Cardiology Coding Alert

Technology:

AI May Be Very Useful in Your Practice, Especially During Staffing Shortages

Published on Fri May 24, 2024

AI must have complete picture of a patient’s record to identify billing errors accurately.

You have probably heard about artificial intelligence (AI) being used in many different kinds of industries. So it’s no surprise that healthcare can benefit from this technology, as well. Experts agree one such benefit may be increased productivity of revenue cycle management (RCM).

Discover more about how AI can increase your practice’s efficiency.

Add AI to Boost Your Revenue Cycle

A 2020 Change Healthcare study found that 98 percent of respondents planned to use AI in RCM by 2023. Also, approximately 65 percent of respondents claimed to use AI in RCM at the time of the study.

Two areas where AI would be a natural fit into your revenue cycle include:

1. Predict Future Claim Denials

By providing the AI with a significant amount of data related to your past claim denials, the technology will be able to predict future claim denials. This can be done in real time or as a final analysis before the claim is submitted electronically. “Denials are rising. Since 2016, the average denial rate was 9 percent and as of the second quarter of 2020, it had risen to 10.8 percent,” said Holly Ridge, BSN, RN, CPC, CPMA, manager of medical necessity and authorization denials for Duke Health in Durham, North Carolina, during AAPC’s HEALTHCON 2022 session, “Medical Necessity Denials — When and How to Appeal.”

By adding AI to your claim submission process, coders and billers can know if a claim may receive a denial before submitting the claim to the payer. Regardless of whether you receive the possible denial warning as you’re filling out the claim or as a final pre-submission check, the AI’s warning will allow you the chance to make adjustments to ensure an accurate claim.

Important: You should know that the technology will only be able to identify denials based on the information it’s provided. This machine learning (ML) uses the submitted data to develop a training dataset, which the technology compares to new information when the AI is activated.

2. Fills in During Staffing Shortages

In January 2022, AKASA published findings of a survey that looked at revenue cycle staffing shortages in hospitals and health systems. The survey showed the number of days to fill vacant roles for different levels of revenue cycle talent:

Entry-level talent: 84 days
Mid-level talent: 153 days
Senior-level talent: 207 days

AI can help maintain production in your workflow during these staffing shortages. The technology can handle simple coding cases with minimal human input, which allows human coders to tackle more complex cases. However human coders still need to review the information to ensure the AI-chosen codes are correct.

Devanshu Yadav, vice president of strategy for Raapid in Louisville, Kentucky, explains that the ultimate goal of incorporating AI into medical coding isn’t to replace human interventions, “but to make human interventions smarter,” so the coder can use their time and knowledge elsewhere.

While there is significant promise for AI to improve your revenue cycle, obstacles still stand in the way of the technology’s growth.

Understand the Challenges for AI in Medical Coding

One challenge for using AI with medical coding, billing, and the revenue cycle involves the complexity of the data. “Healthcare data is complex and understanding the clinical context is difficult for AI,” Yadav adds. At the same time, clinical documents feature different styles, abbreviations, and layouts, all of which the AI can learn over time, but these complexities will require humans to teach the technology to get it up to speed.

Another problem involves gathering datasets for ML. Healthcare organizations and hospitals are abundant spaces for data collection, which will be beneficial to training the AI to understand physician’s notes, operational notes, lab results, and imaging reports. For AI to accurately identify billing errors, the technology will need a complete picture of a patient’s record.

The necessary data includes:

Electronic health record (EHR) data
Admission notes
Discharge summaries
Physician’s notes
Lab results
Pharmacy information

Teams can gather this information through Health Level Seven International® (HL7®) interfaces or EHR connectors, health information exchanges (HIEs), and data dumps and pickups (via Secure File Transfer Protocol (SFTP)).

Also, for AI systems to code encounters accurately, the technology will need to be up to date on the medical codes, instructional notes, and guidelines. Implementing this information into the AI system will require careful guidance and assistance from human coders to ensure the output is correct. Simultaneously, human subjectivity allows them to determine the correct codes for each case.

These challenges don’t mean that AI cannot function in medical coding, but more than a sophisticated team of AI and natural language processing (NLP) specialists will be needed to make adjustments. “The key point is there cannot be one-size-fits-all AI tools, but customization needs to be done on the AI tool every time for the technology to be successful for that particular organization,” Yadav says.