Remote patient monitoring represents a transformative approach to healthcare, offering continuous patient care outside the traditional clinical setup. Originating from early telemedicine concepts, it has evolved with digital technology. Today, remote patient monitoring services ensure 24x7 care management through remote tracking of a patient’s vital signs and health data, supported by “hospital at home” initiatives. Advancements in wearable, IoT, cloud, and AI technologies have played a significant role in the global adoption of remote patient monitoring.

With healthcare trends shifting towards personalized care and patient engagement, the Remote Patient Monitoring market is poised to grow and reach about $41.7 billion by 2028. This post unveils our use case research to identify the specific technology platforms shaping this revolutionary healthcare delivery service.

What is Remote Patient Monitoring?

Remote patient monitoring (RPM) is a healthcare delivery method that utilizes technology to monitor patients' health remotely outside the hospital premises or traditional clinical setting. It relies on several technological interventions, such as wearable sensors, mobile apps, and telecommunication networks, to collect and transmit patient health data to healthcare providers in real time. In this case, the healthcare provider is still a hospital or a clinic, and the RPM setup at the patient’s home is attached to the provider for monitoring and care management purposes.

The RPM systems collects data which typically includes vital signs of a patient, like heart rate, blood pressure, blood glucose levels, and other relevant health metrics. RPM enables healthcare professionals to monitor patients' health status, allowing for early detection of changes or deterioration in health conditions. By providing proactive and personalized care, RPM aims to improve patient outcomes, enhance patient engagement, and reduce the need for frequent in-person visits to healthcare facilities.

Use Cases of Remote Patient Monitoring

Remote patient monitoring is an umbrella theme encompassing a wide range of scenarios specific to the patient's medical history. These include chronic disease management, post-acute care monitoring, and elderly care. However, our research is focused on the technological underpinnings of managing the general workflow involved in any RPM setup.

Reference System Architecture of a Remote Patient Monitoring Setup

To better understand the workflow, let’s define the high-level reference architecture consisting of the main components in a typical RPM deployment.

Remote Patient Monitoring Reference Architecture

This RPM architecture has the following key components:

  1. 1
    Wearables and Trackers: These are portable devices that periodically track the patient’s vital parameters. Some common examples are Pulse Oximeters, Pulse rate Monitors, Blood Pressure monitors, and Weighing Scales.
  2. 2
    Monitoring Gateway: This equipment acts as the patient-side gateway, interfacing with all the wearables and tracker devices to capture the patient’s health data. In critical care or terminal illness cases,the gateway doubles as a bedside vitals display console.
  3. 3
    Patient Self-care App: This is the patient’s personalized app used to monitor and check historical data on vitals, provide additional features to track medication, and seek a doctor or caregiver consultation.
  4. 4
    Telecom Service: This is the local telecom or mobile service provider through which the monitoring gateway transmits patient's health data.
  5. 5
    Cloud Service: This is the remote cloud infrastructure that hosts the applications and data for managing the patient’s healthcare data. This is under the control of a hospital or care management service provider responsible for delivering RPM service.
  6. 6
    EHR/EMR Data Store: This specialized data store contains the patient’s EMR (Electronic Medical Record) and EHR (Electronic Health Record) data. This is primarily a data warehouse where data from multiple EMR/EHR service providers are aggregated for health analytics.
  7. 7
    Care Management Portal: This is the caregiver’s dashboard for facilitating remote healthcare interventions to address patient needs, reviewing the patient’s health data, and managing resources assigned to the patients.
  8. 8
    Doctor Consult App: This is the doctor’s app that provides patients with online and offline consultations and access to their health data.

Detailed Use Cases of the Remote Patient Monitoring Workflow

Let’s drill down the RPM workflow to identify the specific use cases and map them to the components.

