AI's Impact in Revolutionizing Continuous Health Surveillance
The accelerated adoption of remote patient monitoring (RPM) has significantly propelled the growth of telehealth, making at-home care a tangible reality. This innovative approach to care delivery has been instrumental in limiting the spread of COVID-19 among vulnerable populations by permitting healthcare professionals to provide patients with acute and chronic illnesses a comprehensive spectrum of care remotely.
In 2021, the market value of remote patient monitoring stood at USD 1.45 billion, with predictions of reaching USD 4.07 billion by 2030, growing at an 8.74% CAGR during the forecast period.
By incorporating RPM into a patient's care plan, physicians can monitor vital signs and health data between visits, enabling prompt responses and treatment adjustments before a patient's condition worsens or necessitates an expensive emergency department (ED) visit. With this valuable health data, healthcare providers can make more informed clinical decisions and provide more efficient and effective care.
Old Versus AI-Enabled RPM Methods
While the concept of remote patient monitoring is familiar and has been in existence for some time, the COVID-19 pandemic has stimulated the installation of new RPM systems in hospitals. Traditional RPM generally involves installing a camera to live-stream video across a network to a separate location for manual monitoring. Although this can aid in patient monitoring, employees must be available to view the live stream at all times. These devices also lack the analytical capabilities based on the data captured from the live stream.
Artificial intelligence (AI) has the potential to revolutionize this situation. It now enables the automation of data collection for various predictive machine learning models due to the use of AI in RPM. To effectively employ AI in RPM scenarios, embedded vision, and camera technology are essential components. Innovative embedded cameras can capture high-quality images regardless of the room's lighting, from high-resolution cameras to NIR cameras and RGB-IR cameras.
One or more acceptable digital imaging solutions are required for remote patient monitoring, allowing healthcare teams to assess patient conditions remotely and healthcare professionals to monitor multiple patients. Remote patient monitoring is evolving as technology advances, from simple monitoring to sophisticated behavioral analysis, such as fall detection, tracking patient movements, and monitoring people in a room.
By analyzing a patient's behavior and categorizing it, healthcare providers can predict and avoid further falls. For instance, systems like PeopleNet are used to analyze patient behavior.
Improving Care Delivery with AI-Enabled RPM
AI and RPM can enhance therapeutic effectiveness, improve outcomes, and increase patient adherence. The following are the four ways that AI-enabled RPM improves care delivery:
Fostering Patient Adherence and Self-Management
Patient adherence is a significant challenge impeding the widespread implementation of RPM. Many patients require more support and encouragement to utilize RPM effectively, and many clinics require more time to monitor patients' adherence. With AI-enabled RPM, a virtual health assistant can communicate with patients via text message to remind them to take their readings and offer support and guidance, resulting in increased patient adherence and retention.
Enhancing Clinical Efficiency
Greater patient compliance results in more frequent readings, providing healthcare providers with more precise and valuable data. With this information, physicians can make well-informed care decisions and tailor treatment to meet patients' specific requirements. A lack of ongoing communication between a patient and a healthcare provider may result in practices going months without engaging with a patient.
Improving Patient Outcomes
Patients suffering from chronic conditions such as hypertension, heart failure, diabetes, and obesity frequently require strict adherence to medications, exercise, and care programs. With AI-enabled RPM, healthcare providers can identify health trends across time and between visits, leading to early interventions, fewer hospitalizations, and better patient outcomes. AI-enabled RPM has been shown to improve patient health measures, including significant reductions in blood pressure, blood glucose, and weight.
Lowering Care Costs
AI-enabled RPM technology can minimize avoidable emergency department visits and inpatient utilization, ultimately lowering care costs by enabling early diagnosis of clinical deterioration and more timely intervention.
Insurance for Remote Patient Monitoring
As more healthcare providers adopt AI-driven RPM tools, remote patient monitoring insurance coverage is becoming increasingly essential. Many insurance plans now offer partial or complete coverage for RPM services, recognizing its role in reducing hospitalizations and emergency department visits. By covering RPM costs, insurers support preventive care, helping manage chronic conditions effectively and reducing overall healthcare expenditures. Expanding RPM insurance options can further accelerate its adoption, making it accessible to more patients, especially those in rural or underserved areas.
- The growth of telehealth, fueled by the adoption of remote patient monitoring (RPM), has opened avenues for health and wellness services in science and technology, particularly in artificial intelligence-enabled RPM.
- In 2021, AI-enabled RPM has the capability to revolutionize traditional RPM methods, which are often limited by manual monitoring and lacking analytical capabilities based on the data captured.
- AI-enabled RPM offers several benefits, such as fostering patient adherence, enhancing clinical efficiency, improving patient outcomes, and lowering care costs, making it an excellent tool for managing chronic conditions effectively.
- Recognizing the importance of RPM in reducing hospitalizations and emergency department visits, many insurance plans now offer coverage for RPM services, which can further accelerate its adoption and make it more accessible to patients, especially in rural or underserved areas.
