Clinical Intelligence SaaS

We flag clinical decline days before it happens.

We connect via API to the electronic health record and monitor hospitalized patients' vital signs and lab results in real time — flagging risk of death and extended stay well in advance. The physician stays in control; we give the team time.

See how it works ↓
86%of deaths flagged, on average, days in advance
12 daysaverage lead time before death
13,266patients in the retrospective study
Deep Medica Patient Alerts panel showing a list of patients with clinical risk classified by color
Deep Medica mobile app for bedside vital signs and nursing scale recording
HospitalsUrgent CareHealth InsurersZero CAPEX · API integration
The Problem

The root cause is simple to name and costly when ignored

Hospitals live with extended stays and growing risk of infection, ICU transfer, and death. In most cases, the cause isn't a lack of data — it's the difficulty of catching clinical decline in time to act. That creates a hard cycle for patients, staff, and administrators.

Problem 01

Cost of extended stays

The longer a patient stays admitted, the higher the cost — and under Brazil's public health system (SUS), that bill is usually absorbed by the hospital itself.

Problem 02

Risk that compounds over time

Extended hospital stays significantly raise the risk of hospital-acquired infection, ICU transfer, and death.

Problem 03

Gaps in early detection

Care teams genuinely struggle to catch early signs of clinical decline amid the pace of day-to-day care.

Problem 04

Communication breakdowns

Delays in communication between teams slow down diagnosis and treatment — exactly when time matters most.

64% of adverse events in Brazilian hospitals are preventable (Fiocruz, 2022)
21% of ICU transfers are unplanned (Blackwell, 2020)
The Solution — The Intelligence Layer

We turn data into decisions — we don't diagnose. We buy time.

Evidence shows patients display signs of early deterioration long before becoming unstable. Deep Medica analyzes scattered clinical data and turns it into a wider intervention window — to reduce risk, cut costs, recover bed-days, and support proactive decisions.

01

Continuous monitoring

We analyze complex relationships between vital signs and lab results in real time, the moment they're released in the health record.

02

Prediction

We identify, early, cases at risk of death or extended hospital stay.

03

Precise alerts

Structured, urgency-stratified alerts for clinical deterioration — averaging 2.31 alerts/day per hospital, with no alert fatigue.

04

Expanded intervention window

We deliver days of lead time — not hours — for the team to act. Human-in-the-loop: the physician always assesses and decides.

+10 days

of lead time on risk alerts — well beyond what's typically achieved today, where alerting usually happens in a matter of hours.

2.31/day

is the average number of alerts per 170-bed hospital. No alert fatigue — the team is only notified when it truly matters.

Day 7

we flag extended-stay risk this early — a risk most solutions on the market simply don't address.

Zero CAPEX

We connect via API to the health record already in place. No system swap, no construction, no infrastructure investment.

Validation

Results from a retrospective study of 13,266 SUS patients

We followed the hospital stay of 13,266 patients across public (SUS) hospitals over 3 years, excluding outliers (stays over 60 days) and ICU patients.

86% of deaths were flagged, on average 12 days before the outcome (537 of 624)
19% of admissions generated alerts — 2,532 cases flagged over 3 years
2.31 alerts per day on average — no alert fatigue for the care team
22.6 vs 10.7 average length of stay: flagged cases vs. overall average (days)

R$ 18.7 million in additional cost identified, over 3 years, in admissions flagged for risk of clinical worsening alone.

Scenario: Extended Stay


Day 1
Admission
Day 7
Risk alert
Day 14
Target discharge
Day 22
Average

Goal: cut the average length of stay of flagged cases by at least 8 days.

Scenario: Risk of Death


Day 1
Admission
Day 9
Risk alert
Day 21
Death
(average)

Expanded intervention window: 12 days — 86% of deaths were flagged within it.

Features delivered

From bedside to management dashboard

One ecosystem covers the entire journey: data capture, real-time analysis, clinical alerting, and management indicators.

Patient Alerts panel with risk classification

Clinical Risk Panel

The care team sees, in one place, which patients are at risk of clinical deterioration, classified into 3 levels: critical, severe, and worsening risk.

  • Breakdown of altered or critical biomarkers per patient
  • Physician assessment logged, with date and time history
  • Alert dismissal always requires a justification — the workflow itself generates the model's audit trail
  • Immediate push notifications for critical cases

Bedside Recording App

For hospitals without a structured electronic health record, a mobile app lets nursing staff record vital signs and scales right at the bedside — bringing Deep Medica to smaller public hospitals too, where the problem tends to be more severe.

  • Vital signs recording (temperature, blood pressure, heart rate, respiratory rate, glucose, saturation)
  • Nursing scales (level of consciousness, fall risk, pain, pressure injury, and more)
  • Immediate visual alert when a value falls outside the normal range
Mobile app for recording vital signs and nursing scales

Electronic health record connectors

API integration with the leading systems in the Brazilian market — no system swap, no construction, no CAPEX.

PDF lab result reading

For hospitals without a structured record, lab results delivered as PDFs are read and biomarkers are automatically linked to the patient.

Care team dashboard

Indicators such as unassessed alerts, intervention window, alert density, and completeness of nursing records.

Management dashboard

Bed-days avoided, estimated savings, average length of stay for flagged cases, and bed turnover — all in one management panel.

Model & Return

You pay for the monitored bed. The return shows up in month one.

B2B2G SaaS with recurring revenue of R$ 5 per monitored bed per day — just 0.8% of the daily cost of a SUS-admitted patient. Only actively monitored beds are billed.

Zero CAPEXNo system swap or infrastructure investment
Fair billingAn empty bed generates no invoice
Fast rolloutConnectors already built for the country's leading health records

170-bed hospital

Monthly investment R$ 25,500
Projected monthly savings R$ 349,065
ROI R$ 1 → R$ 12
Payback < 1 month
Compliance & Trust

Regulatory treated as part of the product, not paperwork

SaMD Class II

Classified and compliant with ANVISA's RDC 657/2022: documented analytical and clinical validation, technical dossier, and risk management.

LGPD-compliant architecture

Patient data is anonymized and encrypted throughout the entire solution architecture.

Human-in-the-loop

We don't diagnose or prescribe treatment — we buy time. The physician always assesses and decides.

Specialist team

Over 12 years of experience in hospital operations, led by an innovation lead, data scientists, ML/software engineers, and an infectious disease physician guiding clinical direction.

Purpose

"Our purpose is to offer a real path to savings and better quality of care, with technology capable of predicting before it happens — so the care team can fulfill its mission, and together we achieve the best outcomes."

DEEP MEDICA

Let's talk about your hospital?

Tell us a bit about your institution and we'll show you how Deep Medica can anticipate your patients' clinical decline.