Manual Use Case

Manual Use Case

Automated Use Case

Automated Use Case

The specific use cases are:

  1. 1
    Patient Vitals Data Acquisition: Capturing the patient’s vital parameters using medically certified Wearables and Trackers.
  2. 2
    Patient Self Monitoring: Tracking and reviewing the health data by patients or their home caregivers by using the Patient Self-care App.
  3. 3
    Patient Adherence: Ensuring adherence for periodic health data acquisition and following the prescribed medication.
  4. 4
    Vitals Signs Data Transfer: Transmitting the essential vital signs data from the patient-side Monitoring Gateway to the caregiver-side Cloud Service via the Internet or Telecom Service.
  5. 5
    Health Data Aggregation: Integrating with healthcare service providers and EMR/EHR vendors to compile a localized dataset of the patient’s health records for further analysis.
  6. 6
    Clinical Workflow Interoperability: Ensuring transparent data sharing and access across various healthcare-specific information systems supporting patient treatment.
  7. 7
    Continuous Vitals Monitoring: Ensuring continuous monitoring of the patient’s vital parameters transmitted from the patient-side Monitoring Gateway and providing mechanisms to alert the caregiver or doctor in case of any anomalies.
  8. 8
    Remote Doctor Consultation: Providing a mechanism for the patient to reach out to the doctor for offline queries or online consultation, either through messaging, voice, or video calls.
  9. 9
    Health Analytics: Providing detailed historical reports and analysis of the patient’s vitals to the caregiver teams for arriving at timely interventions or improved patient outcomes.

The Top Remote Patient Monitoring Technology Enablers

Realizing the Use Cases in Remote Patient Monitoring Systems

Let’s analyze the specific use cases and technology platforms and tools backing them to realize a standard remote patient monitoring setup.

Manual Use Case

Patient Vitals Data Acquisition

Patient Vitals Data Acquisition refers to physiological measurements that reflect the human body's essential functions and overall health status. These measurements typically include the most elementary health signs, such as heart rate, blood pressure, temperature, and oxygen saturation.

For RPM, vitals data have to be periodically collected in a way that does not cause too much inconvenience to the patient. An array of medical devices serves this purpose, but two possibilities exist considering the different integration scenarios with RPM:

1. Pre-integrated RPM devices with care management systems: Many healthcare product companies offer pre-integrated tracking devices with an existing RPM platform. This is an ideal choice for deploying an RPM solution with minimal effort in custom development and configuration.

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Tenovi offers an integrated solution that combines a care management system with a list of FDA-cleared RPM devices that support a Bluetooth interface for wireless connectivity.

2. Custom tracking hardware: There are situations which demands the development of custom healthcare tracking device. Such devices have specific form factors, specialized sensors, optimization, or proprietary technology requirements.

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Blues offers a hardware system-on-module (SoM) with WiFi, cellular, and satellite connectivity. This SoM can be integrated with medial sensors to build custom wearables or tracking devices.

Manual Use Case

Patient Self Monitoring

Patient Self-Monitoring empowers patients to actively engage with RPM by regularly checking and reporting their health data from home. This use case complements Continuous Vitals Monitoring carried out by the caregiving provider.

The key technology enabler for this use case is the Patient Self-care App, a mobile app that is always synced with the RPM system to access the vitals data. The iOS and Android platforms offer many frameworks and widget libraries to display patient-related information, including charts and historical vitals data reports. From the integration standpoint with the components of the RPM system, there are two important features to be considered:

1. Alerts for patients: Since patients are not always expected to use the app, alerts are the primary means of notifying them of certain events related to their daily adherence and periodic consultations with the caregiving team or doctors.

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PubNub supports mobile push notifications for iOS and Android platforms. These can be triggered from the care management portal based on predefined rules.

2. Information sharing and routine assistance: Caregiving teams must frequently interact with patients to check their well-being and provide assistance to answer questions. AI agents can offload these routine tasks from human caregivers while maintaining frictionless patient experience.

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SmartAction offers an intelligent health agent that can converse with patients via voice and chat-based messaging interface to facilitate information sharing, triaging of symptoms, and appointment bookings.

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One.ai offers embeddable GPT chat agents for websites and apps. These agents can be trained on patient history or healthcare literature to provide streamlined patient interactions for information sharing and consultation support.

Manual Use Case

Patient Adherence

Patient Adherence is paramount in RPM systems since patients are not under regular clinical supervision within a hospital environment. Therefore, ensuring patients' consistent adherence to prescribed treatment plans, monitoring protocols, and Standard Operating Procedures (SOPs) for self-care becomes important. Adherence can be at two levels:

1. The first level concerns treatment plan adherence, which involves medication schedules, dietary restrictions, physical activity recommendations, and other self-care activities.

