Healthcare · Remote patient monitoring
Your RPM program generates more alerts than any care team can act on
Most RPM programmes fail on volume: devices transmit thousands of readings a day, alert thresholds get set to catch everything, and care teams quickly learn to ignore the queue. The patient who actually needs intervention is buried inside it.
Banao builds AI that stratifies risk from live vitals against each patient's own baseline, automates the daily check-in for patients who are stable, and surfaces genuine deteriorations — so nurses spend time on the five per cent that need a human, not the ninety-five per cent that don't.
What a Banao RPM AI deployment covers
Alert suppression alone is not enough. We build the full stack: risk model, automated outreach, device integration, and the reporting your clinical and operations teams actually use.
Patient-specific risk stratification
The model learns each patient's baseline and flags deviations that matter — not every reading that crosses a population-average threshold. Fewer alerts, each one more likely to be real.
Automated stable-patient check-ins
Daily voice or WhatsApp outreach handles the routine check for patients whose vitals are on track. The nursing team sees a summary; they are only pulled in when the AI flags a response that needs a human.
Adherence monitoring and early outreach
The system identifies patients who stop transmitting readings before they drop out of the programme entirely, and sends an appropriately timed outreach prompt — before a missed reading becomes a missed event.
Alert fatigue reduction
A suppression layer sits on top of your existing alert rules: duplicate alerts, readings outside plausible device range, and noise signals are filtered before they hit the care team queue.
EMR and care plan write-back
Vitals, flags, and care-plan updates sync back into your EMR so the monitoring feed does not live in a parallel silo that no one opens during a ward round.
Population-level reporting
Cohort health trends by condition, programme, and geography for clinical leadership and payer reporting — the data your QA and contract-review teams already need, in a dashboard they will open.
We operate our own AI before we ship yours
Banao runs a ~300-person engineering company on the same AI products it sells. InterviewGod screens our own engineering hires. Vikaas runs our own demand-generation pipeline. Systems that have to survive our own operation are built to a different standard than systems built only for a demo.
In patient monitoring, the gap between demo and production is where people get hurt. A vendor that depends on its own AI daily designs for uptime, audit trails, and failure modes that are invisible on a slide deck — because those failure modes land on us first.
- InterviewGodScreens Banao's own engineering hires every week.
- VikaasRuns Banao's own demand-gen pipeline end to end.
When RPM AI is the wrong place to start
Remote monitoring AI fails in predictable ways. We will tell you which one you are walking into before you sign a contract:
- Device coverage gaps: if fewer than sixty per cent of your enrolled patients are transmitting readings consistently, an AI layer does not fix the adherence problem — it amplifies it. Fix the coverage first.
- No integration path: if your RPM platform and your EMR cannot be connected, the AI runs in a silo and the care team has two places to look. Integration feasibility is a week-one audit, not an assumption.
- Small programme volume: below a few hundred enrolled patients, the gain from automated stratification rarely justifies a custom model. We will point you to a configuration-based rule engine instead and revisit when volume supports it.
How we start — fixed price, no commitment beyond the Sprint
We do not quote a build against a brief. We audit your device data, alert volume, and integration landscape first.
- AI Discovery Sprint2 weeks · fixed price
We analyse a sample of your real vitals stream, map your alert rules against actual escalation outcomes, and hand back a stratification feasibility report, an integration options assessment, and ROI maths — yours to keep whether or not you proceed. The Sprint cost is credited against the build if you continue.
- Build
Risk model training on your patient population, automated outreach design and integration, alert suppression layer, and EMR write-back. Compliance and data residency architecture are designed in from day one.
- Production and continuous improvement
Deployment with a clinician in the loop, a care team dashboard, and change management for nursing and coordination staff. The stratification model is retrained quarterly against actual outcomes.
Frequently asked questions
Which device manufacturers and RPM platforms do you integrate with?
We work via standard data feeds — HL7 FHIR, API, or a database read path — so the specific device manufacturer matters less than whether the platform exposes readings in a structured format. The week-one audit confirms what is available. Where a platform is genuinely closed, we will tell you before you commit budget.
How do you avoid suppressing an alert that turns out to be real?
The suppression layer filters noise, not clinical signals. We set conservative suppression thresholds during Sprint calibration, audit every suppressed-then-escalated event for the first 90 days, and tune against actual outcomes — not population averages. Clinicians retain an override, and all suppressed alerts are logged for audit.
What compliance standards apply to RPM data?
Depends on your jurisdiction: HIPAA for the US, DISHA for India, UAE PDPL and Dubai Health Authority rules for the GCC. We design data residency, encryption at rest and in transit, and role-based access up front for whichever applies. A compliance architecture review is part of the first scoping conversation.
Our patients are elderly and not comfortable with smartphone apps.
The outreach layer is not limited to apps. We design check-in flows over basic voice calls or SMS as well as WhatsApp, matched to your patient cohort. The device side stays with whatever hardware you have already distributed — we read from it, we do not replace it.
How long from contract to first patients enrolled in AI monitoring?
A typical path is a 2-week Discovery Sprint, an 8–10 week build, and a 3-week rollout with a clinical cohort. Banao's engineering bench means the build starts within days of sign-off, not after a recruiting cycle.
Bring your alert volume data to a 45-minute call
Tell us how many alerts your team is ignoring and how many enrolled patients are transmitting consistently. In 45 minutes we will tell you whether AI stratification is the right fix and what your data needs to make it work.
Book a 45-min scoping call