Population Health Intelligence
Stop discovering performance gaps after they have already widened.
Population health reporting tells you where your cohorts stand. It rarely tells you which one started moving in March, at which site, or which intervention the evidence actually supports.
Starts from a registry or measure extract you already produce. No data aggregation programme first.
The problem
The quarterly review is where you find out. The drift started two quarters ago.
Every organization managing a population has reporting on it. Measure performance, cohort sizes, care gap counts, risk tiers — produced on a quarterly or monthly cadence, reviewed in a meeting, filed.
The structural problem is that population health moves slowly and reporting is aggregated. A control rate drifting two points a quarter at one clinic disappears inside a system-wide average that barely moves. By the time the aggregate is bad enough to notice, the cohort has been deteriorating for three quarters and the intervention that would have been cheap in month two is now a recovery programme.
The same applies to variation. Most organizations know their sites perform differently. Very few can say which specific site, cohort and measure combination is diverging fastest right now — which is the only version of that knowledge you can act on.
- Care gap lists are produced, distributed, and worked down by whoever has capacity rather than by impact
- Measure performance is reviewed quarterly and investigated annually
- A site-level problem is invisible until it is large enough to move the system average
- Nobody can say which cohort is deteriorating fastest, only which is currently worst
- Risk stratification runs on a schedule, so a patient who became high-risk in week two waits until the next run
- Programme decisions are argued from conviction because the evidence would take an analyst a fortnight
Capabilities
What Vizier surfaces in population data
Vizier evaluates cohort performance continuously and raises what moved — with the affected population identified and the consequence quantified where the evidence supports it.
Cohort deterioration
A measure drifting within a specific cohort, caught while the movement is still small and before the system-level average reflects it.
Variation between sites and populations
Where performance diverges across locations, providers, payer segments or deprivation groups — expressed as the specific gap rather than a spread.
Care gap concentration
Not just how many gaps exist, but where they cluster and which cohorts carry enough combined clinical and financial weight to be worked first.
Rising risk, earlier
Patients whose trajectory is changing, surfaced as the pattern emerges rather than at the next scheduled stratification run.
Benchmark position
How a cohort compares against a meaningful peer group where one exists — and an honest signal when the comparison is not sound enough to rely on.
Programme prioritization
Which intervention the available evidence supports, ranked by expected impact, so limited care management capacity goes where it changes outcomes.
What a finding looks like
The system-level dashboard for this organization looked fine all year. The average moved less than a point, which is exactly what an average does when one part of a population deteriorates and the rest is stable.
The finding names the two sites, the size of the affected cohort, and the pattern underneath it — patients who have not been seen in nine months. That is a recall problem, not a clinical one, and it is fixable this quarter.
A1C poor-control is rising in one clinic while the system average holds steady.
- Poor-control rate at two sites rose from 21% to 29% over four quarters.
- The system-wide rate moved less than a point, so the aggregate view shows nothing.
- The affected cohort skews heavily toward patients with no visit in the last nine months.
- Medication adherence data shows the same divergence at the same two sites.
Review recall and follow-up scheduling at the two affected sites, starting with diabetes patients not seen in nine months.
The reporting gap
Why cohort reporting hides the thing you need to see
Population health reporting is built to describe a population's current state. Detecting change within it is a different problem, and aggregation works directly against it.
- Averages absorb divergence — one site deteriorating and another improving nets to nothing.
- Point-in-time reporting shows the level, not the trajectory, so slow drift looks like stability.
- Quarterly cadence means a trend runs for months before anyone sees a second data point.
- Care gap lists are ranked by count, not by impact, so effort spreads evenly across unequal opportunities.
- Scheduled risk stratification means new risk waits for the next run.
- Cross-domain signals — adherence, utilisation, attendance — sit in separate reports and are rarely read together.
What this replaces
This replaces the analysis nobody has time to run
Most population health teams know their reporting is describing rather than detecting. The work to fix that — cutting each measure by site, cohort and trajectory every month — is genuinely large, and it competes with care management, quality submission and everything else the same small team owns.
So it happens annually, or when something has gone visibly wrong, or as a consulting engagement that produces a snapshot which is stale within two quarters.
Vizier runs that analysis continuously across every cohort and measure combination, and raises only what moved. Your team's time goes into the intervention rather than into finding out whether one is needed.
