Quality Intelligence
The patients who disappear are the ones your reporting cannot see.
Behavioral health outcomes depend almost entirely on whether someone comes back. Most reporting counts the visits that happened, which means the patients who stopped attending are precisely the ones it is worst at showing you.
Works from a scheduling and assessment extract you already produce. No EHR integration required to start.
The problem
Attendance-based reporting measures the people who are still there.
This is the structural problem specific to behavioral health, and it is easy to miss. Reports are built from encounters. A patient who attends twice and never returns contributes two encounters and then quietly leaves the denominator of everything you look at.
The result is that engagement failure is close to invisible in standard reporting. Average PHQ-9 improvement looks reasonable, because it is calculated over patients still in treatment — who are, by definition, the ones it is working for. Follow-up compliance looks acceptable in aggregate while one clinician's panel or one referral source is losing people systematically.
For organizations under FUH, FUM or IET measures, that gap is also a financial problem. Those measures have tight windows, and by the time a quarterly report shows a miss the window has closed on every patient in it.
- Outcome improvement is reported over patients still engaged, which flatters the number
- Nobody can list the patients who stopped attending this month and were never followed up
- Follow-up measure performance is known after the reporting period, when the window has closed
- No-show and cancellation patterns are tracked as a rate, not attributed to clinician, slot or referral source
- Assessment scores are collected diligently and analysed rarely
- Telehealth and in-person cohorts are compared informally, if at all
Capabilities
What Vizier surfaces in behavioral health data
Vizier evaluates engagement and outcome patterns continuously, including for the patients who have stopped appearing in your encounter data.
Disengagement, early
Patients whose attendance pattern has broken — surfaced as the gap opens rather than after they have been absent long enough to fall out of every report.
Follow-up window risk
FUH, FUM and IET obligations approaching or breaching their windows, while there is still time to make contact rather than after the measure is lost.
Outcome trajectory
PHQ-9, GAD-7 and other assessment scores moving within cohorts and caseloads, including where a caseload's average improvement is being propped up by attrition.
No-show concentration
Where non-attendance clusters — clinician, time of day, appointment type, referral source, modality — rather than a single practice-level rate.
Caseload variation
Differences in engagement and outcome between clinicians and sites, presented as a specific gap to investigate rather than a league table.
Revenue attached to engagement
The billing consequence of missed follow-up and disengagement, including measure-linked and programme revenue that depends on contact.
What a finding looks like
Nobody was failing to schedule these patients. They were being scheduled — just outside the window that the measure, and the clinical evidence, cares about.
That distinction is almost impossible to see in a completion rate, and obvious once the finding separates scheduled from scheduled-in-time and points at the two weekdays where capacity moved.
Seven-day follow-up after discharge has fallen sharply for one referral pathway.
- Seven-day follow-up completion for this pathway fell from 71% to 44% over three months.
- Other referral pathways are unchanged over the same period.
- Affected patients are being scheduled, but first appointments now fall outside the seven-day window.
- The shift coincides with a change in available intake slots on two weekdays.
Review intake slot availability against discharge volume for this referring facility, focusing on the two weekdays where capacity changed.
The reporting gap
Why standard reporting misses disengagement
Encounter-based reporting has a blind spot that matters more in behavioral health than almost anywhere else: it can only describe people who showed up.
- Patients who disengage leave the denominator, so outcomes are measured over a self-selecting group.
- Averages over active patients improve when the people doing worst stop attending.
- Follow-up measures are reported after their windows close, when nothing can be done.
- No-show rates are tracked at practice level, which hides where non-attendance concentrates.
- Assessment scores are collected for clinical use and rarely analysed across a caseload.
- The financial consequence of disengagement sits in a different report from the clinical one.
What this replaces
This replaces the outreach list somebody builds by hand
Most behavioral health organizations already do some version of this manually. Someone runs a report, cross-references it against the schedule, and builds a call list of patients who look like they have dropped off.
It is good work and it does not scale. It happens when someone has time, it covers the cohorts that person thought to check, and it is reconstructed from scratch each time because it was never a system.
Vizier does that continuously across every cohort, pathway and caseload, and surfaces the ones where the pattern actually broke.
- The manual disengagement list built by cross-referencing reports against the schedule
- Quarterly follow-up measure review, replaced by knowing while the window is open
- Ad-hoc analysis of no-show patterns when a clinic gets concerned about utilisation
- Assessment score data collected diligently and analysed only when someone asks
- Reconstructing the same caseload comparison every time leadership asks about variation
Who this is for
One engagement problem. Three teams who need to know.
Population Health / Quality
Which patients are disengaging, and which follow-up windows are still open?
- 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
COO / Operations
Where is capacity or scheduling causing us to miss patients we scheduled?
- Throughput and patient flow constraints identified by location and service line
- Performance variability between sites made visible rather than averaged away
- Operational deterioration flagged while it is still a trend, not a crisis
CFO / Finance
What is disengagement costing in measure performance and programme revenue?
- Revenue leakage surfaced with the exposure quantified
- Reimbursement and payer performance movement, early
- Financial impact ranked so the biggest number gets attention first
Getting your data in
Start from your scheduling and assessment data.
Behavioral health data is often more accessible than people expect, because scheduling and assessment records are usually already exportable.
- A scheduling and attendance export — enough on its own to surface disengagement and no-show concentration.
- Assessment score data (PHQ-9, GAD-7 and similar) for outcome trajectory.
- A billing or claims extract, to connect engagement to revenue and measure performance.
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 does 42 CFR Part 2 affect this?
Substance use disorder records carry protections beyond HIPAA, and any engagement involving them needs to be scoped with your compliance team from the outset. In practice a great deal of behavioral health operational analysis — attendance patterns, scheduling capacity, follow-up window performance — can be done without touching Part 2 protected content at all, and that is usually where we would start. It is a conversation to have early rather than an obstacle.
Do you need patient-level data?
For engagement and follow-up work, yes at some level — identifying who has disengaged is inherently patient-level. For pattern analysis such as no-show concentration, caseload variation and capacity effects, pseudonymised data is often sufficient. We would rather scope a first phase around what your governance process can approve quickly than stall on the fullest possible dataset.
Which measures does this cover?
The follow-up and engagement measures behavioral health organizations are typically held to — FUH, FUM, IET, AMM and similar — plus assessment-based outcome tracking using PHQ-9, GAD-7 and comparable instruments. The value is knowing where you stand while the windows are open rather than after the reporting period.
We are a small practice. Is this relevant?
The engagement problem is arguably worse at smaller scale, because there is no analyst at all and the manual list-building falls to a clinician or practice manager. The starting data is usually simpler too. The Practice tier exists for exactly this situation.
Can it tell us why patients are disengaging?
It can tell you where disengagement concentrates — which clinician, pathway, slot type, modality or referral source — and what changed around the point the pattern shifted. That is usually enough to narrow it to a specific operational cause, as in the finding above. The clinical interpretation stays with your team.
Does it work with telehealth and in-person mixed caseloads?
Yes, and comparing the two is often where the useful findings are. Modality is one of the dimensions engagement patterns most reliably differ across, and most organizations have a strong intuition about it and very little evidence.
Related reading
See who your reporting is not showing you.
Bring a scheduling export. Thirty minutes is usually enough to find a disengagement pattern nobody had named.
Start with the data you already have. Connect your EHR when you’re ready.