A Complete Guide to Clinical Documentation Improvement Software: Workflow, Benefits, and Real-World Use Cases

Key Takeaways
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• A clinical documentation improvement program ensures patient records accurately reflect clinical status to support coding, billing, and care.
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• Traditional CDI models are limited by human capacity and are often expensive to scale.
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• Clinical documentation improvement software uses AI and NLP to help healthcare organizations create accurate, complete, and compliant patient records. 
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• Blended “human-in-the-loop” CDI programs are rising.
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• CombineHealth’s AI agents, Lia and Amy, work within your existing CDI workflows and help capture structured clinical notes and ensure accurate coding.
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• Lia and Amy provide real-time, scalable, and cost-effective CDI support, integrating directly into EHRs without extra manual steps.

46%. That’s how many denial claims are caused by missing information or inaccurate documentation^[1]. 

Even the most advanced healthcare systems could break if they don’t sort out their clinical documentation process. And it’s often because clinicians are stretched way too thin, clocking in extra hours just to keep their EHR clean. 

The result? Burnout, incomplete records, errors, and worse, delays in reimbursements.

Traditionally, in-room and remote scribes or speech-to-text tools have been implemented as part of a clinical documentation improvement program. And while they each help, they can be quite expensive, often require constant retraining, and still leave room for human error. 

That’s where AI-powered clinical documentation improvement software comes in. They just don’t transcribe, but also understand the entire correspondence, analyze clinical context, flag documentation gaps, and all for a fraction of the price of traditional solutions.

Still skeptical about using AI for clinical documentation? This guide breaks down how AI-powered solutions can reduce your clinical documentation burden and help ensure only accurate, complete patient data makes it to the billable code. 

What Is Clinical Documentation Improvement?

The clinical documentation improvement (CDI) process involves reviewing documentation of patient records to make sure they accurately represent a patient’s clinical status—from registration to procedural treatments. It verifies whether the documentation clearly outlines the patient’s health condition, the treatment they’re taking, and the outcomes of the treatment.

An effective CDI program is built on the collaboration between multiple staff members—physicians, nurses, and CDI specialists. Many healthcare systems also bring in third-party support like AI-powered CDI software that flags documentation gaps in real time or dedicated CDI consulting teams to handle CDI operations.

Why Is Clinical Documentation Improvement Important?

Poor clinical documentation can seriously impact your bottom line. 

We’re talking about claim denials and reimbursement delays caused by missing details, errors, and inconsistencies in a clinical document. 

Let’s take an example of a document of a patient with a fractured ankle.

The doctor puts on a cast and sends the patient home, but forgets to document which ankle was fractured. 

According to the ICD-10 coding guidelines, laterality matters. Left and right ankles have different codes: M25.1 (left) and M25.2 (right). If laterality isn’t documented, the coder might have to assign M25.9, i.e., unspecified ankle fracture, which is likely to be denied by the payer.

Now, the coder has two options:

1. Take the risk and submit a vague code
2. Go back to the doctor and ask for clarification

Either way, it costs time and risks reimbursement and denial. And if the doctor didn’t even mention the ankle at all? Denial is almost guaranteed.

This is exactly why clinical documentation integrity matters, as it’s not just paperwork, but it’s how hospitals generate revenue.

Types of Clinical Documentation Errors

Let’s look at the common types of clinical documentation errors that CDI programs are generally designed to catch and correct:

What Does a CDI Workflow Look Like?

A CDI program runs in parallel with the medical revenue cycle. CDI specialists regularly review patient charts to identify documentation gaps and work closely with the provider to fix clinical documentation issues. 

A typical CDI workflow follows these four steps:

1. Case selection and prioritization: The CDI team selects which patient charts to review. These could be charts with high risk, high reimbursement, or high probability of documentation errors. 
2. Documentation review and gap analysis: CDI specialists carefully examine the medical record to spot inconsistencies, missing details, or vague terminology.
3. Query creation: The CDI specialist creates a compliant query if the documentation is incomplete. The query is made to the provider to clarify or specify a diagnosis.
4. Provider response and documentation update: The CDI and coding teams coordinate to ensure chart changes are reflected. 

While this is more of a general overview of the CDI process, the in-patient and out-patient workflows may vary.

