For Health Systems · Medical Groups · Value-Based Practices

Your physicians have 15 minutes.
Your patients need 365 days.

Allvi is the between-visit care platform for chronic conditions that disproportionately affect women — built to extend your care team, not replace it. AI-powered daily support, structured longitudinal data, and zero change to existing clinical workflows.

We are currently accepting a limited number of health system pilot partners.

12×
More touchpoints than
standard care
0
Changes to existing
clinical workflows
48hr
From referral to
patient onboarded
Standard care — data available to physician
2 data points per quarter
With Allvi — structured data generated daily
Daily structured data · trigger patterns identified
🔍 AI Insight — Day 10
Stress is the primary driver of this patient's symptom flares. Sleep has minimal independent impact. Management protocol updated accordingly.
70%
Improvement in energy score — from 5.0 to 8.5 out of 10 (all-time high at week 8)
83pp
Reduction in hair loss frequency — a primary symptom of unmanaged thyroid disease
TSH
Normalised 4.48 → 2.13 mIU/L — Quest Diagnostics verified laboratory result
100%
Daily tracking compliance — every day, over the full monitoring period
The Chronic Care Gap

This is not a new problem.
It is a structural one.

Women with thyroid disease, PCOS, endometriosis, and autoimmune conditions are your highest-frequency, most complex chronic patients. They are diagnosed, medicated, and left to manage alone between appointments.

15 min
The average specialist visit for a chronic hormonal condition — every 3 to 6 months. The entirety of structured clinical contact.
51%
Of women report feeling dismissed by their healthcare provider — a signal of unmet complexity at the point of care.
KFF Women's Health Survey, 2024
$17k+
Average annual cost of a patient with multiple chronic conditions — driven by unplanned utilisation and fragmented care.
MEPS / American Journal of Managed Care
  • 01 Physicians make decisions from a snapshot. One appointment every 3–6 months cannot reveal the pattern of triggers driving a patient's symptoms over time.
  • 02 Patients arrive unprepared. They reconstruct months of overlapping, shifting symptoms from memory — compressing complex patterns into a 15-minute window.
  • 03 The between-visit burden falls on the system. Unsupported patients generate unnecessary urgent care visits, unplanned specialist referrals, and avoidable ER presentations.
Data available to physician — 90 days
WITHOUT ALLVI
All symptoms
Appt 1
Appt 2
no data
WITH ALLVI — continuous structured data
Fatigue
Improving steadily →
Anxiety
Correlated with stress
Joint pain
Rapid response wk 2
Sleep
Low trigger weight

Pattern identified: Stress is the primary symptom driver. Sleep has negligible independent impact — a finding that fundamentally changed the management protocol and could not emerge from a single appointment.

How Allvi Works

Three connected mechanisms.
One result: better-informed clinical care.

Allvi operates as an extension of your care team — not a competing service. No change to existing workflows. Nothing new for physicians to learn.

01

Continuous Personalised Data Collection

Every daily interaction generates a structured data point across multiple symptom dimensions simultaneously. Over weeks of continuous tracking, that is a longitudinal dataset that enables pattern recognition no single appointment could produce.

In one case, Allvi identified that stress was the overwhelmingly primary trigger for a patient's flares — while sleep, the assumed driver, had almost no independent impact. That finding changed her entire management protocol.
Real-World Evidence Generation
02

Better-Prepared Patients at Every Visit

Patients arrive with structured longitudinal data — not a vague recollection of how they have been feeling. Symptom complexity is already unpacked. Physicians make faster, better-grounded decisions in the time they have.

Chronic patients typically spend 3–5 minutes of a 15-minute appointment reconstructing recent symptom history. Allvi delivers that summary before the physician walks in.
Clinical Decision Support
03

Reduced Between-Visit System Burden

Continuously managed patients generate fewer urgent care visits, fewer unplanned specialist referrals, and fewer ER presentations. Allvi absorbs the between-visit demand that currently falls on overstretched clinical teams — without requiring anything from them.

The goal is not to replace clinical interactions — it is to make them count, by ensuring patients only contact the system when there is something meaningful to address.
Utilisation Management
Digital, AI & Analytics

AI finds the signal.
Clinicians act on it.

Allvi's technology layer operates continuously between appointments — ingesting patient-generated data, identifying patterns, and delivering structured outputs that make every clinical interaction more effective. All clinical decisions remain with the physician.

Patient Layer · Daily Input

Continuous Data Collection

Structured, daily. Across multiple symptom dimensions simultaneously.

