Clinical documentation integrity (CDI) is foundational to both high-quality patient care and accurate reimbursement for healthcare providers, yet it's historically been limited by labor-intensive, manual workflows.

Traditional CDI programs have concentrated on a narrow subset of encounters—primarily high-dollar inpatient admissions—where health systems have the most revenue at risk and the labor investment is easiest to justify. Even within those programs, staffing shortfalls and rising costs (averaging around $80,000 annual salary per CDI specialist) have constrained review coverage, leaving many documentation gaps unresolved.

The advancement of AI has begun to unlock a fundamentally different scale of possibility. Large language models (LLMs) and machine learning (ML) can now support real-time documentation review, generate intelligent and more concise physician queries, prioritize manual chart audits based on expected impact, and even propose missing diagnoses at the point of care. 

These tools aren’t just improving inpatient workflows—they’re also enabling CDI coverage across outpatient settings where manual review was previously cost-prohibitive. The potential impact is substantial: Some AI-powered CDI tools report improving MCC/CC capture rates by 15-25%, reducing physician query volume by up to 40%, and increasing case mix index (CMI) by as much as 10%. 

By reducing friction, accelerating revenue cycle operations, and improving documentation quality, these solutions claim to offer a pathway to better financial performance and potentially better clinical outcomes. But can they deliver on those promises? And how should organizations measure ROI to be sure that they do?

In this buyer’s guide—intended for healthcare executives, revenue cycle leaders, CDI professionals, and IT decision-makers—we’ll break down how AI tools are supporting inpatient and ambulatory documentation workflows, helping to close documentation gaps, prevent revenue leakage, and reduce audit risk without expanding headcount.

Why CDI, Why Now?

According to AHIMA, CDI programs “facilitate the accurate representation of a patient’s clinical status that translates into coded data.” In other words, CDI ensures medical records are accurate, complete, and specific to reflect the true complexity of a patient’s clinical condition and the care delivered. Because of the limitations of the current manual processes, CDI has traditionally handled primarily inpatient encounters, making it more like insurance against lost revenue on the highest-risk claims as opposed to a system-wide double-check for all documentation. 

However, the deterministic nature of coding rules, clear decision patterns, and structured workflows, as well as the ability to ingest and analyze documentation volume vastly exceeding human abilities, make CDI ideal for AI. 

Beyond supporting care delivery, high-quality documentation (see the sidebar below on what “high-quality” means in the context of CDI) also enhances the overall performance of the revenue cycle downstream, potentially reducing denials or increasing the accuracy of autonomous coding. Altogether, this means that when effectively implemented, CDI—especially with optimized use of AI tooling—can be a force multiplier across the revenue cycle, with patient care and physician quality-of-life as additional benefits. 

The Evolution of Clinical Documentation Integrity

CDI, as traditionally defined, is a quality function executed after initial clinical documentation focused on improving the accuracy, completeness, and specificity of that documentation to support coding, billing, compliance, and care quality. It emerged within the inpatient setting—where the stakes for reimbursement and audit risk are highest—and remains most mature there.

Traditional CDI: Inpatient and Concurrent

In the traditional inpatient model, clinical documentation integrity specialists (CDIS)—typically nurses or HIM professionals trained in clinical terminology, coding guidelines, and regulatory requirements—review patient records during the hospital stay. This is known as concurrent review, though it's important to note that it typically happens hours or days after the clinician's initial documentation, not at the bedside or in real time.

CDISs usually prioritize cases for review based on high-dollar DRGs and the potential for DRG shift; high-complexity or high-risk patients based on severity of illness, risk of mortality, and chronic disease burden; new admissions or specific admit types; and historical audit risk. Their responsibilities typically include:

• Identifying potential documentation gaps or ambiguities (e.g., lab values, radiology findings, treatment patterns).
  

• Querying the physician to confirm or clarify what’s already clinically supported but not yet documented.
  

