Technological Innovations Have Transformed Traditional Healthcare Patient Monitoring Patterns with RPM

The value of AI-driven healthcare technological advancements are doing more than just shortening R&D timelines, streamlining quality controls and reducing drug attrition rates. They can serve as a powerful tool for leveraging innovation in ways that drastically transform traditional healthcare monitoring practices. Conventional patient monitoring approaches involve regular and manual invasive techniques that often require skin contact. Its effectiveness is often constrained by factors such as staff management, health care professionals’ workload and working hours.

The rise in biopharmaceutical innovations enabled remote patient monitoring (RPM) by introducing non-invasive technologies such as like telehealth apps, wireless body sensors and wearable devices. Their healthcare applications help clinicians track vital signs, make informed decisions and improve care.

Remote Patient Monitoring (RPM) Explained: Types of Systems Transforming Biopharma

As RPM evolves, AI and emerging technologies like IoT and blockchain are transforming digital health. These tools enable early detection of health issues and personalized care based on patient behavior. RPM systems have enhanced capabilities for identifying early signs of health decline and tailoring patient monitoring based on their specific needs and behavior patterns. In this blog, we explore how these biopharma innovations using non-invasive techniques and AI, are reshaping how we track vitals, chronic conditions and emergencies through RPM technology systems.

Architecture of a generic RPM system | Image Source

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What types of RPM systems are out there? 

• Video-based monitoring

• Internet of Things (IoT) devices

• Cloud computing

• Fog and Edge computing

• Blockchain monitoring

• AI

• Vital sign monitoring

• Patient activity classification

• Chronic disease monitoring

#1 Video-based monitoring

Telehealth apps have seen a rise in popularity after the COVID-19 pandemic as they enabled safe and flexible remote patient monitoring. Patients can now monitor vitals, manage pain and access mental health support all from their smartphones. Over the last decade, these user-friendly applications were enhanced through the integration of machine learning capabilities combined with advanced imaging techniques.  

“Contacting your doctor has never been easier.’’  

AI and imaging tech have made these apps smarter, even detecting stress through breathing patterns. While they improve access and reduce clinic visits, their effectiveness depends on internet access and device availability. Security remains a concern without proper encryption, but AI-powered telehealth continues to play a key role in diagnostics and emergency preparedness.

#2 Internet of Things (IoT) devices

Wearable IoT devices like smart patches and shirts track vitals—heart rate, glucose, motion—in real time, whether at home or in hospital. Connected to cloud systems, they use AI to detect anomalies and personalize care.

“Monitoring that fits! Literally.’’ 

RPM IoT devices bring real-time, flexible and personalized health metrics analytics which have the potential to completely transform health delivery systems. However, to unlock their full innovative potential, we must tackle privacy concerns and empower medical teams with the right knowledge to empower them to use IoT technologies and interpret complex patient data.

#3 Cloud computing

Extensive health metrics captured remotely by IoT RPM devices can be stored in a centralized data server that allows trends to be analyzed and data to be shared between different parties. Cloud computing does not only accelerate innovation but also allows the transmission of high volumes of data from various resources such as servers, databases, networks, software, and intelligence online. This powerful platform can be used as a personalized monitoring system from IoT devices supported by a decentralized network that sometimes outperforms the traditional models.

One example of this would be the deep learning neural network model which classifies patient’s health conditions based on their long short-term memory (LSTM) tracking which resulted in a 97.13% accuracy score.  

#4 Fog and Edge computing

To overcome cloud computing challenges, decentralized computing strategies, such as fog and edge computing, have been incorporated into RPM mechanisms. By bringing the cloud services closer to the IoT devices, data processing and real-time monitoring has become more efficient.

“Closer than ever to your health data.”

Fog computing

Fog computing bridges the gap between IoT devices and the cloud by reducing latency and enabling faster, more secure real-time data processing at the source.

Edge computing

Edge computing brings data processing closer to IoT devices through programmable controllers, allowing instant analysis of patient data. While it improves efficiency, it also raises privacy and security concerns.

Both approaches have the potential to transform remote patient monitoring, provided medical staff are properly trained to use them.

#5 Blockchain monitoring

Blockchain technology answers to the challenges issued from cloud, fog and edge computing services. This decentralized technology offers undisputable security features as it records data transactions between patients and RPM computing technologies (cloud, fog, edge). As the data gets collected through the network system, the blockchain architecture does not allow for any alterations to the data. Yet, this enhanced security feature depends on energy availability and requires high implementation costs.

#6 AI

Traditionally, RPM was aimed at reporting patient’s clinical status by measuring vital signs such as temperature, pulse, respiratory rate, and mean arterial pressure. The healthcare system recognized the importance of early detection of vital signs deterioration as it directly contributes to initiating rapid medical interventions to minimize adverse outcomes in hospitalized patients. This was made possible by implementing the latest AI innovations in healthcare. AI-driven advancements such as machine learning and deep learning are commonly used in RPM technologies to detect and predict vital signs, classify patient’s physical activity, and to monitor chronic illnesses.

#7 Vital sign monitoring

RPM systems utilize AI technology to monitor a patient’s health status by extracting information such as heart rate, pulse rate, respiratory rate, blood pressure and oxygen volume in blood. Among many innovations, two examples worth mentioning are:

• A machine learning model uses smartwatches to continuously monitor vital signs and support decision-making, achieving 99% detection for cardiovascular disease. However, as a black-box model, SVM lacks transparency in explaining results.

• An AI RPM system was built to  track heart rate, blood pressure, oxygen levels, and more. For example, smartwatches combined with SVM models reached high accuracy in detecting cardiovascular disease, while ECG-based models identified atrial fibrillation with over 99% accuracy in under a second.

#8 Patient activity classification

Patients with mobility-related diseases and recurrent fall patterns can benefit from AI-based RPM models designed for activity detection. Out of several advancements, here are two notable examples of their applications. 

• Using machine learning technology combined with a fragmented modification algorithm, Hsieh et al. developed a novel multiphase fall identification algorithm that can identify the 5 fall phases: prefill, free-fall, impact, resting and recovery. The algorithm used both rule-based methods and five machine learning models (SVM, KNN, naive Bayes, decision tree, and adaptive boosting) to test the RPM system’s ability to detect the falling phases.

• Another fall detection device was designed by Salah et al. using a wearable accelerometer to resolve latency, high power consumption and performance issues often seen in areas where the internet is unreliable. This resource-constrained microcontroller applied fog, edge and cloud computing technologies to enable efficient data-handling and transmission to the IoT gateway using long-range communication protocols.

  
  

#9 Chronic disease monitoring

With the help of AI, RPM models have evolved to provide personalized health monitoring for multi-facetted diseases like diabetes or mental health. Below are two examples of such approaches to monitoring disease patterns.  

• A diabetes prediction model can now classify a patient’s disease state by measuring intrinsic factors such as glucose, body mass index (BMI), age, insulin and external variables. To accurately predict diabetes states, various machine learning algorithms were trained and assessed using a classification report and a confusion matrix.

Considering that machine learning can identify new learning patterns in human behavior, these technologies can assist in a patient diagnosis by detecting mental health symptoms and other risk factors. Their advanced predictive capabilities support disease evolution patterns and tailor therapeutic interventions for optimized outcomes. 

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Bottom Line

As these new emerging technologies pave the way for innovative approaches to care delivery, one still wonders why their implementation in the healthcare system remains stalled. What explains the hesitancy behind unlocking the full potential of these technologies? Stay tuned for the upcoming blogs on this topic!