📊
Symptom Tracking
Fatigue, pain, brain fog, mood — scored and timestamped daily
Daily
🍽️
Nutrition & Diet
Food patterns correlated against symptom flares in real time
Daily
😴
Sleep Quality
Duration and quality, weighted against individual symptom response
Daily
Stress & Activity
Self-reported stress events and physical activity levels
Daily
💊
Medication Adherence
Timing and consistency — flagged when patterns break
Daily
structured
data in
insights
out
AI & Analytics Layer · Always On

Pattern Recognition & Insight

Continuously analyses longitudinal patient data. Surfaces clinically meaningful signals. Never diagnoses or prescribes.

Trigger Identification
Identifies which specific factors — stress, diet, sleep, activity — drive each individual patient's symptoms over time
Longitudinal Pattern Recognition
Detects trends across 30+ daily data points that no single appointment could reveal
Protocol Personalisation
Adjusts nutrition and lifestyle guidance in real time as the patient's data profile evolves
Deterioration Alerts
Flags when symptom patterns suggest worsening — before the patient contacts the system
Real-World Evidence at Scale
At population scale, continuous datasets that shift clinical practice for chronic conditions
physician
outputs
Physician Layer · Decision & Action

What Reaches the Physician

Structured outputs that make every clinical interaction faster and more informed.

📋
Pre-Visit Clinical Summary
Longitudinal symptom trajectory, trigger patterns, and adherence — ready before the appointment begins
🔍
Individual Trigger Profile
Which factors are driving this patient's symptoms — ranked by impact, not assumed by protocol
📈
Biomarker Trend Tracking
How the patient is responding to current management — visible between visits, not only at review
🚨
Early Warning Flags
Deterioration signals surfaced before the patient seeks urgent care — enabling proactive intervention
🧬
Protocol Recommendations
AI-generated suggestions reviewed, adjusted, and approved by the clinical team — never applied autonomously

All clinical decisions remain with the physician. Allvi's AI layer surfaces signals and structures data — it does not diagnose, prescribe, or act without clinical oversight. The result: physicians are better informed at every interaction, and patients receive care that adapts continuously between every visit.

* AI-powered analysis supports clinical decision-making and is reviewed by Allvi's specialist care team. It does not replace clinical judgment, diagnosis, or prescribing authority.

Built for Health Systems

What Allvi delivers to your organisation

Three structural improvements — to visit quality, system utilisation, and population management capacity — without requiring anything new from your physicians.

Clinical Quality

Better visit quality

Your physicians receive a structured clinical summary before every appointment with an Allvi patient. The 15 minutes becomes productive. The patient arrives with longitudinal symptom data, identified trigger patterns, and tracked biomarkers. The physician leaves informed. Decisions are faster and better grounded in what has actually happened since the last visit.

Utilisation Management

Reduced unplanned utilisation

Continuously managed patients generate fewer urgent care visits, fewer unplanned specialist referrals, and fewer ER presentations. In a value-based model, that reduction has direct, measurable economic value. Patients contact the system when something meaningful requires clinical attention — not because they have no other structured support.

Population Scale

Population management at scale

AI-enabled daily monitoring allows one care team to manage a significantly larger patient panel between visits. Continuous chronic care — historically too resource-intensive to deliver at population scale — becomes structurally viable for the first time. Allvi is the infrastructure that makes between-visit management scalable.

Proof of Mechanism

Daily data. Real clinical signal.

We are not presenting a clinical trial. We are presenting proof of mechanism — what continuous management looks like when the infrastructure works. One patient. Thirty-five days. 100% daily tracking compliance.

100%
Daily tracking compliance — every day, over the full monitoring period
+70%
Improvement in energy score — from 5.0 to 8.5 out of 10 — all-time high at week 8
46%
Reduction in self-reported stress score across the monitoring period
83pp
Reduction in hair loss frequency — a key symptom of unmanaged thyroid disease
7/9
Tracked symptoms substantially improved by week 8
0%
Depression symptom frequency by week 8 — down from 33% at baseline
TSH
Normalised from 4.48 → 2.13 mIU/L — Quest Diagnostics verified laboratory result
2wk
Therapy step-down from weekly to fortnightly — documented clinician-noted mental health improvement

I think I have hit my stride. Hair loss has really reduced. I have more energy including mental energy. My therapist and I moved appointments from weekly to every other week — which is a huge win.

