The HIM Advantage: How This Cross-Functional Role Improves AI Success in Revenue Cycle

The healthcare revenue cycle is undergoing a significant digital shift. AI and automation are increasingly used to address persistent challenges in coding accuracy, denial management, and documentation quality. According to a 2025 HFMA survey, 90% of revenue cycle leaders believe AI will be moderately to extremely effective in improving CDI and coding accuracy. The opportunity is substantial, including faster claim processing, reduced administrative burden, and recovered revenue.

Despite this promise, results vary widely. Some organizations report meaningful improvements, while others struggle to achieve ROI after significant investment. The difference is rarely the technology alone. More often, it comes down to how AI is implemented and who is involved in guiding that implementation.

Many AI initiatives fail because they exclude the professionals best equipped to ensure success. Health information management (HIM) leaders sit at the intersection of clinical documentation, coding operations, and payer requirements. That position makes them critical to determining whether AI improves outcomes or introduces new financial and compliance risk.

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There are two main issues that commonly undermine AI performance in revenue cycle operations: First, AI systems trained only on historical data tend to reproduce existing documentation gaps and coding inconsistencies. An AI model trained on incomplete physician notes will generate incomplete codes, simply at a faster pace. Second, many implementations are designed to replace workflows rather than support them, leading to disruption and resistance from the teams expected to use these tools every day. The result is a familiar paradox: the people with the deepest understanding of workflow realities are often excluded from implementation decisions.

Organizations that achieve consistent results take a different approach. They involve HIM professionals as strategic partners in AI implementation rather than positioning them as downstream users adapting to decisions made elsewhere.

Why HIM’s Cross-Functional Position Prevents AI Failure

HIM professionals operate across clinical documentation, coding accuracy, billing operations, and payer policy requirements. This cross-functional visibility exposes complexities that siloed teams and technology vendors often miss.

Consider AI coding software evaluated primarily on its ability to maximize reimbursement. A system focused on assigning the highest-value DRG may technically code accurately, but if documentation does not support medical necessity, it increases audit exposure. Short-term gains can quickly turn into denials, recoupments, and costly RAC audits when clinical justification is not defensible.

This distinction is critical. Effective AI does not simply pursue higher reimbursement. It evaluates whether the documentation supports codes that will withstand payer scrutiny. HIM leaders are uniquely positioned to assess whether an AI solution reflects clinical reasoning and payer reality or whether it is optimizing for outcomes that create downstream risk. When implementations prioritize defensibility and documentation integrity, organizations build sustainable financial performance rather than temporary wins.

The Implementation Gap: Why AI Needs HIM Guidance

Many generic AI tools fail because they attempt to automate entire workflows without understanding operational constraints. The promise is efficiency, but the reality often involves shifting work rather than reducing it.

Vendor selection frequently prioritizes polished demonstrations and theoretical efficiency gains, with limited input from the teams responsible for day-to-day execution. When workflow integration is an afterthought, tools that appear effective in demos introduce friction in production environments.

HIM-guided implementation focuses on augmentation rather than automation. AI is used to handle mechanical and time-intensive tasks such as record retrieval, documentation analysis, policy matching, and document drafting. Human expertise remains central for decisions that require clinical judgment, interpretation, and regulatory awareness. In this model, AI supports HIM professionals rather than attempting to override them.

Operationally, this enables meaningful change. AI can surface relevant evidence, identify documentation gaps before submission, and prioritize targeted queries. HIM professionals move away from manual execution and toward oversight, validation, and refinement, applying payer-specific and regulatory knowledge where it matters most.

The Multiplier Effect: AI That Amplifies HIM Impact

When implemented correctly, AI increases HIM capacity rather than reducing it. Coding and CDI teams handle higher volumes with improved accuracy while focusing their expertise on complex cases, including atypical diagnoses, conflicting documentation, and scenarios that fall outside standard patterns.

By removing the cognitive burden of mechanical work, AI expands what is economically feasible. Organizations that previously appealed only the highest-dollar denials due to staffing constraints can pursue a broader set of claims. Appeals that were once deprioritized become viable. Coding accuracy improves as teams gain time for prebill review instead of reacting solely to denials. CDI specialists shift from retrospective chart review to proactive physician education.

Positioning HIM for Strategic Leadership

Organizations achieving ROI from AI have done more than purchase new software. They have recognized that HIM’s cross-functional role positions these professionals to guide implementation, measure success appropriately, and identify where feedback loops break down.

HIM leaders are uniquely positioned to translate between clinical documentation, coding standards, payer policies, and operational workflows. However, most legacy systems do not give them the tooling required to operationalize that role consistently. This is where technology matters.

At Cofactor, we built our AI platform to work with HIM expertise and enable it at scale. Our platform helps HIM teams act on insights, implement feedback loops, and continuously improve documentation and appeal outcomes. HIM professionals retain oversight and judgment, while Cofactor provides the connective infrastructure required to turn that expertise into measurable results.

The question for healthcare organizations is no longer whether AI will reshape revenue cycle operations. It is whether HIM leaders will be empowered to guide that transformation effectively. Organizations that involve HIM in vendor selection, implementation strategy, and ongoing optimization are more likely to see durable gains rather than short term automation wins.

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