Patients slip through the gaps between events. Vivantal reads the whole record and surfaces what no single chart shows — open loops, contradictions, and silent declines, before they become harm.
12M
US adults affected by diagnostic error each year
$0
to run — no model, no server
4
analysis lenses, one record
RECORD · P-2847READING
Jan 2023Creatinine 1.1 mg/dLacknowledged
Apr 2023Creatinine 1.4 mg/dL ↑not acked
Jun 2023Lisinopril startedactive
Sep 2023Potassium 6.4 mmol/Lcritical
Nov 2023Cardiology referralopen 8mo
Jan 2024Creatinine 1.8 mg/dL ↑↑not acked
3 open loops detected
Lisinopril continued despite critical potassium · referral never closed · renal function declining across three visits
The problem
A chart shows what happened. It can't show what didn't.
The most dangerous thing in a record is often the thing that's missing — the follow-up that never came. No standard view flags an absence. Vivantal does.
Open loops
The gap between an event and its follow-up is where patients get hurt.
An abnormal potassium nobody acknowledged. A suspicious mammogram with no biopsy. A referral placed and forgotten. Each looks fine alone. Together, they're a pattern of preventable harm.
The same gap isn't distributed evenly across patients.
Equal findings, unequal action. Follow-up happens less often for some patients than others — by race, insurance, or language — and it usually goes entirely unmeasured.
Every lens reads the same longitudinal record and surfaces a different category of risk.
01
Open-loop detection
Walks the timeline and finds follow-ups that were never closed — unacknowledged abnormal results, incomplete referrals, imaging recommendations that went nowhere, and medications started without their required monitoring.
Example finding · synthetic demo data
"Potassium critical (6.4) resulted 817 days ago — never acknowledged."
02
Equity audit
Measures how often an actionable finding actually gets closed for each patient subgroup — race, insurance, language, sex, age — and uses a significance test to separate a real disparity from noise.
Example finding · synthetic demo data
"Medicaid patients close 51% of findings vs 74% for the reference group — p < 0.001."
03
Reconciliation
Reads each record for internal contradictions fragmented care produces — dangerous drug interactions, a medication continued against a contraindicating lab, duplicated work-ups.
Example finding · synthetic demo data
"ACE inhibitor active through a critical-high potassium result."
04
Deterioration
Trends each repeatedly-measured value over time and flags patients whose trajectory is sliding toward a dangerous bound — the slow decline invisible in any single result.
Example finding · synthetic demo data
"Creatinine drifting upward across three visits — renal function declining."
0 AI
Deterministic — every finding traces to a named rule
$0
No model, no inference, no server cost
None
Data sent to us — everything runs in your browser
4
Lenses on one shared patient record
Load a cohort. Look for yourself.
80 synthetic patients, realistic gaps, every lens live. No install, no account.
Use cases
Who it's for, and what they'd do with it.
Vivantal speaks to two teams that rarely share a tool — patient safety and health equity — because both questions live in the same record.
Quality & safety
Catch the follow-ups that slipped
Run a unit's or panel's records through Vivantal to find every open loop, ranked by severity and how long it's been open — so the riskiest patients surface first for outreach.
Example finding · synthetic demo data
"Potassium critical (6.4) resulted 817 days ago — never acknowledged."
Health equity
Measure whether follow-up is fair
Audit closure rates across race, insurance, language, sex, and age. See, with a significance test, whether a subgroup's findings are acted on less often than the best-served group's.
Example finding · synthetic demo data
"Medicaid patients close 49% of findings vs 90% for the reference group — p = 0.0008."
Care managers
Build a worklist, or study the pattern
Use the ranked roster as an actionable outreach list, or use the cohort-level statistics to study where and for whom care processes break down — all from de-identified data, in the browser.
Example use
"Pull the 23 patients with unaddressed critical results into this week's safety huddle."
A worked example
One patient, one record, the gaps made visible.