- Monthly measure-by-site cuts produced by hand to see whether anything moved
- The annual population health deep-dive, and the consulting spend attached to it
- Care gap lists ranked by count because ranking by impact would take an analyst a week
- Manual review of stratification output to find who newly became high risk
- Waiting for the quarterly review to discover a site-level problem
- Programme business cases argued from conviction because the evidence was too slow to assemble
Who this is for
One drifting cohort. Three different decisions.
Population Health / Quality
Which cohorts are drifting, and where does intervention actually pay?
- Care gaps and rising-risk cohorts prioritized by impact
- Measure performance variation against peer benchmarks
- Programs ranked by what the evidence says will actually move outcomes
CFO / Finance
Which population health programmes are producing a return, and which are absorbing capacity?
- Revenue leakage surfaced with the exposure quantified
- Reimbursement and payer performance movement, early
- Financial impact ranked so the biggest number gets attention first
Analytics / Data
How do we cover every cohort and measure without running the cuts by hand every month?
- Consistent definitions so two leaders asking the same question get the same answer
- Self-service investigation that does not generate another ticket queue
- Governance, access control and audit logging that survive review
Getting your data in
Start from a registry extract you already produce.
Population health is usually already extracted for quality submission or contract reporting, which means the starting data exists before any integration work begins.
- A quality measure or registry extract you already submit.
- A claims or encounter file, for utilisation and attribution context.
- A care gap or risk stratification export from your existing population health tooling.
01
Upload
CSV, Excel, or an export you already produce. Drop it in and Vizier reads it. This is where most organizations start, and it is enough to see real findings against your own numbers.
02
Scheduled
A recurring feed over secure transfer, on whatever cadence your team already runs. No one re-uploads anything by hand, and nothing about your source systems has to change.
03
Connected
Direct read-only connectivity to your EHR or source systems via FHIR R4, HL7 v2, or vendor APIs. Vizier reads; it never writes back.
Connect your EHR when you’re ready. See supported systems.
Security and governance
The page your CIO will ask for
Security questions get answered before a demo, not after procurement stalls.
HIPAA compliant
PHI handled under HIPAA Security Rule safeguards.
BAA included
Executed within one business day, on every plan.
Encrypted throughout
AES-256 at rest, TLS 1.3 in transit.
Read-only access
Vizier reads from source systems. It never writes back.
Role-based access control
Scoped permissions with SSO available.
Audit logging
Every query logged with account, timestamp and result size.
Tenant isolation
Your data is segregated from every other customer's.
SOC 2 Type II audit underway
Not yet certified. Report available under NDA on completion.
FAQ
Questions buyers ask
How is this different from our population health platform?
Most population health platforms are built around workflow — stratifying a population, generating lists, and supporting care management against them. That is necessary work and Vizier does not replace it. What those platforms generally do not do is watch continuously for change within cohorts and tell you which movement deserves attention this week. If your platform produces excellent lists and your team is still finding out about drift at the quarterly review, that is the gap.
Do you need claims data?
It helps but it is not required to start. Clinical measure and registry data is enough to surface cohort deterioration, care gap concentration and site variation. Claims add utilisation, attribution and cost context, which makes prioritization sharper. Most engagements begin with what is already extracted and add sources as they become available.
Can it work across an ACO or a whole system?
Yes, and variation between constituent organizations is usually where the most actionable findings are. A system-level average is the single most effective way to hide a site-level problem, so multi-site and multi-practice environments tend to produce more findings, not fewer.
How does the benchmarking work?
Where a meaningful peer comparison exists, findings include the benchmark position. Where it does not — because the cohort is small, the population is unusual, or the comparison group is not sound — Vizier says so rather than presenting a comparison you should not rely on. The primary comparison is always your population against its own history.
Will this help with quality measure submission?
It will show you measure performance, where it is drifting and which cohorts are driving it, in time to influence the result. It is not a submission tool and does not replace your registry or submission vendor. The value is knowing in month four that a measure is heading the wrong way, rather than at submission.
How long until we see something?
Usually the first working session, because the starting extract already exists. The finding above — a two-site drift hidden inside a stable system average — is the kind of thing that tends to surface early, because it is exactly what aggregate reporting is structurally bad at showing.
Related reading
See what Vizier finds in your population data.
Bring a registry or measure extract. Thirty minutes is usually enough to surface something the quarterly review has been missing.
Start with the data you already have. Connect your EHR when you’re ready.