PME | Ushering in an era of precision medicine with patient monitoring

The pandemic highlighted the need to strengthen health infrastructure globally and patient monitoring technology emerged as the panacea to the infrastructural gaps in the system.

Patient monitoring devices have emerged as a critical component in the rapidly evolving landscape of healthcare, which have been instrumental in ensuring optimal care and improved patient outcomes.

Patient monitoring devices encompass a wide range of technologies. Apart from hemodynamic monitoring, neuromonitoring, cardiac monitoring, fetal & neonatal monitoring, respiratory monitoring, multi-parameter monitoring, remote patient monitoring, weigh monitoring, and temperature monitoring devices they include wearable sensors, and mobile health apps. These devices enable healthcare providers to collect and track real-time data on patients’ vital signs, such as heart rate, ECG, blood pressure, and oxygen levels.

The availability of data creates an invaluable repository of information that offers valuable insights about patients’ health status to the medical practitioner, allowing for timely interventions and personalised care. Additionally, the data collected from these devices serve as a valuable resource for further drug research, monitoring disease patterns and epidemics, and conducting medical tests and surveys.

By harnessing the synergy of wearable sensors, IoT, and AI, the ascent of remote patient monitoring (RPM) systems has been swift, and certain trends will shape the next chapter of RPM’s evolution.

Anticipated trends include the integration of telehealth, chronic, and acute monitoring, continued growth of Hospital-at-Home (HaH) initiatives, as well as user-centric advancements in wearable technology. These trends provide an insightful glimpse into remote healthcare services’ dynamic development.

Prior to the pandemic, telehealth emerged as a basic service and was used to monitor patients. The early RPM phase focused on chronic care management for conditions, such as diabetes and hypertension. Through the use of blood pressure cuffs and weight scales, RPM added tangible measurements to telehealth.

The use of RPM is shifting to acute care, employing real-time sensors to construct a comprehensive 24/7 patient picture. In 2023, we witnessed instances of this shift, with RPM vendors providing real-time continuous monitoring — a significant component of acute RPM.

The evidence base on remote monitoring, particularly for RPM tools, is growing. Yet some policy experts cite a lack of robust evidence on the optimal use of remote monitoring, including its duration and target patient groups. In the absence of such evidence, these experts question whether we are effectively “rightsizing” the use of these services. Underuse could limit access to beneficial care, while overuse could unnecessarily increase spending in government healthcare programs. Additionally, providers cite the need for tools—such as generative artificial intelligence (AI)—to manage streams of data, otherwise the volume of patient-generated information can become overwhelming and unmanageable.

Integration of AI in remote patient monitoring technology has ushered in a new era of healthcare, making it more patient-centric and enabling a shift from reactive to proactive care. AI techniques in RPM:

Machine learning (ML) uses AI algorithms to learn from the data points it collects and in the context of RPM, it can learn to recognize patterns and trends in a patient’s health data. ML techniques in RPM can be effective for tasks like predictive analytics, anomaly detection, and risk stratification.

Deep learning algorithms, a powerful facet of artificial intelligence, play a pivotal role in remote patient monitoring. By training computer systems to discern complex patterns in healthcare data, they enable real-time identification of crucial health indicators. This advanced technology facilitates prompt intervention by medical professionals upon detection of abnormalities, offering a vital tool in ensuring patient well-being.

Reinforcement learning offers significant potential in remote patient monitoring by enabling computers to learn from healthcare data and feedback, much like humans learn from trial and error. With this approach, computers can continuously improve their ability to make personalized recommendations for patient care, such as adjustments in medication or daily routines.

Anomaly detection in healthcare involves learning individual patterns of vital signs from past data and monitoring them in real time. When significant deviations occur, such as sudden changes in heart rate or oxygen levels, alerts are raised to prompt healthcare intervention.

The remote monitoring of patients using IoT is crucial for continuous observation, enhancing healthcare, and reducing associated costs by minimizing hospital admissions and emergency visits. To address this, a cloud-based patient monitoring model is proposed, integrating IoT-generated data collection, storage, processing, and visualization. This model comprises three main modules: sensing, network, and application. The sensing module employs edge processing techniques to handle large data volumes efficiently, allowing for real-time monitoring of vital signs and environmental parameters for multiple patients simultaneously.

The network module, hosted in the cloud, facilitates data processing and sharing, offering potential enhancements through AI and ML integration for advanced analysis and decision-making.

The application module connects physicians, patients, and caretakers via user-friendly interfaces, ensuring scalability and adaptability.

Moreover, future research focuses on integrating AI and ML modules, ensuring data privacy compliance, analyzing clinical aspects of collected data, and enhancing the model’s capabilities in monitoring additional health indicators while increasing its patient handling capacity.

In recent years, wearable sensors utilizing two-dimensional (2D) materials have made significant strides in detecting physiological and biochemical markers for telehealth applications. These sensors, capable of monitoring vital signs like body temperature, arterial oxygen saturation, and breath rate, offer promising potential for early disease detection.

The development of 2D material-based wearable sensors, highlighting their flexibility, mechanical stability, sensitivity, and accuracy for remote health monitoring. The various types of wearable sensors, including pressure, strain, electrochemical, optoelectronic, and temperature sensors, detailing their operation mechanisms, integration with electronic circuits, and applications in human body monitoring with challenges such as environmental stability and mass production, including low-cost fabrication techniques and AI-based decision-making systems.

But designing new wearable sensors utilizing 2D material highlights the promising role of wearable sensors in revolutionizing remote healthcare monitoring.

A novel DNA-based encryption technique has been proposed to address the critical need for enhanced security in patient monitoring equipment, particularly within Wireless Body Area Networks (WBANs).

As programmable object interfaces gain traction among researchers, the application of such technology in the medical realm, utilizing clinical nano sensors for health monitoring, underscores the necessity for robust data protection measures. Traditional security schemes, while effective, often incur elevated time complexity, leading to slower data transmission.

Moreover, the integration of cognitive radio technology within WBAN systems introduces additional vulnerabilities to security breaches. In response to these challenges, the proposed DNA-based encryption method offers a promising solution.

This technique boasts reduced computational time during authentication, encryption, and decryption processes, enhancing overall system efficiency. Comparative analyses against existing methodologies demonstrate the superiority of the DNA-based approach, showcasing its ability to mitigate security risks while maintaining optimal performance.

By leveraging DNA-based encryption alongside CRN protocols, the proposed method ensures secure data sharing, effectively safeguarding against interference and malicious attacks. Notably, the system yields significant improvements in complexity, time, and energy consumption, thereby enhancing the reliability and efficiency of patient monitoring equipment.

As future research endeavors seek to expand upon this innovative approach, the potential for DNA-based operations to revolutionize data security in medical settings remains promising, paving the way for further advancements in patient care and monitoring technology.

Outlook
The rapid evolution of patient monitoring technologies, driven by advancements in AI, IoT, and wearable sensors, has significantly transformed healthcare delivery. These innovations have not only revolutionized patient care but also paved the way for proactive health management and precision medicine. They hold a promise for further enhancing predictive analytics, personalized interventions, and remote patient monitoring capabilities.

Moreover, the emphasis on data privacy and security measures underscores the importance of safeguarding patient information in an increasingly interconnected healthcare ecosystem.

Continued research and innovation will drive the evolution of patient monitoring, ushering in an era of precision medicine and proactive health management.