• Encouraging specificity (e.g., “pneumonia” becomes “aspiration pneumonia due to stroke with dysphagia”).
  

Ultimately, CDI is not about changing the care delivered, but ensuring that the record fully reflects the complexity of that care. The attributes listed above that contribute to “high-quality” documentation collectively support appropriate reimbursement, reduce compliance risk, and lay the foundation for quality measures and performance analytics.

Expanding Definitions of CDI

While CDI has historically most often referred to a well-defined inpatient workflow led by human CDI specialists performing concurrent review during hospital stays, the term has broadened significantly. Today, many vendors describe their products as “CDI solutions” even if they operate outside of the traditional inpatient context and workflow.

These offerings often focus on professional billing documentation, ambulatory visits, or retrospective review, and they don’t necessarily function as a tool for a dedicated CDI team. Instead, they support documentation improvement as part of a broader effort to ensure clinical accuracy, coding specificity, and complete diagnosis capture across all care settings. 

This definitional shift has created market ambiguity. It is reasonable—and often appropriate—for these vendors to position themselves within the CDI landscape, but it’s important to recognize the distinction between:

• Traditional CDI, with its established inpatient workflows, specialized teams, and case prioritization strategies; and
  

• Documentation improvement tools that apply CDI principles more broadly, particularly in settings where traditional CDI workflows haven’t historically existed. 
  

Understanding this distinction is critical for evaluating vendors, setting internal expectations, and measuring impact relative to existing CDI infrastructure. For the purpose of this exploration of the AI CDI market, we’re using this broader definition of CDI, which essentially applies CDI principles across a range of workflows. 

Traditional CDI Vendors and Workflow Systems

Established vendors like Solventum (formerly 3M) 360 Encompass, Optum Enterprise CAC and CDI 3D, and Nuance CDE One built tools to support the traditional CDI workflow at scale. They serve as systems of record for documentation improvement, allowing CDISs to prioritize cases for review, manage physician queries, track team productivity, and integrate with coding systems. Early versions relied on rules engines and NLP to help CDIS teams scale, and these platforms remain widely adopted across U.S. hospitals.

However, these tools were built for manual team-based workflows and are only now integrating more advanced AI capabilities. As LLMs and predictive models create opportunities for real-time documentation support and broader encounter coverage, a new generation of vendors is challenging the legacy model with AI-native approaches that aim to reduce manual burden, extend CDI principles into outpatient care, and unlock more scalable impact.

Frameworks for Selecting an AI CDI Solution

As AI reshapes the landscape of clinical documentation, health systems face a growing array of CDI tools that differ significantly in workflow design, technical architecture, and intended outcomes. To navigate this evolving market, organizations need a clear framework for comparison that accounts not just for functionality, but also for how each solution aligns with clinical operations, revenue goals, and implementation readiness. 

In the sections that follow, we outline key dimensions of differentiation, including whether the tool operates at the point of care or retrospectively, coverage of inpatient versus outpatient care contexts, solution scope and integration, technical fit, and commercial considerations.

Choosing Your AI CDI Workflows

The biggest area of differentiation amongst AI CDI vendors is whether the solution: 

• Tackles documentation improvement at the point-of-care or even before the encounter, functioning as a clinician-facing tool.
  

• Improves the efficiency and effectiveness of traditional CDI workflows where review is done by CDI teams, either concurrent to an inpatient stay (though after the clinician encounter) or post-encounter in outpatient contexts.
  

• Takes a broader view to revenue integrity, functioning as a pre-bill review on claims, sometimes even post-CDI team review as a secondary audit layer. These are distinct from AI-optimized traditional CDI workflow tools in that they function outside of and potentially in addition to traditional CDI tasks. 
  

