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Healthcare's Other Data Problem: Why Patient and Member Records Still Don't Talk to Each Other
Mike Spence
Healthcare's Other Data Problem: Why Patient and Member Records Still Don't Talk to Each Other
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In most healthcare and health insurance organizations, the clinical team, the billing team, and the member services team are each looking at a version of the same person that the others cannot see at all. The care team knows the diagnosis and treatment history. The billing team knows the claim status and payment history. Member services knows the complaint history and communication preferences.
McKinsey research on administrative simplification puts a real number on the cost of this fragmentation: up to $265 billion in unnecessary U.S. healthcare administrative spending, much of it tied to exactly this kind of disconnected, duplicative data handling. This is a problem that predates any AI conversation by years, sitting underneath most of the operational friction staff deal with daily.
In This Article
Why This Silo Is Different From a Typical CRM Problem
Most industries deal with data silos between a sales team and a service team, which is disruptive but relatively contained. Healthcare deals with silos between clinical, financial, and member-facing systems that were built under different regulatory requirements, on different implementation timelines, often by entirely different vendors with no shared roadmap.
Why the stakes are higher: a missed connection is not just a lost cross-sell opportunity the way it might be elsewhere. It can mean a missed care gap, a duplicate outreach effort that wastes staff time and annoys the member, or a person calling in frustrated because nobody on the phone can see what the last three departments already told them.
Where This Shows Up Operationally
Care coordinators re-key information that already exists in another system because they cannot see it from where they sit and have no efficient way to pull it. Member services representatives have to transfer calls or put people on hold repeatedly to track down basic account history that should be visible in seconds.
Prior authorization specialists work from incomplete clinical context because the full record lives somewhere they don't have direct access to, which slows down every request and increases the odds of a denial that gets appealed and reworked later. None of this is any individual's fault, but the cumulative operational drag adds up fast, and it lands hardest on organizations already managing thin margins and persistent staffing shortages.
What a Unified Relationship Record Changes
A unified patient or member record inside Dynamics 365 does not replace the EHR or the claims system, and it should not try to. It sits alongside them and pulls together the relationship history, care gaps, communication preferences, and outstanding needs into one view that any team touching that person can see, subject to the access controls their specific role requires under HIPAA.
That single structural change removes a meaningful share of the re-keying, call transferring, and context-hunting that eats staff time every single day, freeing up hours that were previously spent reconstructing information rather than acting on it.
Where AI Extends the Value Once Records Are Connected
Once the record is genuinely unified, AI has a foundation to work from that actually holds up under scrutiny. Care gap outreach can be prioritized and drafted at scale rather than handled one member at a time. Member communications can be personalized without a staff member manually pulling context together first for every single outreach.
None of this is safe or effective without the governance and HIPAA-aligned data handling model underneath it, which is exactly the sequencing problem organizations run into when they try to deploy AI before the data foundation is unified and end up with tools nobody fully trusts.
Why Sequencing Matters Here Specifically
Healthcare organizations face more pressure than most to move fast on AI, given the margin environment and the staffing shortages most are working under, and that pressure sometimes pushes teams to skip straight to a pilot before the underlying data problem is addressed.
The organizations that get durable results tend to resist that pressure and fix the record first, even when it takes longer to reach a visible AI deliverable. A pilot layered on top of fragmented data tends to produce inconsistent or untrustworthy output, which damages staff confidence in the tool and makes the next attempt harder to get buy-in for, not easier.
Key Takeaways
- Clinical, billing, and member services teams often see three different versions of the same person, with no shared view.
- McKinsey estimates up to $265 billion in unnecessary U.S. healthcare administrative spending, much of it tied to disconnected data handling.
- A unified record sits alongside the EHR and claims system rather than replacing them, respecting HIPAA access controls by role.
- AI only produces trustworthy output once the underlying record is genuinely connected; sequencing matters more than speed.
Where to Go From Here
If your care, billing, and member-facing teams are working from three different pictures of the same person, closing that gap is the higher-leverage fix to make before launching any AI initiative on top of it. TrellisPoint's AI Value Engine starts with exactly that kind of readiness assessment, and the D365 Accelerator builds the unified record it ultimately depends on.
Start With a Readiness Assessment
Talk through what a connected view would mean for your organization and patient or member population, and which team would benefit most from being the starting point.
Contact TrellisPointSources
- How to Reduce Administrative Spending in US Healthcare - McKinsey
- AI Value Engine - TrellisPoint
