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What medical coders should know about AI

By: exdionrcm

Medical coding and billing is one of the fastest growing jobs in the healthcare field, with a predicted 15% job growth expected between 2014 and 2024. Part of what makes medical coding and billing so difficult to automate is that each coder needs to build an intimate relationship with their employer. That’s because there’s not enough time for doctors to write detailed notes during each encounter. Instead, they jot down generic notes, and then coders interpret that information and break it down into the appropriate medical codes for insurance and billing purposes. Accuracy is vital here.

To perform this job well, medical coders and billing specialists are trained on a range of topics, including HIPAA issues, EHR administration, and medical billing software, but they also need to be familiar with the thousands of unique codes that describe ailments and procedures. It’s a vast skill and knowledge set, and that’s precisely why the human element is so important.

The role of Computer-assisted Coding (CAC)

CAC is one of the oldest forms of AI in our field. The original intent was for the computer to automatically assign the codes and submit the charges. But like flying cars and teleportation, the idea hasn’t fully materialized; however, CAC does help assign modifiers, catch correct coding edits, identify errors, and other tasks, freeing medical coders and billers to concentrate on other things. CAC proponents now concede that the human element is vital for accurate medical coding and billing. This technology also helps speed the reimbursement cycle. CAC can be a valuable lifesaver, just like that timer on your coffee pot.

Ushering in Computer-assisted Physician Documentation (CAPD)

CAPD is being added to EHRs to help providers address gaps in their clinical information. The AI reviews documentation and then guides the provider to adjust the documentation to assure it properly reflects the patient’s condition. The technology also helps capture complications and comorbidities that may affect the patient’s care and payers’ risk now or down the road.

There’s a lot of information packed into diagnostic codes and billing documents, but when doctors and coders look at these documents, they tend to become a blur of numbers and letters. In other words, no one is studying these documents to learn more about patient health patterns. This is exactly where AI has an advantage

AI can be trained to identify repeating patterns in medical coding and billing and identify those patients who were readmitted within a 30-day period. Based on those patterns, the technology can then look at single case billing codes and predict the likelihood of patient readmission. Since this kind of turnaround is bad for patient health – and bad for doctor’s performance statistics and reimbursement rates – the combination of excellent manual coding and AI analysis could provide important treatment breakthroughs for the sickest patients.

Care Transition Analysis

This technology allows payers to identify suspicious or repetitively incorrect billing patterns, and they can use it to coach or exclude the provider or facility from participation. Large practices, facilities and health systems will use this to self-identify problems in medical coding and billing. The data can help auditors and clinical documentation improvement staff create reviews and coaching to improve reimbursement cycles.

Record Reconciliation

AI is the perfect tool to sort through old records to identify conflicting diagnosis, out-of-date medications, and other inconsistencies. This can also help identify quality measurement issues and protect the provider in the future.

Another billing and coding task suited to AI is record reconciliation. Many patients have conflicting diagnoses in their files, or medications that are out-of-date. AI can easily sort through past files and documentation to determine which information is out-of-date, what may need to be added to patient files, and what files are no longer relevant. This reconciliation work can also make patient files much more manageable for doctors and coders.

Payment Productivity

From an administrative perspective, AI augmentation for medical billing and coding may also provide significant support for billing and insurance reimbursement. Firstly, AI can help identify errors in medical coding and prevent lengthy reimbursement cycles. When an insurance company spots a flaw in coding, they may reject the claim, leading to a cycle of claims and bills and phone calls that can leave patients stranded and healthcare providers unpaid.

In the near future, insurers may make decisions about what organizations and providers to partner with based on long-term data mining called “care transition analysis.” This information can help them identify suspicious billing patterns, separate hospitals with a high rate of rehabilitative success from those with extreme readmission rates, and even develop prepayment solutions for quicker transactions.

Wrapping up

Exdion uses AI and deep learning to prevent coding and billing mistakes, and eventually improve revenue. Contact our experts to automate medical coding, increase overall revenue and enhance the customer experience.

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