Take a real-looking record: years of visits, labs, imaging, and referrals. Most of it is fine. Vivantal pulls out only the loops left open — and links each to the exact event so a clinician can confirm it in seconds.
What it flags in a single record
unacknowledged_result
A high calcium of 12.9 was resulted and never acknowledged — possible hypercalcemia left unworked.
imaging_followup_open
A mammogram read "BIRADS 4 — biopsy recommended." No biopsy is on file.
missing_med_monitoring
Amiodarone was started; it requires TSH monitoring, and no TSH has been drawn since.
referral_incomplete
A cardiology referral was placed 596 days ago and is still open — the patient may never have been seen.
How it works
Deterministic. Auditable. Yours to verify.
No model, no black box. Every finding comes from a named rule or an explicit statistic you can check by hand.
Step 01
Load a record
Use the synthetic demo cohort, build a patient by hand, or load your own already-de-identified records. Everything stays in your browser.
Step 02
Read the timeline
Vivantal assembles each patient's events into one longitudinal record — labs, imaging, referrals, medications, vitals.
Step 03
Run the lenses
Four deterministic engines scan the record for open loops, contradictions, subgroup disparities, and deterioration trends.
Step 04
Confirm & act
Each finding links to the exact event behind it, so a clinician can verify it in seconds and export a worklist.
The open-loop rules
unacknowledged_result
An abnormal or critical lab result with no documented acknowledgement in the record.
imaging_followup_open
An imaging study whose report recommends follow-up that never occurred.
referral_incomplete
A referral placed but never completed, ranked by how long it's been open.
missing_med_monitoring
A medication started without the lab monitoring it requires (e.g. warfarin without INR).
The equity method
closure rate
For each subgroup, the share of actionable findings that actually got closed.
two-proportion z-test
Compares each group to the best-performing one; surfaces gaps unlikely to be chance.
four-fifths rule
Flags any group closing at under 80% of the reference rate — a standard disparity threshold.
min-N guard
Cohorts too small for a reliable signal are reported honestly as "not enough data," never as false headlines.
●Live demo. Running on 80 synthetic patients with realistic gaps and disparities built in. Not convinced it's not cherry-picked? Hit Generate random cohort for a brand-new random set — the engine finds the gaps every time. Everything is computed in your browser; load your own de-identified records anytime.
What is this cohort’s record not telling you?
Open loops surfaced from each record — abnormal results never acknowledged, referrals never completed, medications started without their required monitoring. Sorted so the patients most likely to have fallen through the cracks rise to the top.
No cohort loaded
Patients
—
Open loops found
—
Critical, unaddressed
—
Closure rate
—
abnormal findings acted on
Patient roster
click a row to open the record
Patient
Demographics
Open-loop risk
Critical
Findings
No cohort loaded yet
Load the demo cohort, or upload your own de-identified records, to begin.
Does follow-up depend on who the patient is?
Equal findings should get equal action. This audit measures, for each subgroup, how often an actionable finding gets closed — then compares each group to the best-performing one. A gap that survives a significance test is a disparity worth investigating, not noise.
Load a cohort first
Switch to the Cohort tab and load data; the equity audit runs on the same records.
Where does this record contradict itself?
Fragmented care produces conflicts no single clinician sees: interacting medications, a drug continued against a lab that contraindicates it, duplicated work-ups. This lens reads each record and surfaces those contradictions for a pharmacist or clinician to resolve.
Who is sliding, one normal-looking result at a time?
A value that moves from normal toward danger across several visits is invisible in any single result. This lens trends each repeated measurement and flags the patients whose trajectory is heading the wrong way — before it becomes a crisis.
Research & quality-improvement tool — not a diagnostic device. Vivantal analyzes record completeness and follow-up patterns and surfaces process gaps for human review. It does not diagnose, and every finding is a transparent, rule-based flag a clinician should verify. Demo data is fully synthetic; no real patient information is used. Disparity figures are illustrative of the method, not claims about any real population.
