Coronary artery bypass — also known as CABG — is the most common form of open-heart surgery in the United States. Each year, approximately 500,000 of these potentially life-saving procedures are done in hospitals around the country.
However, a patient who is a candidate for this procedure may present with comorbidities — such as diabetes, chronic anemia, or kidney disease — that increase their risk. They may develop an acute kidney injury or other complications that can impact the postoperative course and extend their hospital stay beyond what was initially expected.
How are you handling these and other complex clinical scenarios in terms of clinical documentation integrity (CDI)?
Equally important: Are you being reimbursed for the care your clinical teams provide? And do your hospital’s quality scores accurately reflect that work?
It all starts with the codes.
I like to think of it like this: The DRG is the title of the story and the codes make up the sentences; they are the nouns, the adjectives, and the action verbs describing how sick a patient is.
Without an accurate, descriptive patient story, you’re not going to have the right coding–and that means you’re not going to capture the proper data or get paid the appropriate reimbursement.
Let’s think through two ways that concurrent review helps capture the complexity of your patients while also supporting teamwork.
- Concurrent review captures clarification while the patient is still in the hospital–and the care team is still engaged in their care. This means your CDI team can use a variety of nudges and alerts to help the physician while they’re documenting. The end result: The physician captures all the necessary information for coding. Also, CDI queries are often less disruptive to the physician’s workflow because there’s no struggle to remember a patient who has already been discharged.
- Concurrent review supports relationship-building between physicians and CDI teams. CDI specialists who conduct concurrent reviews typically work at the same facility with physicians and share the same goals of wanting to deliver high-quality patient care. Also, the key stakeholders–from the CEO to the CFO to the VP of revenue cycle management to the VP of quality–share the same understanding of the role of clinical documentation integrity.
This is important in terms of buy-in from physicians and their acceptance of CDI queries. It’s also important because key stakeholders are aware of and support the concurrent review program.
The reality: Without an accurate, descriptive patient story, you’re not going to have the right coding, and that means you’re not going to capture the proper data or get paid the appropriate reimbursement.
This is where AI comes in.
AI technology can help you work smarter and with more confidence — knowing your documentation is accurate and that your role truly matters to both clinical care and the bottom line.
Using a hybrid approach that combines AI precision with multidisciplinary clinical–coding expertise to interpret the gray areas between documentation and coding — can reveal opportunities that would otherwise be lost.
Here’s a breakdown of how AI technology can improve accuracy in documentation and coding:
- Identification: AI reviews every chart to uncover clinical signals, codable insights, and missed opportunities within data.
- Interpretation: Physician-led clinical and coding experts review each signal using medical knowledge, coding standards, and documentation best practices — turning gray areas into clear, accurate codes that reflect the actual care delivered.
- Empowerment: Updates flow directly into your system, keeping physicians, coders, CDI specialists, and revenue cycle teams aligned. Clear reports and practical education support lasting accuracy across the organization.
So, what about the patient who had the CABG procedure? Each patient is different. A patient with a preexisting condition, such as chronic kidney disease, is at a greater risk and may stay in the hospital longer than expected. During the postoperative period they may develop conditions that need to be addressed, and these need to be included in the story — as do the codes.
Capturing the severity of the patient’s chronic kidney disease is a must; it translates into an accurate risk adjustment for that patient. It’s just as important to identify the underlying etiology causing altered mental status — such as metabolic encephalopathy — as it is to document the condition itself. That information must be captured in the patient chart before it’s sent to billing.
You must have the answer to this single question: Why was the patient in your hospital longer than expected?
Physician-led AI technology can interpret these types of findings. It translates clinical reality into precise, defensible codes that reflect the full story of care and strengthen accuracy across all reported metrics. And clinical integrity means better financial performance.
Missed opportunities
The fact is, it’s hard to get staff. Generally, people don’t want to work weekends. Add to that, weekends are usually busy. That means all those cases sit in the CDI queue on Monday. Here’s what’s going to happen: Even with the best of intentions, something will be missed.
I offer an example from my own experience from concurrent CDI work: When a patient was admitted on a Thursday, we reviewed their chart on a Friday. But what happened if the patient was discharged on a Sunday?
The chart would probably drop out of the queue on Monday, which is often the day with the highest volume of charts to review. In general, approximately 20% of charts are lost to final review. That translates to a lot of dollars.
Then there’s your hospital’s reputation. If you don’t code patients’ charts appropriately, it’s going to look like it took you longer to take care of patients than the other hospital down the road. That’s a tough pill to swallow if the only difference is that the other hospital accurately captured the complexity of its patients, and you didn’t.
Tell the real story
AI-driven clinical models review every inpatient chart before billing, surfacing the clinical and coding opportunities that are often lost in translation between medicine and coding. The result is more certainty, fewer unnecessary queries, and a final coded record that accurately reflects actual care.
When you combine clinical integrity with smart automation, AI helps health systems capture the revenue they’ve already earned — and sets them up for greater accuracy and innovation down the road.
AI allows CDI teams to fully understand the patient story and close the gap between the clinical picture and the codes used to represent it. It can be a welcome partner to support — not replace — your expertise.