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Automated Medical Coding

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  • Kyle Swarts

Status Quo is the Biggest Impediment to AI Adoption

In 2023, the healthcare industry witnessed a surge in interest surrounding the adoption of Artificial Intelligence (AI). Despite the buzz, the actual integration of AI technologies in healthcare practices remains relatively low, primarily hindered by the industry's status quo mentality. Based on new research from Bain & Company, deploying AI is a top priority in 2024 for healthcare leaders. But is it?

The adoption of AI in healthcare is at a critical juncture, balancing between academic curiosity and practical implementation. The challenges are real, but so are the opportunities. As the industry navigates the complex landscape of AI adoption, a focus on education, cultural transformation, strategic investments, and a keen eye on emerging trends will be crucial for success.

The healthcare of tomorrow is undeniably intertwined with the AI advancements of today. Innovative leaders need to embrace #artificialintelligence as a "must have'' and thoughtfully identify strategic and tactical initiatives that embrace a symbiotic relationship between humans and AI that leads to improved financial and clinical outcomes. In other words, think big and start small.

Furthermore, the coding shortage in healthcare is a reality, prompting providers to explore outsourced coding options and/or investing in AI to augment staff to maintain the practices’ financial health and stability. However, receptivity to this approach varies across the industry, with some embracing the solution and others remaining cautious. One of the key hurdles in AI adoption is identifying and agreeing on an initial use case with a quantifiable ROI. Once you get past that conversation, be committed to reimagining outdated workflows and technology.

On-going education on the applicable use of AI in the ambulatory market is required as many stakeholders are yet to fully embrace its use for trivial administrative and clinical tasks. One myth that requires continued clarity is the distinction between Computer Assisted Coding (CAC), E/M Level of Service Calculators (native to most Electronic Medical Records) and Real-Time Auditing Tools. These tools still require 100% human intervention, a far fetch from true definition of AI-enabled coding automation.  Understanding the intended use and benefit of each tool will help identify the strategic AI priorities for your organization.

In my personal opinion, AI should be deployed to eliminate mundane administrative tasks, get the providers out of medical coding, and focus exclusively on patient care.

While my opinion is a bit biased, I see firsthand how AI has made significant and practical strides in many non-revenue cycle functions. Lots of genies left in the bottle. 

No doubt, healthcare leaders today are in a daily wrestling match juggling strategic priorities while cautiously allocating funds towards AI. Where should I start? What vendor has a proven track record? What if it fails? What investment in time and resources does my team need to achieve increased productivity and efficiencies?

The safe option is investing in the status quo. Stutter-stepping on deploying AI will leave you in the dust and at a disadvantage slowly watching the competition leapfrog your competitive advantage. Challenging the status quo within your organization may feel like a battle of David versus Goliath. You’re not alone. True innovative leaders look down the barrel of the status quo and feel confident in placing calculated and strategic bets while investing the necessary political capital to embrace the future of healthcare. 

Be bold. Be Innovative. Be ready. 


Automated Medical Coding

©2024 aiHealth. All rights reserved.

©2023 aiHealth   Privacy Policy | Cookie Policy

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