods and processes utilized to care for the patient and determine the level of care provided, patient risk categories and circumstances, etc. Artificial intelligence is not always capable of picking up on the nuances required for this. Medical records still need to be reviewed by humans to determine things that data alone may not be able to answer.

AI cannot use human experience to make decisions. It is not well-suited for situations that require logical reasoning or interpretation of notes.

A Matter of Intent

Accurate coding should recreate a physician’s thought process. Machines may be able to identify and analyze codes and keywords, but they cannot interpret intent. Take, for example, the selection of a principal diagnosis on an inpatient claim, through which the coder is supposed to identify the condition that was found, after study, to have occasioned the admission. This requires some interpretation of what’s going on in the patient’s stay that may not be accurately captured in the kind of data points that machine learning relies on – in other words, discrete pieces of data, such as specific codes or keywords.

AI can collect the data, but it takes a human reader capable of asking questions to put that data together into a story that accurately reflects the physician’s intent and the patient’s experience. This may involve discussion with a physician to find out whether certain factors were ruled in or ruled out during the encounter. If a particular keyword shows up in a record, does it mean that factor was ruled out? AI may not be able to tell.

Gray Areas

Relying on AI for broad-stroke information can also be problematic. For example, say you have a particular facility whose AI-reviewed data is showing a high rate of codes for certain complications. At first pass, this facility looks like an outlier, and could potentially be a problem. When a human reviews the data, it turns out that the facility is a trauma center. So, the codes were appropriate, but the AI mistakenly flagged it as an anomaly.

These problems are a measure of the complexity of the healthcare field. Coding has so many variables that it sometimes requires a bit of human ingenuity to figure out what is really going on for any given case. This is especially true in situations where there is a gray area around the coding process, such as in cases of sepsis, where the criteria are not always simple and clear-cut. Even in the simplest situations, coding and reimbursement are extremely complex, and technology alone cannot guarantee improvements in payment integrity.

One sure way in which AI can benefit a payment integrity program, however, is by tracking and flagging more common errors. This in turn frees up resources to allow your team to focus on the more complex situations that AI may not be as good at handling.

Until AI starts to look more human-like – think Data from Star Trek, as opposed to Alexa – the best payment integrity work will come from a combination of humans and machines. When experienced coders are equipped with the best tools, you get the best of both worlds.

Print Friendly, PDF & Email
Facebook
Twitter
LinkedIn

Laura Collier

Laura Collier is president of Penstock, a payment integrity and reimbursement consulting company. Penstock is an affiliate of Goodroot, a community of companies reinventing healthcare one system at a time. Goodroot is committed to eliminating medical debt, lowering healthcare costs, and increasing access to quality care.

Related Stories

Confusion Reigns over Application of G2211

Confusion Reigns over Application of G2211

Although the effective date for billing Office and Outpatient (O/O) Evaluation and Management (E&M ) Visit Complexity Add-on Code G2211 was Jan. 1, the Centers

Read More

Leave a Reply

Please log in to your account to comment on this article.

Featured Webcasts

Leveraging the CERT: A New Coding and Billing Risk Assessment Plan

Leveraging the CERT: A New Coding and Billing Risk Assessment Plan

Frank Cohen shows you how to leverage the Comprehensive Error Rate Testing Program (CERT) to create your own internal coding and billing risk assessment plan, including granular identification of risk areas and prioritizing audit tasks and functions resulting in decreased claim submission errors, reduced risk of audit-related damages, and a smoother, more efficient reimbursement process from Medicare.

April 9, 2024
2024 Observation Services Billing: How to Get It Right

2024 Observation Services Billing: How to Get It Right

Dr. Ronald Hirsch presents an essential “A to Z” review of Observation, including proper use for Medicare, Medicare Advantage, and commercial payers. He addresses the correct use of Observation in medical patients and surgical patients, and how to deal with the billing of unnecessary Observation services, professional fee billing, and more.

March 21, 2024
Top-10 Compliance Risk Areas for Hospitals & Physicians in 2024: Get Ahead of Federal Audit Targets

Top-10 Compliance Risk Areas for Hospitals & Physicians in 2024: Get Ahead of Federal Audit Targets

Explore the top-10 federal audit targets for 2024 in our webcast, “Top-10 Compliance Risk Areas for Hospitals & Physicians in 2024: Get Ahead of Federal Audit Targets,” featuring Certified Compliance Officer Michael G. Calahan, PA, MBA. Gain insights and best practices to proactively address risks, enhance compliance, and ensure financial well-being for your healthcare facility or practice. Join us for a comprehensive guide to successfully navigating the federal audit landscape.

February 22, 2024
Mastering Healthcare Refunds: Navigating Compliance with Confidence

Mastering Healthcare Refunds: Navigating Compliance with Confidence

Join healthcare attorney David Glaser, as he debunks refund myths, clarifies compliance essentials, and empowers healthcare professionals to safeguard facility finances. Uncover the secrets behind when to refund and why it matters. Don’t miss this crucial insight into strategic refund management.

February 29, 2024
2024 ICD-10-CM/PCS Coding Clinic Update Webcast Series

2024 ICD-10-CM/PCS Coding Clinic Update Webcast Series

HIM coding expert, Kay Piper, RHIA, CDIP, CCS, reviews the guidance and updates coders and CDIs on important information in each of the AHA’s 2024 ICD-10-CM/PCS Quarterly Coding Clinics in easy-to-access on-demand webcasts, available shortly after each official publication.

April 15, 2024

Trending News

SPRING INTO SAVINGS! Get 21% OFF during our exclusive two-day sale starting 3/21/2024. Use SPRING24 at checkout to claim this offer. Click here to learn more →