The following is a guest article by Steve Sutherland, SVP Information Systems at CERIS
Payment integrity often lives at the end of the claims line, with teams measuring success by how much they can win back after payment. While recoveries matter, most payer and program leaders know the hidden cost of that model.
That is why more organizations are now “shifting upstream” in payment integrity. This trend of shifting upstream or left means being proactive and moving detection, validation, and decisioning upstream, closer to claim intake and adjudication. The goal here is to catch issues earlier in the claim lifecycle, before funds go out the door, and when problems can be clarified, corrected, or resolved with less disruption.
This change touches the full healthcare ecosystem. Large health systems and hospitals feel the strain when recoupments disrupt revenue cycle planning. Group practices and solo practitioners experience the drag of repeated record requests and appeal cycles. Government agencies face heightened scrutiny and pressure to demonstrate program integrity. Health IT companies, consultants, and associations are under the impression that integrity initiatives must balance accuracy, access, and administrative burden.
Shifting upstream is gaining ground because it supports a better process, not just tougher policing.
Why Prepay Strategies are Speeding Up Now
Claims keep rising in both volume and complexity. At the same time, many plans and programs run short on staff, and post-pay recovery work can be costly and hard to scale. Even when recoveries are successful, they can come with downstream costs that are harder to quantify but easy to recognize, including strained provider relationships, more appeals, and more time spent resolving disputes than preventing them.
Prepay prevention reduces that churn. The most successful programs focus on clear rules and tight scope, and target common failure points. They also route those claims through steps that teams can explain and providers can resolve.
Prepay work also helps payer information technology and claims teams measure results faster. Intervention is happening in a shorter window, making it easier to track outcomes, compare changes, and refine edits.
What Prepay Prevention Looks Like in Practice
Shifting upstream shows up in a few core moves across the industry, and the following steps are quickly becoming standard as programs mature.
One step is clinical validation, which helps confirm that a billed service aligns with clinical documentation and applicable policy. This often involves reviewing whether the medical record supports the level of care, medical necessity, or clinical appropriateness. Another intervention is coding checks, where payers identify inconsistencies such as incompatible code combinations, unbundling risk, upcoding indicators, and mismatches between codes and documented services. A third is documentation review, which catches missing or unclear details before payment. That timing matters as providers can often respond faster before a claim turns into a formal dispute.
The operational upside is clear: providers still have context, and claims staff can resolve questions without triggering a long recovery cycle. From a technology view, payers can embed edits and workflows where they work best, which leads to clearer routing, stronger audit trails, and more consistent decisions.
Where AI is Making a Real Impact
Artificial intelligence can help payment integrity teams move faster and stay consistent. Additionally, the technology works best when teams pair it with tight governance and human review. AI can help sort incoming claims by risk and provide better triage. It can use signals like pattern fit, dollar risk, and confidence scores to cut through the noise. AI also keeps human review focused on the claims that matter most.
AI also provides faster record review. Modern tools can pull key facts from records and highlight the right sections. Some tools can also draft short summaries for reviewers, which reduces the time spent hunting for details across long notes. Reviewers are moving faster when using AI without lowering the bar.
Lastly, AI can also help with pattern detection at scale, surfacing emerging anomalies across procedures, sites of service, or provider groups. This is particularly useful for identifying where an edit may need to be tuned, where education could prevent repeated errors, or where a new billing pattern is creating avoidable leakage.
The key point is trust. Artificial intelligence helps most when it supports decisions and leaves a clear trail. If teams cannot explain why a claim was flagged, they should not rely on the output.
Where Human Expertise Stays Essential
Even as automation improves, payment integrity still depends on expert judgment. Complex clinical scenarios, nuanced coding situations, policy exceptions, and context-specific documentation issues do not always fit neatly into rules or model predictions.
Humans also protect quality, calibrate rules, and handle escalations. Without those steps, teams risk uneven decisions and weak rationales. Provider trust drops fast when outcomes feel unsystematic.
Clinical reviewers and coding experts add the interpretive layer that keeps decisions fair. They can tell the difference between a true billing issue and a chart that needs one more detail. They also feed what they learn back into edits and models. With mature programs treating this as a loop and automation handling volume, experts are able to handle nuance and oversight.
Earlier Intervention Improves Transparency and Reduces Provider Friction
When a payer flags an issue before payment, the message can be more direct and the policy cite can be clear. The provider can respond while the facts are still easy to confirm. Early intervention also improves internal transparency, because prepay workflows can be instrumented to show exactly why a claim was flagged, what information was reviewed, what was requested, and how the final decision was reached.
For payer information technology teams, that trace supports audit needs and strong controls. For providers, it reduces the feeling that rules change after the fact.
What Shifting Upstream Means for Payer Information Technology and Claims Teams
Shifting upstream is ultimately a modernization effort. It requires payer IT and claims operations teams to treat payment integrity as a lifecycle capability, supported by workflow design, data quality, and disciplined governance.
Teams can make the change with a few practical moves:
- Precision Before Scale – Pick a small set of high-impact use cases and make sure they are defensible and workable in prepay; measure results, then expand
- Tie Every Decision to Evidence – Each intervention should produce a clear rationale and a policy link; it should also point to a consistent path to resolve the issue, and explainability turns automation into trust
- Use Artificial Intelligence where it is Strong – Use AI for routing, record search, summarization, and trend detection; keep expert review for clinical judgment, coding nuance, and quality checks
- Measure Success Beyond Recoveries – Track avoided improper payments, cycle times, touch rates, appeal outcomes, provider escalations, and cost-to-manage, because those indicators reveal whether shifting upstream is improving integrity through transparency
Across healthcare, the goal is to improve billing accuracy by strengthening transparency and integrity. By preventing errors early, organizations reduce waste, minimize rework, and lower friction for everyone involved. The result is a clearer, more consistent, and fairer billing and claims experience.
The following is a guest article by Ashutosh Kavathekar, Global Head – Healthcare at 