FOR: Care teams scaling RPM
    Healthcare · Applied AI · Remote Patient Monitoring

    Scale care,
    not headcount.

    Growing a remote-monitoring program used to mean growing the nursing team just as fast — more routine check-ins, more vitals to triage, more chances to miss a deteriorating patient. We built an AI layer that handles the routine and surfaces the urgent, so panels grow and deterioration gets caught earlier without adding nurses at the same rate.

    Designed · Built · Clinically guardedSingle client engagementDetails anonymized
    Triage queue
    Re-ordered by risk, in real time
    LIVE
    1
    Patient A · CHF
    Symptom chat flagged breathlessness
    SpO₂ 88% ↓ · HR 112
    2
    Patient B · Post-op
    Two readings above threshold
    BP 168/101 ↑
    3
    Patient C · Diabetes
    Trend rising over 3 days
    Glucose 212 mg/dL ↑
    4
    Patient D · Hypertension
    Auto-handled · adherence 100%
    BP 124/79 · stable
    5
    Patient E · COPD
    Routine check-in complete
    SpO₂ 96% · stable
    The executive so-what

    Larger panels per nurse. Earlier intervention. Fewer readmissions.

    The point was never a chatbot. It was to let a fixed clinical team safely monitor a growing cohort — freeing nurses from routine data collection so their hours land on the high-acuity cases that actually need a human.

    24/7
    Continuous vital monitoring
    3+
    Connected device classes
    HIPAA
    Compliance engineered in
    1
    Unified EHR patient view

    Proactive intervention

    Continuous AI monitoring catches deterioration earlier through timely, threshold-aware alerts — reducing the emergency visits and readmissions that follow a missed trend.

    Nurses freed for high-acuity care

    Routine check-ins and data entry are absorbed by the AI, so clinical time shifts to complex cases — the same team manages a materially larger monitored panel.

    Scalable, data-driven programs

    Physician offices and health systems manage larger remote cohorts effectively, with cleaner trend data supporting more informed treatment adjustments over time.

    How it works

    Signal → AI triage → prioritized clinician attention

    Every reading and every message runs through one triage layer. Routine cases are handled and logged automatically; urgent ones are ranked and escalated — so the clinician always sees the right patient first.

    CONNECTED DEVICES BP cuff Glucose Pulse ox AI TRIAGE LAYER Threshold + anomaly check Guardrailed symptom chat Risk score + rank URGENT Escalate to clinician now ROUTINE Auto-handled + logged Clinician attention where it counts
    Fig. 1 — Home-to-clinic monitoring pipelineRoutine auto-handled · Urgent escalated
    BEFORE A nursing shift, unaided Routine check-ins & data entry · 70% High-acuity · 30% AFTER The same shift, with the AI triage layer Routine · 20% High-acuity care & complex cases · 80% time reallocated to the patients who need a human Routine monitoring the AI now absorbs High-acuity clinical care
    Fig. 2 — How a nurse's shift is reallocatedIllustrative of the intended shift in clinical time
    How we delivered

    One chatbot. Fewer routine hours. Faster escalation.

    Four coordinated pieces — engineered where the engineering earns trust, and no further.

    PART 01

    A chatbot patients actually use

    A conversational agent that meets patients in a familiar app or web interface — medication reminders, scheduling, symptom reporting, plain-language education. Natural dialogue kept inside strict clinical guardrails so every exchange stays focused on patient safety.

    Conversational · Multi-channel · Guardrailed
    PART 02

    A vital-monitoring AI agent

    Continuously pulls readings from connected RPM devices, analyzes trends, and flags values outside physician-defined thresholds. When something looks off, it opens a chat to assess symptoms and weighs symptom-plus-vital combinations to catch early deterioration.

    Trend analysis · Anomaly detection · Thresholds
    PART 03

    A prioritized clinician dashboard

    Nurses and physicians get a single triaged view — the patients who need attention now surface at the top, backed by summarized vitals, chatbot transcripts, and reported symptoms. Time shifts from routine collection to the cases that need a human.

    Triage-first · Summarized · Actionable
    PART 04

    EHR integration, done safely

    Collected data and alerts flow into the existing Electronic Health Record for a single, unified patient view — no parallel system to reconcile. HIPAA-conscious communication and encryption are engineered in from the first commit, not retrofitted before launch.

    EHR write-back · HIPAA · Unified record
    Spec sheet · how we build

    The engineering under the conversation that makes RPM trustworthy.

    Jargon only where it adds credibility. Every choice below traces back to patient safety or scale.

    S-01
    Cloud-native and scalable
    Built to grow with the patient cohort on secure cloud infrastructure — the same program handling ten patients handles ten thousand without a rebuild.
    S-02
    Guardrailed LLM dialogue
    Large language models deliver natural conversation, bounded by explicit safety guardrails so the chatbot advises, reminds, and escalates — never improvises clinical judgment.
    S-03
    Rules plus learned patterns
    The monitoring agent combines physician-set rules with trend and anomaly analysis, so alerts reflect both clinical policy and the individual patient baseline.
    S-04
    Device and EHR integrations
    API connectors to RPM device manufacturers and EHR systems keep readings, alerts, and records in sync across every source of truth.
    S-05
    Compliance as a foundation
    HIPAA-aligned data handling, encryption, and secure protocols are treated as architecture, not paperwork — designed in before the first patient message.
    Final assembly

    The hard part of clinical AI is knowing when to escalate. That is what we built.

    Have an RPM program straining under its own growth? Let us show you the version that scales care instead of headcount.

    Start a conversation →hello@altslate.com