How to Choose the Best Remote Patient Monitoring Devices 

How to Choose the Best Remote Patient Monitoring Devices 

by Ree AI Health Tech Med Tech

Remote patient monitoring (RPM) has become a cornerstone of modern healthcare, as the global RPM systems market is projected to be worth over $1.7 billion by 2027. As healthcare providers and patients navigate this growing market, it’s crucial to choose the best remote patient monitoring devices.

This guide will walk you through the key factors to consider when selecting RPM devices, so you can make informed decisions that benefit patients and healthcare teams.

Contents

RPM Basics

The basics of RPM describes the:

  • Definition of remote patient monitoring

  • Benefits for patients and healthcare providers

  • Types of health conditions suitable for RPM

Comparing Different RPM Device Types

RPM devices come in various forms, each with its own strengths and limitations. Let’s explore the main types.

Wearable devices

Elderly hands on smartwatch

Wearable devices like smartwatches and patches offer continuous monitoring with minimal disruption to the patient’s daily life. They’re useful for tracking metrics like heart rate, activity levels, and sleep patterns.

Example: The Apple Watch Series can monitor blood oxygen levels, a feature especially useful for patients with respiratory conditions.

Home-based monitoring systems

These devices are designed for periodic measurements at home. They’re typically used for monitoring vital signs like blood pressure, weight, and blood glucose levels.

For instance, smart scales measure weight and body composition, and some can even detect subtle changes that might indicate fluid retention—a potential sign of heart failure.

Implantable devices

implantable cardioverter-defibrillator

For certain conditions, implantable devices offer the most comprehensive and continuous monitoring. These are typically used for serious cardiac conditions.

Modern implantable cardioverter-defibrillators (ICDs) can monitor heart rhythm continuously and transmit data to healthcare providers, allowing for early detection of potentially life-threatening arrhythmias (Sahu et al., 2023).

Assessing Patient Needs and Preferences

Choosing the right RPM device isn’t just about the technology—it’s about finding a solution that fits the patient’s lifestyle and capabilities.

Consider the patient’s age and tech-savviness

Older man with white hair using tablet

Not all patients are equally comfortable with technology. When selecting an RPM device, consider the patient’s familiarity with digital devices.

For older adults or those less comfortable with technology, look for devices with simple, straightforward interfaces. Some blood pressure monitors, for instance, require just a single button press to take a reading and automatically sync data to a smartphone app.

Evaluate mobility and dexterity requirements

Some patients may have physical limitations that make certain devices harder to use. Consider devices that are easy to handle and don’t require complex movements.

For example, wrist-worn blood pressure monitors can be easier for patients with arthritis to use compared to traditional upper arm cuffs.

Address privacy and security concerns

Many patients are concerned about the privacy and security of their health data. Look for devices and systems that prioritize data protection.

Ensure that the RPM system you choose complies with HIPAA regulations and uses strong encryption methods to protect patient data during transmission and storage.

Key Features to Look for in RPM Devices

When evaluating RPM devices, it’s crucial to focus on several key features that can make or break your experience. 

Data accuracy and reliability

Black woman gold top showing phone with glucose meter on arm

The cornerstone of any effective RPM system is its ability to provide accurate and reliable data. After all, what good is a monitoring device if you can’t trust the information it provides?

Look for devices that have been clinically validated and FDA-approved. These certifications ensure that the device has undergone rigorous testing and meets high standards for accuracy. 

Example: The Dexcom G7 continuous glucose monitor has been shown to have a mean absolute relative difference (MARD) of 8.2%, indicating high accuracy in measuring blood glucose levels.

Ease of use for patients

The success of an RPM program depends in part on patient adherence. If a device is too complicated or cumbersome to use, patients are less likely to use it.

Consider devices with intuitive interfaces and clear instructions. For instance, some blood pressure monitors feature large, easy-to-read displays and one-touch operation, making them ideal for older adults or those with limited dexterity.

Battery life and power options

Nothing’s more frustrating than a device that constantly needs charging or battery replacement. Look for devices with long battery life or convenient charging options.

Some wearable devices, like certain fitness trackers, can last up to a week on a single charge. Others, like certain blood glucose monitors, use replaceable batteries that can last for months.

Connectivity options (Bluetooth, Wi-Fi, cellular)

WiFi signal over city buildings

Consider how the RPM device transmits data. Different connectivity options offer various benefits:

  • Bluetooth: Ideal for short-range communication with smartphones or tablets.

  • Wi-Fi: Allows for direct data transmission to the cloud when in range of a network.