In-patient CDI Process

When a patient is admitted to the hospital, CDI specialists (usually nurses versed with clinical and medical coding guidelines) review the patient’s medical records before the patient is discharged. If something’s unclear or missing, they send a query to the provider asking for clarification.

Consider this example of a typical inpatient CDI review:

A 68-year-old woman was admitted for severe dehydration due to vomiting and diarrhea. The attending physician documented only “dehydration” in the discharge summary.

The case was coded as:
- Primary DX: E86.0 (Dehydration)
- DRG: 640 (without CC/MCC)
- Reimbursement: ~$6,000
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But, here’s what actually happened during the stay:

- Treated with IV fluids and antiemetics
- The lab result showed a creatinine spike from 0.9 to 2.3
- Urine output < 400 mL/day
- Nephrology consult noted acute kidney injury (AKI)

The CDI specialist flagged the issue and sent a complaint query, and the document was updated with AKI (N17.9) codes. As a result, the reimbursement value increased to $8,500.

Out-patient CDI Process

In outpatient settings, like a doctor’s office or clinic, CDI works a bit differently. Since the consultation and treatment are already completed without admission, CDI specialists review the provider’s documentation after the visit. This is called a retrospective review, and it might happen days, weeks, or even months later, depending on the clinic’s workflow.

Unlike in hospitals, outpatient CDI doesn’t involve formal queries. Instead, it focuses on education, like helping providers understand how to document more clearly and completely in future visits.

Here’s an example from one of our customers:

A patient consults the doctor in their clinic complaining of severe shoulder pain. The physician ordered an X-ray, reviewed the radiologist’s report, and agreed with the findings, but didn’t document their own interpretation.

They also ordered CBC and CMP labs and administered IV morphine for pain due to the intensity of the patient’s pain. 

But, due to the missing imaging interpretation, the case was down-coded from Level 5 (99285) to Level 4 (99284), resulting in lower reimbursement.

Amy, CombineHealth’s AI-powered coding and CDI agent, flagged this as a CDI issue, highlighting that proper documentation of the provider’s X-ray interpretation could have justified a higher E/M level.

Where Traditional CDI Falls Short (and Where AI Steps In)

Most healthcare organizations either handle their CDI process in-house or outsource it to a third-party service provider. But to understand which of these two approaches would benefit your revenue cycle, it’s best to weigh the following factors:

• Cost and ROI: Partnering with an external CDI expert can be expensive and expose sensitive patient data to third parties. That’s where an AI-powered CDI software can be comparatively cost-effective, as it often requires a subscription or license fee. 
• Scalability and human dependency: CDI companies (or in-house staff) have a human capacity limit. Each nurse or CDI specialist can only review so many charts per day, and expanding coverage means hiring and training more staff. On the contrary, organizations using AI have reported a 35–45% increase in their chart review volumes without adding staff^[2].
• Staffing shortages and skill gap: The shortage of CDI talent and high turnover remain key challenges for healthcare organizations^[3]. An AI solution can act like a “force multiplier” for your CDI program.
• Documentation quality and consistency: Relying on manual review alone can lead to errors and inconsistencies. AI-assisted documentation reviews identify approximately 32% more instances of missing or insufficient clinical documentation compared to traditional CDI processes^[2].‍
• Workflow efficiency and speed: Usually, CDI staff manually have to sift through charts, and physicians often get queries days after care. All of this can slow down billing or lead to memory gaps. But, AI-driven solutions prioritize charts in real-time and even assist at the point of care.

How Should You Approach CDI for Your Organization?

Working with CDI specialists can certainly improve documentation and revenue, especially when you have a foundational CDI program or skilled internal team. But the margin narrows quickly when you factor in:

• High personnel costs (CDI nurses, coders, consultants)
• Staffing shortages and hiring challenges
• Clinician burnout from documentation overload
• Slower turnaround times for provider queries
• Scaling cost and effort without increasing proportional headcount 

This is where choosing an AI-powered CDI solution is the winning choice. 

Many organizations follow a blended approach, i.e., a collaboration between AI and CDI staff, also known as a “human-in-the-loop” model. AI can ensure no obvious gap is overlooked and enforce consistency, while human experts handle the complex cases and clinical reasoning that AI might not fully grasp.

Take the example of the Cleveland Clinic’s CDI program^[2]. 

