Supplemental Data Impact Analysis
Every feed should earn its place.
A per-feed answer to the question nobody has the data to settle: is this one worth keeping? Contribution traced to source records, cost stated honestly, audit exposure named.
Supplemental data is the fastest way to move a quality rate and the easiest place to accumulate cost that nobody measures. Plans add a registry, a lab feed, a health information exchange connection, or a vendor abstraction file, and each one arrives with an integration cost, an ongoing validation burden, and an audit exposure. Very few plans can say which of those feeds is carrying its own weight. This assessment answers that per feed, with the contribution traced to source records rather than estimated.
The problem
Supplemental feeds are added under deadline and reviewed almost never. The decision to add one is usually made on a vendor claim about lift, and the decision to keep it is made by nobody at all: it simply persists in the pipeline. That is how a plan ends up validating a feed that duplicates data it already has, while a feed that genuinely moves a bubble measure gets no engineering attention.
What the analysis produces
Per-feed rate contribution
how much each supplemental source adds to numerator capture, measure by measure, traced to the records that made the difference.
Overlap analysis
which feeds are supplying events already present in claims or in another feed, which is where most unmeasured cost sits.
Provenance and audit exposure
whether each feed's contribution can be traced to a source record with a stated collection method, and what happens under review if it cannot.
Total cost of a feed
integration, ongoing validation, reconciliation effort, and the engineering attention it consumes. Named, not estimated for you.
Bubble measure sensitivity
which feeds affect measures close to a cut point, where a small contribution has outsized weighted impact.
Keep, fix, or retire recommendation
a written call per feed with the reasoning attached, so the decision survives the person who made it.
What it looks like in practice
A plan runs several supplemental feeds and is being asked to cut analytics spend. Rather than cutting by contract size, we measure what each feed contributes to numerator capture, what it costs to keep validated, and whether its provenance chain would hold up under review. Some feeds turn out to be load bearing. Some turn out to be duplicating claims data already in hand.
We will tell you to keep a feed we did not build
The recommendation is per feed and it is not a sales instrument. If a vendor abstraction file is doing real work, we say so, and we say it in writing. An assessment that always concludes you need more of what the assessor sells is not an assessment.
Contribution is measured, not modeled
Rate contribution is computed from your own records, not projected from a vendor's published lift figure. The distinction matters because a published figure describes some other population. Where we cannot measure a contribution we say the number is not available rather than supplying an estimate with a confident tone.
Who it is for
Stop rebuilding the same model every year. Let AI handle the routine ad-hoc requests so your team can build the predictive future instead of being the report desk, on a maintained registry with versioned specifications you can inspect. You are the person a security review runs through, so the first product asks for nothing that needs one.
You know what the strategy should be. You are waiting on a ticket for the numbers, and the two things you fear most are a half star drop and a failed audit. Walk into the executive suite with a ranked, dollar weighted plan at the start of the measurement year, instead of a year end surprise.
Where the findings land
Feeds that need provenance work are exactly what Provenance First Data Architecture addresses, and feeds worth keeping usually need the ongoing monitoring in Managed Clinical Analytics.
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