  • Cellular: Offers the most flexibility, allowing data transmission from anywhere with cellular coverage.

For example, some modern pacemakers can transmit data via cellular networks, allowing for continuous monitoring without the need for a separate transmitter.

Compatibility with Existing Healthcare Systems

RPM systems should fit into existing workflows seamlessly. Here’s what to look for.

Integration with electronic health records (EHR)

worker looking at 3 monitors on desk

An RPM system that integrates with your EHR can streamline data management and improve efficiency. Look for systems that offer API integration or direct data transfer to your EHR system.

For instance, some RPM platforms can automatically populate patient data into EHR systems like Epic or Cerner, saving time and reducing the risk of data entry errors.

Data transmission and storage capabilities

Consider how the RPM system handles data transmission and storage. Look for systems that offer:

  • Real-time data transmission

  • Secure cloud storage

  • Custom alerts based on patient data

Some advanced RPM systems use AI algorithms to analyze patient data and predict potential health issues before they become serious.

Compliance with HIPAA and other regulations

Ensuring compliance with healthcare regulations is non-negotiable. Choose RPM systems that are designed with HIPAA compliance in mind.

Look for features like:

  • End-to-end encryption

  • Secure user authentication

  • Audit trails for data access

Remember, HIPAA compliance isn’t just about the technology—it also involves proper training and protocols for staff using the RPM system.

Evaluating Cost and Insurance Coverage

While the benefits of RPM are clear, cost considerations are important for both healthcare providers and patients. 

Initial device costs

The upfront cost of RPM devices can vary widely. Simple devices like blood pressure monitors may cost less than $100, while more advanced systems can run thousands of dollars.

Consider the long-term value rather than just the initial cost. A more expensive device that offers better accuracy and reliability could be more cost-effective in the long run.

Subscriptions and service fees

Calculator

Many RPM systems involve ongoing fees for data storage, analysis, and support. These costs can add up over time, so it’s important to factor them into your decision.

Some providers offer all-inclusive packages that cover the device, data transmission, and analysis for a fixed monthly fee. This can make budgeting more predictable.

Reimbursement options and insurance coverage

The good news is that many insurance plans cover RPM services, including Medicare. However, coverage can vary depending on the specific device and condition being monitored.

Medicare reimburses for RPM services under CPT codes 99453, 99454, 99457, and 99458. Use these codes to cover device setup, data transmission, and time spent on RPM-related care for your Medicare patients.

Assessing Vendor Support and Reliability

The relationship with your RPM vendor doesn’t end when you purchase the system. Ongoing support is crucial for the success of your RPM program. Here’s what to look for.

Customer service and technical support

Customer service reps

Look for vendors that offer comprehensive support, including:

  • 24/7 technical assistance

  • Multiple support channels (phone, email, chat)

  • Resources for patient education

Some vendors even offer dedicated account managers to help healthcare providers optimize their RPM programs.

Device maintenance and updates

RPM technology is constantly evolving. Choose a vendor that provides regular software updates and has a clear process for hardware maintenance or replacement.

For example, some vendors offer automatic over-the-air updates for their devices, ensuring they’re always running the latest software.

Training for healthcare providers and patients

Demo of a CPR mask

The success of an RPM program often hinges on proper training. Look for vendors that offer comprehensive training programs for both healthcare providers and patients.

This may include:

  • In-person or virtual training sessions

  • Online resources and tutorials

  • Ongoing education about new features or best practices

Some vendors even offer patient onboarding services to help get your RPM program up and running smoothly.

Conclusion

Choosing the right RPM system or device involves careful consideration of various factors, from technical specifications to patient needs and regulatory compliance. By focusing on these key areas, you can select an RPM solution that enhances patient care, improves outcomes, and integrates seamlessly with your existing healthcare routine.

The goal is to find devices that monitor health effectively and integrate seamlessly into patients’ lives and your healthcare workflows. Take the time to thoroughly evaluate your options, and don’t hesitate to ask vendors for demonstrations or trial periods before making a decision.

With the right RPM system in place, you can provide more personalized care to your patients, no matter where they are. Stay informed about the latest options so you can make the best choices for your patients and practice. 

References

A Comprehensive Guide to Remote Patient Monitoring (RPM). (2023). Prevounce. Retrieved from https://www.prevounce.com/a-comprehensive-guide-to-remote-patient-monitoring

Krupa, A. Senior monitoring systems: How to find the option that’s best for your loved one. Care. Retrieved from https://www.care.com/c/remote-monitoring-for-seniors/

Sahu, P., Acharya, S., & Totade, M. (2023). Evolution of Pacemakers and Implantable Cardioverter Defibrillators (ICDs) in Cardiology. Cureus, 15(10). doi.org/10.7759/cureus.46389

The technology, devices, and benefits of remote patient monitoring in the healthcare industry. (2023). Emarketer. Retrieved from

https://www.emarketer.com/insights/remote-patient-monitoring-industry-explained

The Impact of AI on Healthcare Cost Reduction and Resource Allocation 

The Impact of AI on Healthcare Cost Reduction and Resource Allocation 

by Ree AI Health Tech

In an era where healthcare costs are skyrocketing, AI can be a game-changer. The impact of AI on healthcare cost reduction and resource allocation has been overwhelmingly positive so far. A recent study by Accenture predicts that AI applications in healthcare could save up to $150 billion annually for the U.S. healthcare economy by 2026. 

Let’s see how AI can help reduce costs and staff human resources more efficiently.

Contents

Understanding AI’s Role in Healthcare Cost Reduction

Definition of AI in healthcare 

Artificial Intelligence (AI) in healthcare uses complex algorithms and software to analyze, interpret, and understand complicated medical and healthcare data. AI technologies such as machine learning (ML), natural language processing (NLP), and predictive analytics integrated into various healthcare processes can enhance efficiency and accuracy.

U.S. healthcare costs

Source: American Medical Association

By 2031, almost 20% of U.S. spending will be on healthcare, which is a lot compared to other wealthy countries.

Healthcare costs are a major burden for families in the U.S. According to the Kaiser Family Foundation, about half of American adults find it difficult to afford healthcare costs. 

24% have had problems paying for healthcare premiums, deductibles, or copays in the past year. That number is 33% for those in poor health. These high expenses often lead to delayed care, skipped medications, and financial strain. 

About 100 million people in America have serious medical debt. They often rely on savings, credit cards, and side jobs to make up the slack. This financial pressure underscores the need for cost-effective solutions.

Helping more people afford health care often means the government spends more money. On the other hand, trying to reduce overall spending might increase costs for individuals. This makes health care policy very challenging, with no easy solutions.

Key areas where AI can impact costs

AI can cut healthcare costs in many ways, such as:

  • Administrative Efficiency: Automating routine tasks such as data entry and claims processing can save time and reduce errors.
  • Diagnostic Accuracy: AI can improve diagnostic accuracy, reducing the need for unnecessary tests and treatments.
  • Predictive Analytics: AI can predict patient outcomes and optimize resource allocation, reducing waste and improving care efficiency.

Labor costs are the greatest expense hospitals have, as shown in the following chart.

Source: American Hospital Association (AHA) and Strata Decision Technology

A McKinsey/EIT Health report shows that tasks by several healthcare occupations can be at least partially automated by 2030, providing more cost savings to healthcare organizations. 

Next, let’s look at how AI can improve resource management in hospitals.

AI-Driven Resource Allocation in Hospitals

Facility management

AI can make hospital buildings run smoother by controlling temperature systems to save energy and keep patients comfortable. It also spots equipment problems early, avoiding breakdowns and saving money on repairs (Varnosfaderani & Forouzanfar, 2024). 

Predictive analytics for patient flow and bed management

Empty recovery room

Managing patient flow and bed use is also key for hospital efficiency. AI-driven predictive analytics can predict patient admissions, discharges, and bed availability, allowing hospitals to optimize their resources. 

Hospitals can manage their emergency services with efficiency if they can predict how many emergency patients will come in. They currently use simple guessing methods based on past patterns. 

Hospitals could use real-time patient data from electronic health records (EHRs) to make short-term predictions about bed needs. This ensures that beds are available when needed, reduces the time patients spend waiting for care, and avoids cancelling planned surgeries (King et al., 2022).

Staff scheduling optimization

Using AI for scheduling can reduce overtime costs and prevents staff burnout, leading to better patient care and lower operational costs.

AI can analyze historical data and predict staffing needs, ensuring that hospitals have the right number of staff at the right times. This includes scheduling medical procedures to maximize the use of operating rooms and staff, while minimizing patient wait times (Varnosfaderani & Forouzanfar, 2024). 

Equipment and supply chain management

AI can streamline equipment and supply chain management by:

  • Studying trends 
  • Predicting demand 
  • Optimizing inventory levels 
  • Automating orders

This reduces the risk of shortages and overstocking to cut waste, save money, and ensure that necessary supplies are always available. In emergencies, AI quickly figures out what’s needed and helps get resources where they’re most important (Varnosfaderani & Forouzanfar, 2024). 

Clinical documentation is ever-present in healthcare. Let’s discuss how AI can streamline admin tasks.

Streamlining Administrative Processes with AI

Doctor on the phone

Automating paperwork and data entry

Administrative tasks like paperwork and data entry take time and are prone to errors. But AI can read and sort different forms and reports quickly. 

AI can automate these processes to save time, free up staff to focus on more critical tasks, and reduce the likelihood of mistakes (Varnosfaderani & Forouzanfar, 2024). 

Improving billing accuracy and reducing errors

It takes time and expense to fix billing errors. A study in the insurance industry showed that ML can improve insurance estimates better than traditional methods (Baudry & Robert, 2019). 

AI can improve hospital billing and insurance claim accuracy by automating the coding process and identifying discrepancies before they become issues. This leads to quicker reimbursements and fewer denied claims.

Enhancing insurance claims processing

AI can streamline the insurance claims process by automating the verification and approval of claims. This reduces the time it takes to process claims and improves customer satisfaction by minimizing delays and errors.

Beyond administrative tasks, AI is also making significant strides in improving patient care and treatment.

AI in Diagnostic Accuracy and Treatment Planning

Brain scans

Reducing misdiagnosis rates and associated costs

Misdiagnoses can lead to unnecessary treatments and additional costs. AI can analyze medical data with high accuracy, reducing the likelihood of misdiagnoses and ensuring that patients receive the correct treatment the first time (Khanna et al., 2022).

Personalized treatment recommendations

AI can provide personalized treatment recommendations based on a patient’s medical history and current condition. This ensures that patients receive the most effective treatments, improving outcomes and reducing costs associated with trial-and-error approaches (Alowais et al., 2023).

Early disease detection and prevention strategies

Early detection of diseases can significantly reduce treatment costs and improve patient outcomes. AI can analyze large datasets to identify early signs of diseases, allowing for timely interventions and preventive care (Alowais et al., 2023).

AI can also help diagnose illnesses and assess symptoms with virtual methods in telemedicine and telehealth.

Telemedicine and Remote Patient Monitoring

Phone with chatbot conversation

AI-powered virtual health assistants

Virtual health assistants powered by AI can provide patients with medical advice, schedule appointments, and answer health-related questions. This reduces the need for in-person visits and allows healthcare providers to focus on more complex cases.

Chronic disease management via remote monitoring

AI can monitor patients with chronic diseases remotely, also called remote patient monitoring (RPM). When AI analyzes data from wearable devices, it can notify healthcare providers about any concerning changes to trigger an alert. This proactive approach reduces hospital visits and readmissions, saving costs and improving patient quality of life.

Reducing unnecessary hospital visits and readmissions

By providing continuous monitoring and early intervention, AI can help prevent complications that would otherwise require a patient to return to the hospital. This not only improves patient outcomes, but also reduces the strain on healthcare facilities.

Challenges and Considerations in AI Implementation

Doctor shows tablet to nurse

Initial investment and integration costs

Implementing AI in healthcare requires a high upfront investment in technology and training. While the long-term benefits can outweigh these costs, the initial financial burden can be a barrier for some healthcare providers.

Data privacy and security concerns

AI systems handle vast amounts of sensitive patient data, raising concerns about privacy and security. To implement these systems successfully, healthcare organizations must comply with regulations and protect patient information (Alowais et al., 2023).

Workforce adaptation and training needs

Integrating AI into healthcare workflows requires training staff to use new technologies effectively. This can be challenging, particularly for those who are less familiar with digital tools. Ongoing education and support are essential to ensure that healthcare professionals can leverage AI to its full potential (Alowais et al., 2023). 

Future Outlook: AI’s Long-term Impact on Healthcare Economics

Projected cost savings and efficiency gains

AI has the potential to generate significant cost savings and efficiency gains in healthcare. By automating routine tasks, improving diagnostic accuracy, and optimizing resource allocation, AI can reduce operational costs and enhance patient care (Khanna et al., 2022).

Potential shifts in the healthcare job market

Integrating AI in healthcare systems causes a shift in the job market. While some administrative roles may become redundant, new opportunities will emerge in AI development, data analysis, and technology management. Healthcare professionals will need to adapt to these changes and acquire new skills.

Ethical considerations and policy implications

The use of AI in healthcare raises ethical considerations, such as ensuring fairness in AI algorithms and addressing potential biases. Policymakers should establish guidelines and regulations to ensure that we use AI responsibly and equitably in healthcare (Alowais et al., 2023).

Conclusion

AI’s impact on cost reduction and resource allocation in healthcare is profound and far-reaching. From streamlining administrative tasks to enhancing diagnostic accuracy, AI technologies are valuable allies in the quest for more efficient and affordable healthcare. Successful implementation will require careful planning, ethical considerations, and a commitment to ongoing innovation. 

As AI continues to evolve, its long-term impact on healthcare economics will depend on how effectively these challenges are addressed and how well healthcare providers can integrate AI into their workflows. By embracing AI responsibly, healthcare providers can work towards a future where high-quality care is more accessible and affordable for all.

References

Alowais, S. A., Alghamdi, S. S., Alsuhebany, N., Alqahtani, T., Alshaya, A. I., Almohareb, S. N., Aldairem, A., Alrashed, M., Saleh, K. B., Badreldin, H. A., Al Yami, M. S., Harbi, S. A., & Albekairy, A. M. (2023). Revolutionizing healthcare: The role of artificial intelligence in clinical practice. BMC Medical Education, 23. doi.org/10.1186/s12909-023-04698-z 

Altman, D. (2024). The Two Health Care Cost Crises. Kaiser Family Foundation. Retrieved from https://www.kff.org/from-drew-altman/the-two-health-care-cost-crises/

America’s Hospitals and Health Systems Continue to Face Escalating Operational Costs and Economic Pressures as They Care for Patients and Communities. (2024). American Hospital Association (AHA). Retrieved from https://www.aha.org/costsofcaring

Baudry M., & Robert C.Y. (2019). A machine learning approach for individual claims reserving in insurance. Applied Stochastic Models in Business and Industry; 35:1127–1155. doi:10.1002/asmb.2455

Collier, M., & Fu, R. (2020). AI: Healthcare’s new nervous system. Accenture. Retrieved from https://www.accenture.com/au-en/insights/health/artificial-intelligence-healthcare

Hasa, I. (2024). From Data to Decisions: AI-driven Healthcare Resource Optimization. LinkedIn Pulse. Retrieved from https://www.linkedin.com/pulse/from-data-decisions-ai-driven-healthcare-resource-inamul-hasan-m-sc–stzaf

Khanna, N. N., Maindarkar, M. A., Viswanathan, V., E Fernandes, J. F., Paul, S., Bhagawati, M., Ahluwalia, P., Ruzsa, Z., Sharma, A., Kolluri, R., Singh, I. M., Laird, J. R., Fatemi, M., Alizad, A., Saba, L., Agarwal, V., Sharma, A., Teji, J. S., Al-Maini, M., . . . Suri, J. S. (2022). Economics of Artificial Intelligence in Healthcare: Diagnosis vs. Treatment. Healthcare, 10(12). https://doi.org/10.3390/

King, Z., Farrington, J., Utley, M., Kung, E., Elkhodair, S., Harris, S., Sekula, R., Gillham, J., Li, K., & Crowe, S. (2022). Machine learning for real-time aggregated prediction of hospital admission for emergency patients. Npj Digital Medicine, 5(1), 1-12. doi.org/10.1038/s41746-022-00649-y

Lopes L., Montero A., Presiado, M., & Hamel, L. (2024). Americans’ Challenges with Health Care Costs. Kaiser Family Foundation (KFF). Retrieved from https://www.kff.org/health-costs/issue-brief/americans-challenges-with-health-care-costs/

M, N. (2023). Artificial Intelligence (AI) in Healthcare Claims Processing. Nanonets. Retrieved from https://nanonets.com/blog/ai-healthcare-claims-processing/

McDill, V. (2024). New Study Will Explore Whether Artificial Intelligence Reduces Healthcare Spending and Impacts Health Outcomes. University of Minnesota School of Public Health. Retrieved from https://www.sph.umn.edu/news/new-study-will-explore-whether-artificial-intelligence-reduces-healthcare-spending-and-impacts-health-outcomes/

Spatharou, A., Hieronimus, S., & Jenkins, J. (2020). Transforming healthcare with AI: The impact on the workforce and organizations. McKinsey & Company. Retrieved from https://www.mckinsey.com/industries/healthcare/our-insights/transforming-healthcare-with-ai

Trends in health care spending. (2024). American Medical Association (AMA). Retrieved from https://www.ama-assn.org/about/research/trends-health-care-spending

Varnosfaderani, S. M., & Forouzanfar, M. (2024). The Role of AI in Hospitals and Clinics: Transforming Healthcare in the 21st Century. Bioengineering, 11(4). doi.org/10.3390/bioengineering11040337