Point-of-Care AI CDI Tools

Point-of-care solutions support clinicians in ensuring the initially generated notes achieve the highest level of quality and completeness, potentially reducing the number of issues to be addressed downstream. One novel aspect of some point-of-care solutions is that—beyond the typical CDI priorities of complete and accurate documentation for coding purposes—there’s an additional focus on accurately and transparently identifying diagnoses from clinical data, with the intent of improving clinical care in the moment as well as revenue downstream. 

Given the focus on clinician workflows, for many (though not all) point-of-care options, CDI support acts more as an “add-on” to a core ambient scribing or chart summarization functionality. For example: 

• Regard, which sits across CDI, clinical decision support, and summarization, actually pre-generates a note for providers, so they start an inpatient encounter with a rough draft. 
  

• Others take a more intra-encounter approach, with Ambience and MarianaAI drafting CDI recommendations alongside clinical notes being produced by their integrated ambient scribe.
  

• Evidently bills itself a “Grammarly for CDI,” essentially acting as a real-time editor during charting, whether the initial note was created by an ambient scribe or written by the clinician.
  

Retrospective AI CDI Tools

Retrospective solutions, however, remain the most common approach among AI CDI vendors, with tools supporting documentation audits and chart reviews prior to bill submission. Within retrospective solutions, there are really two subgroups: 

Traditional CDI workflow tools enhance or automate traditional CDI processes, ensuring documentation completeness and accuracy with an eye towards quality, safety, reporting, patient outcomes, as well as revenue opportunity. They leverage AI to support the traditional CDIS functions of prioritizing charts, identifying discrepancies between documentation and coding or payer guidelines, and generating physician queries. Of the solutions explored in this guide, they are the most apples-to-apples alternative to legacy CDI tools, completing the same functions but integrating more modern models. 

Pre-bill or revenue integrity solutions offer a final “double check” on documentation with an eye toward improving reimbursement. Some solutions focus exclusively on “high-impact” cases whereas others, like SmarterDx for example, allow teams to review all cases with potential discrepancies. 

In addition to addressing traditional CDI priorities, revenue-focused vendors specifically prioritize proactively preventing denials. This means that chart audits and reviews not only flag and prioritize based on highest potential new revenue (like missed HCCs), but also proactively on where lack of documentation specificity may mean a higher likelihood of denial. It’s also worth noting that some vendors are increasingly working toward integrating across the revenue cycle and using claims outcome data to improve their pre-bill algorithm. 

Ultimately, the lines between tools that optimize traditional CDI workflows versus pre-bill solutions can be blurry and many solutions in this retrospective bucket will overlap.

Pros and Cons of Point-of-Care vs. Retrospective Solutions

Choosing the right AI CDI solution depends on a system’s clinical workflows, staffing structure, and desired ROI profile.

Change management: Point-of-care solutions do require additional change management on the clinician side, particularly around EHR integration and onboarding. However, the long-term gains in documentation quality, revenue integrity, and operational efficiency can be substantial if implemented correctly. Post-encounter solutions, by contrast, are typically easier to stand up but may rely more on retrospective audit, rework, or manual escalation. 

Integration: Point-of-care solutions universally require embedding directly into the EHR. All retrospective solutions also require some level of data-in and data-out integration, but typically have their own application (sometimes integrated into an organization’s single sign-on).

Pricing: Pricing models vary amongst vendors with some operating on a pure subscription model whereas others, like AKASA or SmarterDx, operate on a contingency basis with fees tied directly to financial impact. 

Attributability: With multiple CDI tools layered across the documentation journey—from point-of-care to traditional CDI workflows to pre-bill—it can be hard to determine which solution drove a specific improvement. Attribution gets muddied when multiple tools touch the same chart. Post-encounter solutions, especially those at the final prebill stage, often enjoy clearer attribution: if they catch something, it wasn’t caught by anything before, making their impact easier to quantify. In contrast, point-of-care tools require more robust tracking to isolate and defend their contributions.

