Why clear, explainable decision-making matters for modern payment integrity teams
What gets lost when decision logic isn’t visible
Transparency in payment integrity is often discussed at a high level, but the impact is usually felt in small, day-to-day moments.
Let’s run an example. A claim is flagged based on current policy and detection logic. When a provider appeals, however, the explanation is difficult to pull together. The reasoning may be split across policy documents, plan or vendor rules, internal notes - throw black box AI in there? It might not be visible at all. Each piece might exist, but not in a single, explainable view.
Over time, this creates friction. Providers may have lengthy payment times, or inaccurate or unjustifiable denials creating frustrating back-and-forth processes. Payer teams may struggle to resolve questions quickly, or succinctly respond to appeals with proper reasoning. That reputation of a trusted, reliable payer starts to erode, while scrambling work loads stack up.
Yes, initial accuracy is critical, but unexplainable decisions create equally critical priorities. They are about whether decisions are trusted and understandable.
When decisions can’t be explained, value breaks down
These small day-to-day moments lacking transparency create bottlenecks and issues that compound over time. Each unclear decision adds review time, increases escalation, and creates uncertainty around whether claim edits or detection logic is performing as intended.
A claim edit may identify meaningful financial exposure, but without clarity into what drives the decision, confidence breaks and that potential can’t be realized. Appeals increase. Internal questions multiply. Teams begin to adjust or disable logic, not always because it is wrong, but because it is hard to manage - allowing losses to pass through.
This pattern shows up in a handful of ways:
- Edits are loosened or turned off instead of refined
- Appeals create workloads without clear insight into root causes
- Reported savings become harder to defend and sustain
Explainable decision-making allows teams to respond with confidence. With visibility into how claims edits trigger and why decisions occur, teams can improve accuracy rather than turn off edits or turn away from a savings opportunity.
Shift’s Head of Value Engineering and Customer Success, Jesse Montgomery adds “As policy and care delivery change quickly, claims editing and other payment integrity decisions need clear context. When an edit is tied directly to the relevant policy language or claim line, teams have the detail they need for trusted, stronger decisions. We’ve seen that transparency reduce provider friction and overall appeal timing because the reasoning behind the decision is right there.”
With experience leading both payer teams and tech solutions, Jesse adds, “AI delivers the most value when decisions are explainable. Clear, policy-based outcomes speed up appeals and strengthen trust between payers and providers.”
Breaking black boxes = breaking down silos
Clear decision-making affects more than outcomes. It changes how teams collaborate and information flows.
When payment integrity actions are supported by understandable policy references and claim context, provider conversations tend to be more productive. Discussions focus on alignment to billing rules rather than disputes over issues like suspicious intent.
Internally, transparency gets teams such as customer service, legal, compliance and other teams on the same page - united around the same context or detail. Decisions are easier to support, challenge, or adjust early when needed.
Transparency may not eliminate disagreement, but it reduces unnecessary friction and shortens the path to resolution.
What a transparent, AI-Native model enables
Achieving transparency at scale requires more than just better reporting. It requires systems designed to make reasoning visible at the core. With the growth of AI-powered payment integrity solutions coming to market and momentum of AI adoption, transparency plays a critical role in deploying these solutions responsibly, at scale, and in an impactful way.
In a transparent, AI-native payment integrity model, teams can:
- See which policy or guideline language informed a decision
- Understand how codes and services were evaluated together
- Test and refine claims edits or detection logic before it impacts providers
- Trace the output’s steps and data analyzed to explain a decision
- Identify potential provider impact earlier in the process
Instead of reacting to appeal trends after deployment, teams gain the ability to adjust, prioritize or optimize claims edits proactively and with context.
In an industry where "integrity" is in every job description, transparency is key. This is how transparency gets infused to smooth and strengthen those day-to-day moments, reduce provider friction and not drive teams to pull together after-the-fact explanations.
To learn more about how Shift prioritzes transparency in payment integrity decisions, or the benefits of explainable AI - schedule some time with our team.