AI in Remote Patient Monitoring Patent Landscape Analysis Report 2026 Edition

AI in Remote Patient Monitoring Market Size, Growth Trends, and Industry Outlook

Monitoring patients at home has moved from a niche service for a few device-dependent groups to a routine part of chronic disease management and post-discharge care. Health systems face growing numbers of patients with heart failure, diabetes, chronic obstructive pulmonary disease and kidney disease, while nursing and care coordination staff remain in short supply. Connected blood pressure cuffs, pulse oximeters, glucose sensors, patches and wrist-worn devices produce continuous data that no clinical team can review by hand. Machine learning is applied to separate meaningful change from noise, rank patients by urgency and predict decline before symptoms prompt an emergency visit. Demand is strongest where programmes are reimbursed, where clinicians carry large panels, and where hospitals want to free beds through supervised care at home.

Market growth is driven by the need to review very large volumes of device data with limited clinical staff, which makes automated alert triage a condition for running monitoring programmes at scale rather than an optional extra. Cardiovascular disease causes close to 20 million deaths worldwide each year according to the World Health Organization, which supplies a very large pool of candidates for home surveillance, and more than 500 million adults live with diabetes. In the United States, Medicare created billing codes for remote physiologic monitoring in 2019 and 2020, so providers can be paid for reviewing device data, which has drawn investment towards tools that make the review efficient.

Staffing sets the practical limit on programme size, which explains why prioritisation features attract so much development effort. A nurse reviewing readings from several hundred enrolled patients cannot inspect every value, so scoring models decide who is contacted first, and that in turn determines how many patients one team can carry safely. Acute hospital care at home waivers in the United States, introduced in 2020 and extended several times since, showed that hospitals will treat acutely ill patients at home when continuous monitoring is in place; each extension encouraged health systems to build monitoring and escalation workflows as permanent services. Patient engagement features matter for a related reason, because missed measurements leave gaps in the data that weaken risk scores, so adherence prompts raise the value of every downstream model.

AI in Remote Patient Monitoring Patent Filing Trends and Innovation Activity

Filing intensity has risen in step with commercial investment in connected care. Device makers, monitoring service companies, health systems and software start-ups have all added patent families covering how home sensor streams are cleaned, interpreted and turned into clinical action, and filings from companies that run monitoring programmes at scale are growing faster than those from sensor suppliers. Funding rounds for home monitoring start-ups, acquisitions of cardiac monitoring and chronic care companies, and the launch of hospital at home programmes in large health systems have each been followed by new filings on analytics and workflow claims. Claims have moved steadily from sensor hardware towards software that manages alerts and predicts risk.

The landscape comprises more than 8,700 patent families, with annual filings peaking in the period from 2021 to 2024, when reimbursement, pandemic-era adoption of home care and cheaper wearable sensors coincided. Main technology clusters are deterioration and anomaly detection from home and wearable data, alert prioritisation and nurse workload reduction, risk scoring for chronic disease patients, and hospital at home monitoring platforms. Two further clusters, connected device and wearable data integration and patient engagement and adherence prompts, add volume and are growing from a smaller base.

Rising demand for home-based monitoring that can run with limited clinical staff explains the steady expansion of patent families, because each new programme model requires fresh claims on detection, prioritisation and escalation methods.

Scope of the AI in Remote Patient Monitoring Patent Landscape Analysis Report

This report covers patent families filed with the United States Patent and Trademark Office, the China National Intellectual Property Administration, the European Patent Office, the Korean Intellectual Property Office, the Indian patent office and the World Intellectual Property Organization, together with national filings in other major markets. Market coverage spans the full range of artificial intelligence applied to remote patient monitoring, from wearable, patch and home sensor systems to cardiac rhythm and glucose monitoring services and hospital at home programmes. Device-level inventions and programme-level methods are both included, so sensing, analytics, clinical workflow and reimbursement-linked filings are represented in every region.

Technology scope covers monitoring solutions including Wearable and Patch Monitoring Systems, Home Vital Signs Monitoring Platforms, Cardiac Rhythm Monitoring Services, Glucose and Metabolic Monitoring Platforms and Hospital-at-Home Monitoring Systems. Artificial intelligence functions in scope are Anomaly and Deterioration Detection, Alert Prioritisation and Triage, Predictive Risk Scoring and Engagement and Adherence Support. Applications are examined across Cardiovascular Disease, Diabetes, Chronic Respiratory Disease, Chronic Kidney Disease and Post-Acute Recovery, with attention to the sensors, models, software and clinical workflows that connect monitored data to a care decision.

Entities include monitoring device manufacturers, patient monitor and sensor suppliers, cardiac monitoring service providers, digital chronic care companies, hospital at home platform developers, health system innovation units and specialist start-ups developing analytics for home data. Academic medical centres and university groups contribute filings on prediction models validated against clinical outcomes, and technology companies with consumer wearables hold claims on sensing and data processing. End user organisations such as Hospitals, Remote Monitoring Service Providers, Home Care Settings, Health Insurers and Payers and Telehealth Providers appear as applicants or licensees where they have developed their own models.

Global AI in Remote Patient Monitoring Patent Landscape Analysis

AI in Remote Patient Monitoring Patent Filing Trend Analysis

Patent trends show steady expansion in filings since 2020, with the strongest growth in software and analytics claims rather than sensor hardware. More than 1,800 patents were granted in 2025, and grant rates have been helped by applicants narrowing claims to specific signal processing and workflow steps. Early filings concentrated on single-parameter alarms, whereas recent families describe multi-signal models that combine heart rate, respiration, oxygen saturation, activity and patient-reported data.