2. The second level of adherence concerns vitals monitoring for following a preset schedule for capturing vitals as part of Patient Vitals Data Acquisiton.

Ensuring Patient Adherence is tricky since monitoring all the levels and sublevels of adherence is technically challenging and overburdens the patient. From RPM's perspective, the most critical adherence is to monitor the regular measurement of vital parameters.

tenovi_logo

Tenovi’s RPM solution comes with an intelligent detection mechanism that provides patients with a visual indication if they miss their vitals monitoring adherence.

Automated Use Case

Vitals Signs Data Transfer

Vital Signs Data Transfer involves the real-time transmission of physiological measurements from the patient side to the remote caregiver side components of the RPM system. This data must be transmitted securely and in real time to a centralized Cloud Service that hosts the RPM system.

Although transmitting data between two devices over the Internet is possible in many ways, RPM has a few technological barriers. Firstly, the RPM setup should be able to scale to handle real-time data ingestion from thousands of remote patients. Additionally, data integrity must be ensured over an unstable wireless connection.

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PubNub’s global data streaming network, with over 15 points of presence worldwide, ensures high reliability and extremely low latency for healthcare data communication.

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Hazelcast's managed data platform offers a computing and storage component that can be hosted on any cloud closer to the physical RPM deployment. This platform can process high-volume data transfer with mission-critical reliability.

Automated Use Case

Continuous Vitals Monitoring

Continuous Vitals Monitoring is the most critical and frequently executed use case in RPM. It involves the continuous and real-time tracking of patients' vital signs from the remote healthcare provider's location. This allows healthcare providers to keep a continuous watch on patients' health status, and prompt detecting of abnormalities or changes in vital signs.

This use case is facilitated through a dashboard dedicated to the care management team to check and report the patient’s vitals.

tenovi_logo

Tenovi’s care management dashboard is capable of managing the day to day RPM operations right from onboarding, to reviewing patient data, billing, and managing the allocation of RPM devices to patients at home.

However, given that the care management staff is always loaded with alert fatigue from multiple patients, an AI agent is an excellent option. AI can relieve the burden on healthcare staff in two ways:

1. As a virtual care assistant: An AI agent can double as a care assistant to maintain day-to-day patient interaction and perform routine care management chores.

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Esper AI (from 100 Plus) is a remote care assistant that augments care management teams by onboarding patients, guiding them through their routine care schedule, and assisting the caregiving teams with analyzing patient data.

2. Automated vitals monitoring: AI can analyze continuous physiological data to detect time-varying and drifting characteristics, to be reported to care management teams for necessary intervention.

tabiat_logo

Tabiat is an early-stage startup working on cases of COVID-19 and COPD (Chronic Obstructive Pulmonary Disease). Its goal is to detect changing patterns in a patient’s vitals data in real time and provide efficient and timely medical intervention.



Automated Use Case

Health Data Aggregation

Health Data Aggregation involves collecting and consolidating various health-related information from multiple sources to provide a comprehensive view of a patient's health status for facilitating care delivery. This is not an RPM specific use case. However, it assumes a lot of importance in ensuring efficient care management.

This use case is realized by combining electronic health record (EHR) data, which contains historical vitals data, with electronic medical record (EMR) data, which includes the patient's medical history. This data is sourced from external systems, such as third-party EHR providers and electronic health information exchange systems. The aggregation process gets complicated due to integrations with Hospital Information Systems (HIS), Laboratory Information Systems (LIS), and Radiology Information Systems (RIS) to source historical diagnoses, tests, and case notes. Further complications arise due to the addition of medical imaging data from the Picture Archiving and Communication System (PACS). Therefore, the dataset representing a patient’s digital health record is highly diverse and unstructured.

Thankfully, standardization of healthcare data makes it easier to interoperate between various sources and data formats. Fast Healthcare Interoperability Resources or FHIR, part of the HL7 umbrella standard for exchanging, sharing, and retrieving electronic health information, is the latest initiative towards this standardization effort. FHIR is designed to be flexible and incorporates different data formats, media resources, and document structures to cover all forms of medical and clinical data generated concerning any patient’s treatment. This data is standardized and normalized to ensure consistency as per the prevailing terminologies, such as Current Procedure Terminology (CPT), International Classification of diseases (ICD), and National Drug Code (NDC), and formats, such as Digital Imaging and Communications in Medicine (DICOM) and Clinical Document Architecture (CDA).