Expansion of AI CDI Into Outpatient Workflows

Traditional CDI has been centered on inpatient care where trained specialists review documentation during hospitalization to support DRG assignment, coding, and audit readiness. However, there is growing attention on improving documentation across outpatient settings (growing from 17 to 25% from 2021 to 2023, according to an ACDIS survey), with support of value-based care and risk-adjustment driving much of the attention. Ensuring accurate and complete capture of patient conditions in value-based care arrangements is critical to financial viability, and outpatient CDI plays an important role in closing any gaps.  

This shift is also driven by the ever increasing complexity of outpatient coding. For example, accurately assigning evaluation and management (E/M) levels can significantly impact revenue if not handled appropriately. Recent changes to CMS E/M coding guidelines in 2021 and 2023, which placed greater emphasis on medical decision-making and documentation specificity, have further accelerated this trend. 

In general, outpatient CDI workflows are similar to inpatient, but the higher visit volume, shorter timelines, and thinner staffing generally mean there’s a greater emphasis on full automation for easy cases, and algorithmically-driven prioritization and auto-generated queries for specialist-driven workflows. Some organizations may find that clinician-driven solutions like those discussed above are their best option based on a lack of CDI staff. 

It’s worth noting there is a growing class of adjacent solutions focused specifically on care gap closure and risk adjustment optimization (e.g., Navina, Reveleer), which we aren’t covering here within our CDI guide, but may be worth exploring for some organizations, as they support many of the same goals around documentation accuracy, codability, and compliance.

Ultimately, the care setting shapes both the technical requirements and the operational model for AI CDI tools. While inpatient solutions typically augment trained review teams, outpatient tools must adapt to leaner staffing models, different documentation workflows, and a higher volume of encounters. Buyers should be mindful of how these tools align not only with billing structures (facility vs. professional) but also with clinical practice patterns and revenue strategy.

Additional Considerations

As previously discussed, care setting and workflow alignment will heavily influence which solutions are viable for a given organization. But once you have a shortlist of potentially viable options, there are a number of other important considerations. 

Solution Scope and Workflow Integration

• Does the solution support inpatient, outpatient, or both? Within outpatient, what specialties are supported?

• How does it integrate into the existing clinical workflow? Is it pre-charting/pre-round, during the encounter (ambient/real-time), or post-encounter/post-coding/pre-bill?

• What’s the change management involved? Who’s workflow changes the most? Are there new workflows involved?

• Does the solution provide confidence or probability scores for suggestions? Can these scores trigger automation?

• Does the solution generate queries automatically or require human intervention? For automatically generated queries, how are the templates decided? Can they be customized?

• What EHRs and claims/RCM systems are integrated?
  

Performance Overview and Technical Fit

• How does it incorporate clinical guidelines, payer rules, and regulatory requirements? For example, how are clinical practice guidelines sourced; how are your organizational policies or payer contracts shared? 

• How does it reconcile conflicting guidelines (e.g., hospital policy vs. payer rule)?

• How does it stay updated as guidelines change (e.g., Sepsis-3)?

• How does it verify that diagnosis or coding recommendations are clinically valid and aligned with rules?

• How does the system identify missed or insufficient diagnoses or diagnoses lacking necessary specificity?

• How does it pinpoint specific parts of the medical record supporting suggestions? Does it provide evidence links?

• Does it use Generative AI, Predictive Modeling, NLP? How are these applied?

• Are models custom-built/fine-tuned, off-the-shelf, or a combination? How are they trained and validated? Custom-built solutions may seem more precise, but can be costly; off-the-shelf solutions offer cost savings but have the perception of reduced accuracy. Organizations will likely all view these tradeoffs differently.

• What strategies are used to reduce AI hallucinations? Are audit trails visible to the end-user?

• What is the policy on data ownership and how is data handled upon contract termination?

• What data gets returned to the models for continuous feedback? Is my data used to improve model performance for all customers?
  

Commercial Details

• What are the expected ROI metrics? (e.g., CMI change, DRG accuracy, HCC/RAF capture, denial reduction, query reduction, time savings)?