They implemented a “human-guided” AI approach, with CDI specialists retaining ultimate authority over documentation decisions. As a result, they saw a 15% increase in case-mix index (CMI) accuracy and a 30% reduction in retrospective queries.

How CombineHealth’s AI-powered Solutions Optimize the CDI Process

CombineHealth offers two AI agents (Lia and Amy) to help you enhance the accuracy, efficiency, and scalability of your CDI program. Instead of relying on CDI specialists to manually review select charts every other quarter, these agents perform real-time quality checks on every case, flagging documentation issues as they arise.

Let’s take a look at how both tools can fit into your existing CDI workflow:

Lia: The Intelligent AI scribing agent

Lia works as your personal scribing assistant who takes clinical notes on your behalf and flags missing details in real-time. All you need to do is start the narration, and Lia will listen to every word and structure it into clean and detailed clinical notes based on clinical standards.

She also flags potential documentation issues, which you can review and choose to accept or reject. If accepted, Lia automatically updates the notes with the suggested changes.

Amy: Your real-time CDI and coding partner

Amy is trained to read clinical notes, assign accurate medical codes, and identify documentation gaps that could affect reimbursement or claim approval. 

Sourabh Agrawal, CombineHealth’s Co-founder, points out that Amy’s role goes well beyond medical coding:

“Think of Amy as more than just a coder. She’s your frontline CDI gatekeeper. She ensures that no documentation issue slips through and flags CDI issues every single time. A human coder might overlook them—after all, it’s not always their primary focus."

Here’s how Amy supports your CDI efforts:

1. Amy extracts relevant codes from the provider’s notes—diagnosis (ICD-10), procedures (CPT), and E/M levels—with justification for each code.

2. As she codes, she simultaneously flags missing or vague documentation that could lead to claim denials or under-coding.

3. For critical documentation gaps, Amy raises compliant queries to providers. For example, if a diagnosis is missing key details like laterality or specificity, putting the claim at high risk of denial, Amy flags it as a priority for provider clarification.

4. Amy automatically logs CDI issues across charts and providers, generating insights like the most common documentation errors or recurring gaps by physician.

Ready To Complement Your CDI Efforts With AI?

CombineHealth’s AI agents use generative AI and large language models (LLMs) that deeply understand revenue cycle guidelines and pick up on clinical and coding nuances. They act as intelligent co-pilots for your CDI process, ensuring documentation is complete, compliant, and optimized for reimbursement.

Book a demo to see Lia and Amy in action.

FAQs

How to improve clinical documentation?

Follow these steps to improve your clinical documentation process:

1. Standardize your clinical documentation by creating templates, compliance standards, and terminology guidelines.
2. Offer training to your clinicians on proper documentation practices.
3. Assign dedicated CDI specialists to review charts, identify gaps, and collaborate with providers to resolve documentation issues.
4. Invest in an AI-assisted CDI software to analyze clinical notes in real-time, flag missing details, and ensure your documentation supports accurate coding.

What is CDI software?

Clinical Documentation Improvement (CDI) software uses AI and NLP to review patient records in real time, flag documentation gaps, and ensure clinical notes are accurate, specific, and billing-ready. It streamlines coding, reduces denials, and supports compliance, all while easing the burden on providers.

What is the difference between CDI and coding?

CDI bridges the gap between what is documented and what is required for accurate coding, reimbursement, and compliance.

Medical coding, on the other hand, involves translating that documentation into standardized codes (ICD-10, CPT, HCPCS) used for billing, reporting, and analytics.

What is the clinical documentation improvement plan?

A Clinical Documentation Improvement (CDI) plan outlines strategies to ensure patient records are accurate, complete, and reflective of the care provided. It typically includes structured workflows for real-time chart review, compliant provider queries, documentation training, and performance tracking.

References

^[1]Fierce Healthcare. https://www.fiercehealthcare.com/providers/provider-surveys-vendor-benchmarking-data-underscore-rising-claims-denial-rates, sourced July 7, 2025

^[2]Medlearn. https://medlearn.com/ai-and-augmented-intelligence-in-clinical-documentation-integrity/, sourced July 7, 2025

^[3]HFMA. https://www.hfma.org/revenue-cycle/strategies-for-success-tackling-common-clinical-documentation-integrity-challenges-head-on/, sourced July 7, 2025

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