The main technological barriers to health data aggregation are:

1. Integration with external providers: Since health data records are practically fragmented across multiple sources and formats, the main challenge in RPM is to build an iPaaS based integration hub to connect with disparate health data providers.

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WSO2 is an enterprise-grade iPaaS tailored for healthcare data aggregation. It is FHIR compliant and supports many connectors for ingesting data from various generic and healthcare-specific sources.

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Kodjin (from Edenlabs) is a good choice as a hosted FHIR server and supports healthcare data normalization via its terminology service and data mapper.

2. Data persistence: The FHIR data formats are designed to be conducive to modern web applications that follow a RESTful interface and JSON/XML format. Given the multi-model nature of digital health records and their integration requirements with external systems, the data persistence layer must be capable of handling complex data structures beyond the traditional relational linking capabilities.

surrealdb_logo

SurrealDb is a promising database platform offering a complete multi-model data schema and a host of integrations. This makes it a good choice as a persistence layer for aggregating a patient’s health data, with custom integrations for further analysis.

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Fhirbase (from Health Samurai) is a good choice as an open-source, PostgreSQL compatible database supported by Aidbox, an FHIR-compliant iPaaS platform.

Automated Use Case

Clinical Workflow Interoperability

Clinical Workflow Interoperability refers to the ability of different stakeholders to seamlessly exchange and process clinical data and information within clinical workflows. A seamless clinical workflow is the essence of an efficient clinical decision-support mechanism between the various stakeholders in the care management system.

This use case is closely tied to Health Data Aggregation and Health Analytics. Therefore, choosing the right technology enablers as part of these associated use cases is also a deciding factor in the success of this use case. However, given the advent of AI and its impact on healthcare, there is a potential to further ease the clinical workflows through small language models, which can effectively build a patient-specific knowledge base to answer queries related to medical history and treatment options without sifting through loads of records.

leewayhertz_technologies_logo

The ZBrain platform (from Leeway Heartz) leverages generative AI to expedite clinical workflows involving the development of patient care plans and summary reports based on patient’s historical data.

Manual Use Case

Remote Doctor Consultation

Remote Doctor Consultation involves connecting patients to doctors for medical consultation, advice, treatment recommendations, and follow-up care from healthcare providers remotely, without the need for in-person visits to healthcare facilities.

The care management teams facilitate Remote Doctor Consultation, and the patient and doctor interact with each other via the Patient Self-care App and Doctor Consult App. There are two primary considerations for this interaction:

1. Appointment scheduling: This is part of the healthcare provider's customer service. AI agents can automate this process to schedule and reschedule appointments and deliveries.

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Talkie.ai is an AI phone automation solution that helps healthcare providers manage appointments and prescription refills with multilingual support.

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Cognigy offers a suite of customer care solutions based on voice and text, which can be used to build advanced healthcare service workflows with human-in-the-loop capabilities.

2. Voice/Video consultation: For doctor consultations requiring real time interactions, a voice or video call must be initiated between the Patient Self-care app and the Doctor consult app.

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EnableX provides a CPaaS platform with voice and video calling capabilities that can be easily embedded in apps.

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Sinch offers voice and video and rich messaging capabilities to enable real-time communication and media sharing between apps.

Automated Use Case

Health Analytics

Health analytics is a broad use case that covers many nuanced clinical use cases specific to different health conditions and diseases. This is beyond the scope of this discourse. From an RPM perspective, the main goal of health analytics is to maintain clinical surveillance based on the data captured through the related use cases of Vitals Signs Data Transfer, Health Data Aggregation, and Continuous Vitals Monitoring to stratify patients based on their risk levels for adverse health outcomes. This helps prioritize high-risk patients through proactive interventions and resource allocation for priority care coordination and preventive care management. RPM service providers have a few options here:

1. Full-stack healthcare data platform: Such a platform unifies all the data across healthcare information systems, human resources, and digital health records to build advanced analytics that manage patient stratification and cohort allocation to address gaps.

arcadia_io_logo

Arcadia is a healthcare data platform that offers a data lakehouse for delivering efficient data analytics for remote care management.

