Your patients’ health in real-time, without the hospital walls

AI improves remote patient monitoring by learning each patient’s baseline and detecting subtle deteriorations rather than just firing when a static threshold is crossed. It can combine multiple signals (vitals, trends, symptoms, meds) into a risk score, prioritize high‑risk patients, and reduce noise by adapting alerts over time.

For reliable RPM models, high‑frequency vitals (heart rate, BP, SpO₂, weight) and device data from wearables are core, especially for cardiac and respiratory conditions. EHR history, diagnoses, meds, labs, prior admissions, and patient‑reported outcomes (symptoms, mood, adherence) markedly improve calibration and personalization. Start with vitals + EHR history, then layer in PROs and device-specific metrics as data volume and governance mature.​

Technically, the hardest parts are interoperable data exchange (FHIR/HL7), device heterogeneity, real‑time streaming, and mapping RPM events into existing clinical workflows and EHR UIs. Regulators expect clear data lineage, audit trails, and transparency on how AI influences care, plus alignment with HIPAA, CMS RPM billing rules, and sometimes FDA expectations for clinical decision support. Designing around clinician workload (no extra clicks, no alert overload) is as critical as the APIs.

AI‑enabled RPM increases the attack surface because PHI now flows through devices, apps, clouds, and AI services. Mitigation means strong encryption, hardened device/app authentication, least‑privilege access, and strict vendor management (BAAs, data processing agreements, regional controls). You also need continuous security monitoring, audit logs, and documented incident response tuned for remote data streams.

When evaluating “AI‑powered” RPM vendors, ask what models they use (rule-based, ML, deep learning), which outcomes they have validated, and on what populations. Request evidence: published results, real before/after metrics for readmissions or escalations, and details on how they handle bias, drift, and human oversight. Also probe integration depth (FHIR, major EHR/telehealth platforms), security posture, and how configurable their alerting and workflows are for your clinicians.​