Build a patient
Turn a few clinical facts into an analyzable record.
Add events one at a time — a lab, a referral, an imaging study, a medication. Each becomes a row. When the record is built, analyze it directly or export it to load into the cohort tools.
🔒No identifying information. This builder never asks for names, dates of birth, or medical record numbers. Each record gets a de-identified code, and you only enter demographic categories and clinical events. Everything stays in your browser — nothing is sent anywhere.
Add an event
Pick a type, fill the fields, add it to the record.
Patient (categories only)
When the event occurred.
Leave blank if no monitoring is required.
Working recordRL-0001
No events yet
Add a lab, referral, imaging study, or medication from the left to start building this record.
Date
Type
Detail
Status / flag
0 events
0 open loop(s) detected
Saved records
Trust & safety
Vivantal never compromises patient privacy.
Protecting patient data isn't a policy we promise — it's an architecture we can't violate. Here's exactly how, in plain terms, and the rules we hold ourselves to.
The one rule we never break
Protected health information is never compromised. Vivantal does not receive, store, or transmit identifiable patient data — ever. There is no server in our analysis pipeline to send data to. Everything runs in your browser, on your machine. You can't leak what you never receive, and we never receive it.
How privacy is guaranteed
Protection by architecture, not by promise.
No server, no transmission
The entire engine is JavaScript running inside your browser tab. Records you load are processed locally and never sent anywhere. Disconnect from the internet entirely and Vivantal still works.
De-identified data only
Vivantal is designed for data that's already de-identified. A separate, open converter — which runs on your machine, never ours — removes the 18 HIPAA Safe Harbor identifiers before any record reaches the app.
No tracking, no telemetry
No analytics, no trackers, no third-party scripts, and nothing stored about you or your data between sessions. Close the tab and nothing persists.
Fully auditable
Because Vivantal is deterministic and open, anyone can verify these claims: open your browser's network tab and watch zero data leave the page. Nothing hidden in a model or a server you can't inspect.
The rules we hold ourselves to
✓
We only support use on data the user is already authorized to access, under their own approval such as an IRB protocol. Vivantal grants no access to data — it only analyzes what a user may already handle.
✓
De-identification happens on the authorized user's machine, before any record reaches Vivantal — never on our side, because we have no side that touches data.
✓
We treat an automated tool as an aid, not a certification. The converter produces a candidate de-identified file plus a review report, and states clearly that the user and their IRB must confirm it meets the legal standard.
×
We never ingest identified patient data into anything our team controls. No "upload your chart and we'll clean it" — that would mean PHI touching our systems, and we will not build it.
×
We never claim more than is true. Vivantal surfaces process gaps for human review. It does not diagnose, and we say so everywhere.
How real data is handled — and why it stays safe
01
Authorized access
A clinician with lawful access to their own data, under their IRB approval.
02
Local de-identification
They run the open converter on their machine; identifiers are stripped before anything leaves.
03
IRB review
They and their IRB confirm the de-identification meets the legal standard.
04
Local analysis
The clean file is loaded into Vivantal — which also runs locally.
At no point does identifiable patient data reach the Vivantal team. We contribute software; the authorized user keeps custody of their data the entire time. That's what makes the privacy commitment true by construction, not just asserted.
Research & quality-improvement tool — not a diagnostic device. Vivantal analyzes record completeness and follow-up patterns and surfaces process gaps for human review. It does not diagnose. Use on real patient data requires the user's own lawful access, de-identification on their machine, and institutional review.
Team
Built by three people who think medicine's blind spots are an engineering problem.
Vivantal started from a simple conviction: the most preventable harm in healthcare hides in the gaps between events, and you can make those gaps visible without a black box.
Works on the open-loop ruleset and the clinical reasoning behind Vivantal's four lenses. The best first point of contact for research collaboration, IRB partnerships, and clinical validation.
