Healthcare changes constantly, and it should. These innovations in care delivery are improving patient outcomes and expanding access. New tech is streamlining back-office processes, reducing administrative burden, and helping payers manage an increasingly complex system more efficiently. The goals are universal - reduce costs and improve care in a landscape that is relentlessly getting more expensive and complex.
However, every wave of this positive change carries a downstream effect. Changes in how care is delivered reshape coverage, documentation and coding updates. New tech in provider workflows change how claims are submitted and coded. JAMA recently reported that providers utilizing AI scribes (leading to automated coding processes) are seeing a 6% increase in value delivered. Each of these shifts creates an obligation somewhere in the edit program - a policy to update, a code set to incorporate, a rule to revisit or care delivery to keep pace with.
For plans that rely on periodic review cycles to keep their edits current, every delay between policy change and edit deployment is a window where errors can build up or payments can pass through unchecked.
When edit programs fall behind, the consequences get real:
What was once a pace plans could manage has now become a growing source of errors, losses and administrative burden for plans.
Most edit programs were designed around a predictable cadence of change, one that the current policy environment has now outpaced. The standard update sequence (a policy changes, a team member identifies it, a coding expert interprets it, IT configures and tests the logic, and weeks or months later the edit deploys) was built for a slower environment. Everest Group researchers, who speak with 40 to 50 healthcare payers annually, consistently find that plans are responding to continuous change on a quarterly or annual basis, at best.
The sources driving edit changes have also multiplied. Any of the following can create an obligation for the edit library:
No single team can monitor all of these sources manually at the pace needed to keep up. “Off-cycle” edit or policy updates are especially costly, where a new code added or policy adjustment issue wouldn’t surface until months of leakage have already built up.
The concept of edit drift occurs when policy change and existing edit logic diverge far enough to matter financially and operationally. It is a condition that grows, not a one-off event - and for most programs, it shows up quietly.
The clearest way to spot drift is in trend data: spend increasing in a category without a clear driver, denial rates declining without improvements in provider billing accuracy, or dispute rates rising in a particular line of business. By the time these trends surface in a report, the underlying drift has likely been accumulating for months.
Improper payments are the most obvious sign, but operational costs become an equal threat to the plan:
Not all providers, or LOBs are treated equally. Dr. Priscilla Alfaro, a strategic advisor for health plan cost adjudication and payment accuracy, describes this as a “peanut butter” approach to editing: applying (or spreading) the same rules broadly across an entire provider population. This evenly spread distribution creates two main risks:
Tracking how a specific provider's billing patterns have evolved, what changes in their coding behavior might indicate, and whether a new pattern represents an error or a legitimate shift requires a level of granularity that manual programs struggle to sustain across a full provider network.
The plans most at risk are those operating reactively — updating their edit programs only when an audit finding, provider complaint, or visible leakage event forces the issue. In that model, the program is always addressing yesterday's problem.
The difference between edit programs that stay current and those that fall behind is rarely a technology deficit alone. It is almost always a governance oversight rooted in an absence of the structures, cadences, and decision rights that allow a program to move at the speed of change.
According to Everest Group and other industry experts, most plans are operating at Level 1 or 2. Getting to Level 3. Building toward Level 4 requires rethinking both the technology and the organizational structure around the edit program.
Governance means having the right tech, but more importantly, the right people in the room. An effective edit review brings together stakeholders beyond the clinical coding team:
Visibility and traceability are non-negotiable for a defensible program. One of the most consistent issues in edit governance is the black-box problem - deploying edits through a vendor where the plan has limited visibility into how logic was generated or why a specific claim was flagged. When a provider appeals a denial and the plan can't produce a clear, policy-backed explanation, the outcome is almost always a reversal, plus the cost of the appeal process.
Governance requires transparency for every edit decision: what policy drove it, how the logic was codified, what claims it has touched. The ability to test a proposed edit against historical claims data before deployment adds another layer of confidence. It surfaces which providers would be affected, the estimated financial impact, and likely dispute volume before the edit goes live.
Leading programs treat process, technology, skills, and data as essential levers, but ones that only work when pulled together. Investing in one without the others consistently limits results.
AI in edit programs works best when it removes the manual bottlenecks that slow human judgment down — not as a replacement for that judgment.
Automated policy monitoring and ingestion is the most immediate application. Rather than relying on teams to manually track these policy sources, AI agents can continuously monitor key code sets, policy and other sources for changes and surfacing relevant policy text for review. This shifts the human role from research to validation.
AI-assisted edit generation accelerates the time from policy change to potential edits for programs. When a new policy is published, AI can transform the unstructured text into a structured edit — identifying relevant CPT and ICD-10 codes, flagging related conditions, and surfacing the specific policy sections that support the logic. Clinical coders and policy experts still validate the output; they are not performing the initial research.
Human + AI collaboration and oversight is essential. AI-generated edit logic needs expert review — the context, nuance and application of field expertise is the final step that separates a misfired edit from one with true impact. ICD-10 codes can have clinical synonyms that don't appear in the policy text. A coding pattern that looks like an error may reflect a legitimate shift in care delivery. The reviewers who catch these nuances with their own inherent context (seasoned coders, medical directors, line-of-business experts) are what make an AI-assisted program successful. The goal is to give those reviewers better, faster inputs, not to bypass their judgment, and also use those outputs for continuous improvement of models.
For payment integrity leaders who know their edit programs are behind, the path forward can follow a consistent ramp regardless of team size or current maturity. It is a progression, and it starts with an honest look at where your program stands.
Step 1: Diagnose before you deploy. The assessment has to be organizational, not just technological. That means:
Step 2: Start small and stay focused. Scope the first AI-assisted effort to a specific category where the diagnosis identified clear exposure. A successful pilot with measurable results builds both organizational confidence and the operational muscle to expand.
Step 3: Invest in change management as seriously as technology. Payment integrity teams often describe resistance less as opposition than as uncertainty — not knowing what their role looks like when AI is handling more of the initial research and codification. Dr. Alfaro has seen success with a "see one, do one, teach one" approach. When teams can observe the process, work through it themselves, and then bring that experience to their colleagues, adoption follows. The wins from a successful pilot are the most effective tool available.
Step 4: Deploy with traceability built in. When expanding beyond the pilot, ensure the governance infrastructure is in place before scaling. Defensibility at scale requires the same rigor that was applied in the pilot.
Step 5: Treat optimization as a continuous cycle. As edits deploy and provider behavior evolves in response, new patterns will emerge. Governance needs to be designed to catch those patterns and update the program. The value of an adaptive program is that it doesn't require a new transformation initiative every time the policy environment changes.
The edit programs keeping pace today are not necessarily the biggest or best-resourced. They are the ones that have stopped treating policy change as a periodic event to respond to and started building programs designed to absorb and adapt continuously.
That shift requires AI to handle the heavy lift of monitoring, research, and initial codification that human teams cannot sustain at volume, and it requires the human-in-the loop for expert context, final touch, ongoing governance, and great data to put that AI to productive use.
Start with an honest assessment of where the program stands, prove value in a specific area, and build from there. Plans that move on this now will be better positioned as the pace of change continues to accelerate.
This subject was recently covered in a virtual panel, featuring experts from Shift, Everest Group and other payment integrity market leaders. For additional insight and first-hand experience, watch the replay here.
Sources: