The healthcare industry needs more workers globally. We require skilled individuals in the right places at the right times to help people better.
Healthcare professionals use AI for patient monitoring and other functions. AI Remote Patient Monitoring (RPM) is revolutionizing healthcare by being proactive. It eliminates waiting rooms and check-ups.
AI has transformed healthcare by integrating into many parts of our lives. AI remote patient monitoring is a significant advancement in healthcare. People have been using it in recent years.
This article will discuss the new advancements in AI for Remote Patient Monitoring. These innovations will change healthcare in 2023 and beyond.
Understanding AI-Powered Remote Patient Monitoring
Keeping an eye on your health has never been this smart. Imagine technology tracking your body’s signals and movements instead of a doctor. We’re discussing Remote Patient Monitoring (RPM) — your health companion.
Welcome to the age of the Internet of Things (IoT) and body sensors. Here, advanced wireless communication technologies revolutionize healthcare. We don’t watch your health; we create customized plans for you.
New inventions that can track your health and prevent certain illnesses, keeping you well, showcase the immense potential for AI to benefit healthcare service delivery. Doctors are actively researching how AI can assess disease risk, enhance patient care, and reduce complications.
Patient Monitoring’s Three Essential Sections
A system that uses the Internet of Things can help track patients in real time. Homes and hospitals use IoT systems; sometimes, they can use one system for both.
The typical architecture used for patient monitoring is broken down into three sections by Thanveer Shaik et al., researchers at the University of Southern Queensland.
- Patients wear devices that collect vital signs like temperature, breathing, heart rate, and pulse.
- Cloud services store data and enable machine learning techniques to analyze, predict, or categorize patient data. The method can find abnormal events using vital sign limits and tell medical staff.
- In architectural design, wearable sensors connect to a monitoring network. Reader antennas placed on the patient’s body do this.
Healthcare workers can track patients and solve issues by using wearable technology such as patches and t-shirts featuring this technology.
Remedy Ready with AI
Artificial Intelligence is revolutionizing various aspects of healthcare, including drug discovery, patient care, and cost management. This insight is drawn from May Ohiri’s article in RiseApps.
Efficient Diagnosis and Treatment Planning
AI revolutionizes medical diagnosis, analyzing vast datasets and patient records for quicker and more accurate assessments. Addressing the complexity of disease detection, AI is a game-changer.
Recent studies validate this, showcasing AI’s proficiency in diagnosing lung cancer and tuberculosis, rivaling or surpassing radiologists.
Remote Patient Monitoring and Management
AI streamlines patient monitoring by delving into large data pools. It saves time and shows patients’ activities, making monitoring easier.The audit tools help manage healthcare data by offering quick insights.
Predictive Analytics for Early Intervention
AI systems analyze patient data to predict health problems and complications. Doctors can stop illnesses from spreading and improve how patients recover.
Personalized Medicine and Treatment
AI in healthcare utilizes patient data to create personalized treatment plans and achieve better outcomes. By enhancing treatments, minimizing side effects, and increasing patient satisfaction, AI significantly improves healthcare.
This tailored approach exemplifies the positive impact of AI in the field.
Streamlined Administrative Processes
AI systems can handle administrative tasks like scheduling appointments and managing billing. It helps free up human resources and allows humans to focus on more creative and complex jobs.
In this case medical staff can avoid mistakes and focus on patients when they reduce the workload.
Challenges in Adopting AI for Remote Monitoring Systems
When choosing a patient monitoring system, hospitals should consider their staff’s opinions. Staff support is essential. AI has the potential to transform healthcare by analyzing and classifying data. Yet, there should be more caution in adopting this technology.
There are challenges in using AI for remote monitoring systems. These challenges focus on tracking vital signs and recognizing activity by Thanveer Shaik and colleagues at the University of Southern Queensland.
AI or ML explainability
The primary challenge lies in interpreting results from AI or ML models.
These models are better than humans at reading data and predicting outcomes. Yet, they can’t explain how they conclude—making it hard for healthcare professionals to use them.
Privacy
Neural networks are black-box systems, so predicting what they learn from data is challenging. Users might pick up biases, leading to discrimination against user data. The chance of information exposure rises as a result.
Uncertainty
There are many uncertainties in AI for healthcare, especially in RPM systems. These uncertainties come from data collection, deep neural network construction, and outcome modeling. Accurate data gathering is essential in these systems.
Signal Processing
Without surgery, RPMs face challenges converting RFID tag data from body parts to vital signs. The challenge is to avoid patient contact and deal with noise.
Some RFID devices use frequency hopping, and it can disrupt heart and breathing signals and cause extraction errors.
How Can Healthcare Services Be Improved for Both Patients and Providers?
Improving healthcare services for patients and providers is the aim of RPM applications. To achieve successful integration, addressing the challenges associated with utilizing AI and aligning it with RPM must be addressed. AI outcomes should be more understandable. Approaches to explainable AI are moving in this direction.
We apply explainability strategies like DeepLIFT, LIME, and SHAP to RPM systems. Doctors and nurses must stay updated on new research to make intelligent choices in treatment.
How Can AI in RPM Enhance Healthcare Quality?
AI is playing an increasingly pivotal role in driving transformative changes in health and healthcare, both within and beyond clinical settings. Ongoing exploration of the opportunities and limitations in this field reveals significant challenges.
Researchers are focusing on improving collaboration in learning to enhance data security and privacy, with the utilization of blockchain technology seen as a solution. While blockchain ensures private yet transparent and unchangeable information, it comes with the drawbacks of being expensive and energy-intensive.
Addressing model structure and hyperparameter ambiguity is crucial for enhancing the reliability of findings. The use of Uncertainty Quantification (UQ) techniques during optimization and decision-making can help mitigate uncertainties.
Doctors benefit from various strategies to handle uncertainty, including both probabilistic and nonprobabilistic approaches.
Establishing AI in healthcare necessitates clean and efficient data, with the challenge of balancing classes, addressing issues such as label imbalance or long-tail data.
Reinforcement learning emerges as a promising approach to improving healthcare applications like JITAIs and treatment regimens. Reinforcement learning agents, functioning as virtual robots, can monitor patients and predict rare events.
FAQS
1. Who qualifies for RPM?
The 2021 Proposed Rule mentions that practitioners can use RPM services. These services help them collect and analyze data from patients. They can do this, which works for acute and chronic conditions.
Even though the 2019 PFS final rule defined RPM services, it is clear. These services were for patients with chronic conditions.
2. Who can provide RPM services?
Qualified healthcare professionals, including physicians, can request and administer RPM. Under the billing provider’s general supervision, clinical staff can help and oversee RPM.
3. Who can receive RPM?
RPM services can be provided to patients with acute and chronic conditions as long as they can be effectively managed and the clinician orders or prescribes RPM.
4. What is the most common device used for RPM?
RPM or wired can use medical equipment. Remote patient monitoring devices include blood pressure cuffs, glucose meters, cardiac implants, ECGs, pulse oximeters, scales, thermometers, and wearable blood pressure monitors. These devices offer continuous monitoring and also track activity.
Common RPM devices are blood pressure cuffs, glucose meters, and heart implants. We also have devices to measure heart rate, oxygen levels, weight, temperature, and blood pressure..
5. Do patients need to pay for remote patient monitoring devices?
No. When patients join a remote patient monitoring program, these gadgets are given to them for free. Patients with acute or chronic illnesses will receive suitable health monitoring devices.
Final Check-Up
Over the last ten years, AI and information systems have changed healthcare applications. This is especially true in remote patient monitoring. The RPM systems have improved a lot.
They use data to predict health problems, make personalized apps, and understand behavior. Scientists are studying how AI can track patients’ health, diseases, and emergencies. They use federated and reinforcement learning.
In healthcare, we use advanced AI techniques such as federated and reinforcement learning. Some people worry about privacy and data issues. Infrastructure like cloud, edge, fog, and blockchain supports these techniques.
This is a good time to invest in AI remote patient monitoring research and development. We can address data privacy and security concerns with innovative policies. These policies will help us track patients and improve healthcare worldwide.