Filing activity grew by an estimated 19 percent a year, on average, between 2020 and 2024 (approximate). The increase was driven by the expansion of home care during the pandemic, Medicare reimbursement for remote physiologic monitoring, repeated extension of hospital at home waivers, and wider use of consumer wearables with clinical-grade sensors. Applicants also began filing on language model summaries of patient trends and on models trained on large wearable datasets, which added a new group of claims in 2023 and 2024.

Leading Companies and Assignee Analysis in the AI in Remote Patient Monitoring Patent Landscape

Geographic Analysis of AI in Remote Patient Monitoring Patent Activity

The report assesses patent activity across major jurisdictions, including:

• United States
• Europe
• China
• Japan
• South Korea
• India
• International and WIPO filings

The United States leads filing volume, supported by reimbursement for remote physiologic monitoring, hospital at home waivers and a large base of monitoring service companies. China ranks second, with filings from device makers, internet health platforms, hospitals and universities directed at chronic disease management and elderly care. Europe follows, with the European Patent Office receiving applications from medical device groups and national health system pilots. South Korea contributes through electronics and wearable manufacturers, India through health technology start-ups and service firms working on low-cost monitoring, and WIPO international applications show how applicants seek protection in several regions at once.

AI in Remote Patient Monitoring Technology Segmentation and Innovation Areas

Patent activity in AI in remote patient monitoring falls into five technology areas:

• Deterioration and anomaly detection models
• Alert prioritisation and triage engines
• Predictive risk scoring for chronic disease
• Hospital at home monitoring platforms
• Wearable and device data integration

Deterioration and anomaly detection models lead patent activity, because every monitoring programme needs a method that recognises meaningful change in vital signs before an alert can be prioritised or a risk score applied. The cluster draws filings from device makers, cardiac monitoring services and hospital groups, and detection claims tied to a specific sensor type or condition are easier to define and defend than general workflow claims. Alert prioritisation follows closely, driven by pressure on nurse workload.

Competitive Landscape and Emerging Trends in the AI in Remote Patient Monitoring Market

Competition is spread across device incumbents, monitoring service operators, hospital at home platform developers, consumer technology companies and start-ups. Koninklijke Philips and Masimo compete on sensor quality and clinical credibility, while Biofourmis, Teladoc Health and Current Health compete on analytics, care team workflows and programme delivery. Acquisitions have shaped the field, including the purchase of BioTelemetry by Philips in 2021 and of Livongo by Teladoc Health in 2020. Entry barriers are lower in software than in hardware, so new applicants appear mostly in prioritisation, risk scoring and engagement claims.

Companies are focusing on:

• Multi-signal deterioration prediction models
• Alert filtering to reduce nurse workload
• Chronic disease risk stratification
• Hospital at home escalation workflows
• Wearable and patch sensor integration
• Adherence prompts and patient engagement
• Cardiac rhythm and glucose analytics

Emerging themes include models trained on large wearable datasets, language models that summarise patient trends for clinicians, continuous noninvasive estimation of blood pressure and glucose, and federated learning methods that train models across hospitals without moving raw data. Filings are also appearing on closed-loop programmes in which a risk score triggers a medication review or a nurse visit, and on methods for explaining alerts so that clinicians can judge them. Regulators in the United States and Europe have issued guidance on how algorithm updates are managed after clearance, which is shaping how applicants describe model changes in their claims.

Strategic Insights from the AI in Remote Patient Monitoring Patent Landscape

The AI in remote patient monitoring patent landscape combines sensor, analytics and care delivery inventions, and its shape reflects the move from hospital monitoring towards continuous supervision at home. More than 8,700 patent families are held by device groups, service operators, health systems and start-ups, with ownership split between hardware incumbents and software-led programme operators. Knowing which clusters are crowded and which remain open, such as engagement support or kidney disease monitoring, helps readers judge where new claims are still possible and which assignees control the routes into the market.

The landscape provides actionable intelligence that supports nine uses:

• Intellectual property strategy for monitoring analytics
• Research and development prioritisation for triage
• Competitive benchmarking of leading assignees
• Technology scouting in wearable data analytics
• Licensing evaluation for sensor and model patents
• Partnership assessment with monitoring service operators
• Investment analysis of home monitoring start-ups
• Market entry planning by jurisdiction
• Portfolio expansion in chronic disease risk scoring

Research Methodology for the AI in Remote Patient Monitoring Patent Landscape Analysis

Patent research covered the databases of the United States Patent and Trademark Office, the European Patent Office, the China National Intellectual Property Administration, the Korean Intellectual Property Office and WIPO, together with commercial databases that consolidate worldwide records. Searches combined Cooperative Patent Classification codes for physiological monitoring, telemedicine and medical data processing with keywords on remote monitoring, wearable sensors, early warning, risk prediction and hospital at home. Records were gathered up to the most recent complete filing year, and applications were grouped into patent families so that one invention filed in several countries is counted once.

Screening removed duplicate families and filings with no relevance to remote patient monitoring, such as claims on general-purpose sensing with no monitoring use. Remaining families were classified by monitoring solution, artificial intelligence function, chronic condition, jurisdiction, assignee and technology cluster, and assignee names were harmonised across subsidiaries and acquired companies. Legal status and grant information were recorded to separate granted patents from pending applications.

Market analysis support drew on regulatory filings, reimbursement policy documents, company disclosures, clinical literature and published programme data to connect patent activity with commercial and clinical developments. Filing trends were compared with approvals, reimbursement changes and partnership announcements to identify where invention preceded or followed adoption. Estimates of growth rates, shares and peak periods are approximate analyst figures and are rounded.

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