Works on engineering, the equity-audit methodology, and data architecture — focused on making the analysis rigorous, reproducible, and genuinely deployable.
Works on the web application, the in-browser analysis engine, and the de-identification tooling that keeps patient data on the user's own machine.
Where this is headed
From prototype to pilot.
Vivantal today is a working, validated prototype on synthetic data. The path forward is real: validating the open-loop rules with clinicians, testing the method on de-identified institutional data under review, and adding lenses onto the same spine. The goal is a tool quality and equity teams reach for, then trust.
Contact
Meet the team.
Vivantal is built by three student developers. If you're a clinician, researcher, or institution interested in the work, the people below are the ones to reach.
Add headshot
Neeraj Movva
Developer
Works on the open-loop ruleset and the clinical reasoning behind Vivantal's four lenses, and is the best first point of contact for research collaboration, IRB partnerships, and clinical validation.
Designed the open-loop detection rules and severity model
Works on engineering, the equity-audit methodology, and data architecture — focused on making the analysis rigorous, reproducible, and genuinely deployable.
Built the equity-audit engine and significance testing
Vivantal is a research and quality-improvement tool, not a diagnostic device, and the hosted demo uses only synthetic data. We never receive, store, or transmit patient information.
Your workspace
Account
Signed in. Your settings and audit history live on this device.
Care-gap thresholds
Tune the engine to how your practice works. Changes apply immediately to the next analysis. These are preferences, not patient data — nothing here is PHI.
Referral open limitFlag a referral as high-severity after it's been open this many days.
days
Monitoring gap limitFlag a medication's missing monitoring lab as high-severity after this many days.
days
Critical escalationA value past its reference bound by more than this fraction of the normal range escalates to critical.
× range
Minimum subgroup sizeAn equity subgroup needs at least this many actionable findings before a disparity is reported.
findings
Per-lab cutoffs Custom
Override the high/low bound for a specific test — for example, flag HbA1c high at 7.0 instead of the lab's default.
Clear this device
Wipe your account, settings, and audit history from this browser. Use this on a shared or public computer when you're done. This can't be undone — you'll need your recovery phrase to get back in elsewhere.
Audit trail
A timestamped record of every audit you've run — counts only, never patient data. This is the evidence a clinic shows its insurer or regulator to prove gaps are being actively monitored.
No audits logged yet
Open the demo, load or generate a cohort, and your audit will be recorded here automatically.
Analytics Admin
Aggregate usage across all Vivantal accounts. Counts only — no patient data exists on our servers, so none can appear here.
Loading analytics…
Total users
—
Audits run
—
all time
Records processed
—
cumulative
Critical findings
—
surfaced for review
Equity audits
—
Admins
—
+1 owner
Activity over time
Audits run per day. A pulse-check on how actively the tool is being used.
By user
Per-account usage. Both admins and the owner can remove a user; only the owner can change roles.
Members Admin
Everyone with a Vivantal account. Membership and roles are non-PHI; patient data never leaves each person's own machine. Only the owner can change roles.
Generate a compliance report
Produce a branded, timestamped PDF summarizing your audit activity — the document a clinic hands to HHS or its malpractice insurer. Built entirely in your browser from metadata only.
No patient data appears in the report — counts and dates only.
Vivantal never compromises patient privacy.
Runs in your browser · no data ever leaves your machine
Vivantal
Settings
Preferences for how Vivantal looks and behaves on this device. These are saved on your machine only.
High contrastStronger text and border contrast for readability.
Reduce motionMinimize animations and transitions.
Show privacy reminderDisplay the privacy notice on first load.
Default analysis thresholds
Applied to the demo and to any record you load. Signed-in users can save custom thresholds to their account.
days
days
Privacy
Vivantal stores account info and preferences in your browser only. No patient data is ever sent anywhere.
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Sign in
Your clinical thresholds and audit history, on every device you use.
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