Week 8 unprompted patient update
Hashimoto's Thyroiditis

Verified Laboratory Results
TSH — Thyroid Stimulating Hormone
4.48 2.13 mIU/L
↓ 52% — normalised within reference range
Quest Diagnostics · verified laboratory result
Depression Symptom Frequency
33% 0%
↓ 100% · by week 8
Therapy Session Frequency
Weekly Fortnightly
Clinician-documented step-down
Energy Score (out of 10)
5.0 8.5
↑ 70% improvement · patient-reported daily
Case Study — Stress Trigger Identification

A finding that changed a patient's management protocol.

Through structured daily data collection, Allvi's AI identified that stress was the overwhelmingly primary driver of this patient's symptom flares — while sleep, the assumed primary factor, had almost no independent impact. This finding could not have emerged from a single appointment. It required longitudinal data.

What the data showed
Stress events → Symptom flare within 48 hours in 89% of instances

Poor sleep alone → Negligible symptom impact when stress was absent

Protocol change: Shifted focus to stress management — resulting in 91% reduction in joint pain and 81% reduction in anxiety.

Data from one de-identified participant. Presented as proof of mechanism. Confounders acknowledged — concurrent medication initiation and lifestyle changes. Prospective multi-patient study with validated QoL instruments in development.

Who We Work With

Built for health systems,
medical groups, and value-based practices.

Allvi is designed for partnerships with health systems, accountable care organisations, large medical groups, and value-based care practices managing chronic condition populations at scale.

🏥

Health Systems & Hospital Groups

Allvi reduces between-visit demand on overstretched clinical teams — improving outcomes for chronic condition populations without increasing physician headcount.

  • Reduced unnecessary urgent care contacts
  • Lower avoidable ER presentations
  • Population-level longitudinal data for planning
  • No clinical workflow changes required
📋

ACOs & Value-Based Care Practices

In a value-based model, the economic case for continuous chronic care is direct. Allvi reduces avoidable utilisation while improving the quality data needed for risk adjustment and performance measurement.

  • Patients arrive with structured data at every visit
  • Fewer avoidable urgent and emergency contacts
  • Longitudinal patient data for quality reporting
  • Supports chronic care management billing models
🔬

Large Medical Groups & Women's Health Practices

Allvi is purpose-built for the conditions that disproportionately affect your female patient panel — thyroid disease, PCOS, endometriosis, and perimenopause — the highest-frequency, most complex chronic cohort in most practices.

  • Specialist nutrition and lifestyle support between visits
  • Medication adherence monitoring
  • Early flagging before patients escalate to urgent care
  • Physician pre-visit summaries — structured, not narrative
Partnership Models

How we work together.

Allvi is designed to be deployed alongside existing care pathways — as a structured pilot, a commissioned service, or an integrated referral pathway.

Commissioned Service

Per-Patient Deployment

For organisations ready to commission Allvi as a structured chronic condition self-management and early intervention service across a defined patient population.

  • Per-patient-per-month model — costed on request
  • Deployed across a defined population or care pathway
  • No clinical staff redeployment required
  • Structured outcomes reporting for commissioning review
  • Integrates with existing referral and care coordination pathways
The Team

Built by someone who lived it —
and knows how to scale it.

Rashmi Gupta
Founder, Allvi

Rashmi was diagnosed with hypothyroidism after more than two years of being dismissed — told her fatigue, brain fog, and weight gain were stress. She experienced firsthand what the chronic care gap looks like from inside it. Allvi is built on both the clinical understanding of the problem and the conviction that it can be solved at scale.

Dr. Kashinath Dixit
MBBS, MRCP · Endocrinology & Lifestyle Medicine
30 years of clinical experience as an Endocrinologist and Andrologist, with a specialisation in lifestyle medicine and hormonal health management.
Dr. Rashmi Kumari
MBBS, MS (OB-GYN) · Gynaecology & Surgery
14 years of experience as a Gynaecologist and Laparoscopic Surgeon. Evidence-based approach to women's reproductive and hormonal health.
Dr. Marina Hedlund
DC · Functional Medicine
Functional Medicine specialist with a focus on thyroid disorders, autoimmune conditions, and women's hormonal health across the full lifecycle.
Ready to explore a pilot?

The data your physicians need
is being generated every day.
Allvi makes it available.

We are at the earliest stage — real data, proven mechanism, and a founding team that has built at scale. We are actively seeking health system pilot partners to prove this model at population scale.

Or reach us at rashmi@allvihealth.com · allvihealth.com