2. Custom dashboards: For custom analytics on patient healthcare monitoring from Continuous Vitals Monitoring, healthcare specific, web-based dashboards can be developed using embedded widgets.

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Sisense offers an embedded dashboard with AI/ML enriched analytics and insights to boost operational efficiency and deliver an exceptional patient experience.

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BoldBi offers an embedded analytics solution for building custom BI dashboard with a rich set of widgets and custom dashboard templates for healthcare analytics.

Key Technological Challenges in the Implementation of Remote Patient Monitoring


Data Security and Privacy

Protecting patient data privacy and ensuring data security during transmission, storage, and processing are critical challenges in remote patient monitoring. In the United States, the Health Insurance Portability and Accountability Act (HIPAA) sets standards for protecting patients' protected health information (PHI) and ensuring the privacy and security of electronic health records. Healthcare technology enablers must comply with HIPAA regulations when collecting, transmitting, storing, and accessing patient data in remote patient monitoring systems. Similarly, the Food and Drugs Administration (FDA) regulates the design and manufacturing of medical products used in RPM. In the European Union, the General Data Protection Regulation (GDPR) is applicable for protecting patient’s personal information.

Interoperability and Standards Compliance

Interoperability challenges arise between the various technology components of RPM due to incompatible interfaces and unsupported platforms. The key to address this challenge is the selection of components with open APIs and well-known integrations. Apart from that, the components must be compatible with underlying platforms. This includes mobile OS platforms such as iOS or Android for end-user mobile apps and cloud platforms such as AWS or Azure for hosting the backend components. Besides that, standards compliance as per HL7 FHIR, DICOM, and Logical Observation Identifiers Names and Codes (LONIC) formats is also a critical requirement. 

User Adoption and Engagement

Remote patient monitoring requires proactive participation from patients. However, patients may refuse to comply due to a lack of digital literacy, resistance to change, and fear of data security. Similarly, clinicians may encounter challenges integrating remote monitoring into their workflows. Conducting patient education and training and personalized onboarding is one way of addressing this problem. On the clinician side, incorporating gamification elements and incentives into the remote patient monitoring program to make monitoring activities more engaging and rewarding for care management teams is another way to tackle this challenge.

Technological Reliability

Since remote patient monitoring is so heavily dependent on connectivity, a lack of reliable Internet or connectivity on the patient side can lead to poor user experience and withdrawal. Additional reliability issues also arise due to device malfunction or software bugs. Therefore, designing the system with redundancy and failover mechanism, priority technical support, and troubleshooting service is also essential to remote patient monitoring deployment.

Cyber Security Risks

By far, the most significant risk in remote patient monitoring is cyber security. From unauthorized device access, falsification of vitals data in transit, malware and ransomware attacks, remote patient monitoring deployments can be an easy target for cyber attacks, all thanks to the multi-party supply chain and stakeholdership across the entire deployment. Similar incidents involving patient data breaches have had catastrophic consequences for healthcare companies in the past, and it is proven to be a low hanging fruit for hackers

Safeguarding remote patient monitoring systems from such risks entails developing a comprehensive IT security strategy, with additional checks and balances, and deploying advanced security posture management platforms.

Liability and Risk Management

Lastly, the legal responsibilities in remote patient monitoring are a tricky issue. Usually, healthcare providers assume primary responsibility for patient care and safety. They must establish clear protocols, guidelines, and procedures for remote monitoring with the technology providers, including handling emergencies for responding to abnormal findings that mandate immediate triaging. However, since the patients and their family members are also contributing stakeholders, the legalities can become complex, especially in life-threatening cases.  

Join Us in Shaping the Future: Contribute Your Insights on Remote Patient Monitoring!

As we delve deeper into remote patient monitoring, it becomes increasingly evident that it holds immense potential for revolutionizing healthcare. Your insights and contributions are pivotal in driving the future course of technology transformation behind this use case.

Please complete our survey to share your valuable insights. Additionally, you can leave your comments below. Together, let's leverage the opportunity to harness the power of remote patient monitoring and pave the way for a more accessible, efficient, and patient-centered healthcare landscape. 

About the author 

Radiostud.io Editorial Team

Radiostud.io Editorial Team - Handpicked content created by Team Radiostudio for customers and partners, showcasing thought leadership and trends across emerging technologies.

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