• What is the typical payback period? What data supports the ROI claims?

• What reductions in manual effort can be expected for CDI staff, coders, and providers?

• What is the pricing structure (e.g., subscription, implementation fee, flat fee, per-patient-per-month, contingency/performance-based, shared savings)?

• Are there fees for licensing, maintenance, integration, customization, or optional features?

• Is a free trial or pilot program offered to validate performance before committing? What are the terms? Are there opt-out clauses tied to performance benchmarks?
  

Assessing these points will help health systems determine which AI CDI solution best aligns with their operational needs, financial goals, technical environment, and compliance requirements.

Is AI CDI Worth It—and How Do You Measure ROI?

Not every organization will benefit equally from an AI CDI investment. For provider groups or health systems evaluating whether it’s the right time to act, the first step is identifying where documentation breakdowns are creating measurable financial, operational, or compliance risk. Signs it may be time to invest include: 

• Persistent post-discharge query lag
  

• Lower than forecasted CC/MCC or HCC capture
  

• Growing denial or downcoding rates
  

• Resource constraints limiting CDI coverage

Organizations should look for a “minimum viable problem,” a targeted CDI pain point that, if addressed with automation or triage support, could generate disproportionate impact. Once a need is established, the next step is to size the opportunity using an ROI framework grounded in measurable outcomes. AI CDI solution can drive value across three categories:

• Operational efficiency: improvements in query rate, turnaround time, and CDI staff productivity.
  

• Financial performance: case mix index (CMI) uplift, increased CC/MCC or RAF capture, reduced denials.
  

• Clinical outcomes: clearer documentation of acuity, SOI/ROM score accuracy, and alignment with quality reporting.
  

Calculating ROI

To calculate ROI, compare baseline performance against projected improvements based on peer benchmarks and vendor-supported models. Some vendors will also perform an analysis on historical data to help assess potential future uplift. For example, if a solution reduces query turnaround from 72 to 48 hours, or increases CC/MCC capture by 10–15%, estimate the downstream impact on coding accuracy and reimbursement. Ensure vendors contextualize claims by clarifying the scope (e.g., inpatient DRGs or outpatient HCCs), baseline metrics, and timeframe of any reported gains.

It’s also worth considering the impact of AI CDI solutions on future headcount needs. In general, AI CDI vendors do not promise to reduce CDI staff, but rather increase productivity and coverage of a greater proportion of encounters. With that said, we’ve heard from provider organization leaders that they’re considering AI CDI as protection against disruptions in the event of staff turnover, as a large portion of coding and CDI workers are reaching retirement age and exiting the workforce. 

Finally, match solution type to scope of impact. Targeted tools make sense when a specific metric—like RAF accuracy in clinics or denial volume in outpatient surgery—is underperforming. Broader platforms may deliver greater ROI when multiple CDI processes are strained across departments. And if current performance is strong and EHR-native capabilities are on the horizon, a wait-and-see approach could be reasonable. The key is aligning investment to the most pressing gaps, then holding vendors accountable to real-world results.

Final Thoughts

The promise of AI-powered CDI is compelling, but success hinges on precise problem-solution fit. Health systems that clearly identify their documentation pain points (e.g.,missed CC/MCCs, incomplete HCC capture, or mounting denials) and match them to the right solution approach (point-of-care, traditional CDI, or pre-bill) will see the strongest returns. Those that treat AI CDI as a silver bullet, without considering their unique workflows and risk tolerance, risk implementing sophisticated technology that merely shifts work rather than eliminates it.

Need more personalized support? Whether it’s identifying the right CDI workflows for your organization, thinking through ROI, or ultimately making vendor decisions, fill out this form to connect with our team. Through these engagements, we support health systems and provider groups in navigating these inflection points, positioning them to make smarter and more agile tech stack decisions that drive sustainable success.