5 Best Remote Patient Monitoring Systems for Healthcare Providers

5 Best Remote Patient Monitoring Systems for Healthcare Providers

by Ree AI Health Tech Med Tech

Remote patient monitoring (RPM) has become an essential tool for healthcare providers, allowing them to track patient health data outside of traditional clinical settings. The use of remote patient monitoring systems for healthcare providers continues to grow, offering benefits such as improved patient outcomes, reduced hospital readmissions, and enhanced chronic disease management.

This article explores the top RPM systems helping healthcare providers deliver more efficient and personalized care. We’ll discuss the key features to look for and provide guidance on implementing these systems in your practice.

Contents

Features to Look for in Top RPM Systems

When evaluating RPM systems, several key features can make a significant difference in their effectiveness and usability. Here are the essential elements to consider.

Real-time data collection and transmission

Hand touches a screen displaying a heartbeat

Collecting and transmitting patient data in real time is crucial for timely interventions and effective care management. Look for systems that offer:

  • Continuous monitoring capabilities

  • Minimal latency in data transmission

  • Automatic data syncing between devices and the central platform

User-friendly interfaces for both patients and providers

Ease of use is paramount for both patients and healthcare providers. A good RPM system should have:

  • Intuitive mobile apps for patients

  • Clear, easy-to-read dashboards for providers

  • Customizable views and reports

Integration with existing electronic health record systems

Seamless integration with your current electronic health record (EHR) system can streamline workflows and improve data consistency. Consider systems that offer:

  • Bi-directional data flow between the RPM platform and EHR

  • Single sign-on capabilities

  • Automated data entry to reduce manual work

Data security and HIPAA compliance

Protecting patient data is non-negotiable. Ensure the RPM system you choose has:

  • End-to-end encryption for data transmission and storage

  • Multi-factor authentication for user access

  • Regular security audits and updates

Customizable alerts and notifications

Heart illustration for ECG monitor

Timely alerts can help providers intervene before a patient’s condition worsens. Look for systems with:

  • Configurable alert thresholds

  • Multiple notification methods (e.g., SMS, email, in-app notifications)

  • Escalation protocols for critical alerts

Top Remote Patient Monitoring Systems for Healthcare Providers

Now that we’ve covered the essential features, let’s dive into some of the top RPM systems available to healthcare providers in 2024.

1. HealthSnap

Healthsnap RPM system

HealthSnap is a comprehensive Virtual Care Management Platform facilitating integrated, continuous remote patient care with chronic care management (CCM). The system is designed to improve health outcomes for patients with chronic conditions.

Key Features:

  • Cellular-enabled, pre-configured health devices

  • Automated data transmission

  • Integrated platform for monitoring and managing chronic diseases
ProsCons
Easy setup with no Wi-Fi requiredMay have higher upfront costs
Proven to improve patient outcomesLimited to specific chronic conditions
Transparent performance metrics

Use case 

A primary care practice uses HealthSnap to monitor patients with hypertension, diabetes, and obesity. The cellular-enabled devices allow for easy adoption among elderly patients who may not have reliable internet access.

To learn more, visit:

Healthsnap

2. Optimize Health

Optimize Health RPM system

Optimize Health offers a comprehensive RPM solution that focuses on improving patient outcomes with CCM, while maximizing reimbursements for healthcare providers.

Key Features:

  • Customizable RPM programs

  • Integrated billing and reimbursement support

  • Patient engagement tools
ProsCons
Flexible program optionsMay require more setup time
Strong focus on ROILearning curve for customization
Robust patient engagement

Use case

A cardiology practice implements Optimize Health to monitor patients with heart failure, resulting in reduced hospital readmissions and improved medication adherence.

To learn more, visit:

Optimize Health

3. Athelas

Athelas Home RPM system

Athelas provides an AI-powered RPM system with a focus on simplicity and preventative care. Their at-home blood diagnostics device is designed to identify health concerns early, potentially reducing the need for hospitalizations.

Key Features:

  • SIM-connected devices for easy setup

  • Nurse-monitored health readings

  • Preventative approach to patient care
ProsCons
Simple device setupLimited device options
Professional monitoringMay have ongoing monitoring costs
Early intervention focus

Use case

A rural health clinic uses Athelas to monitor patients with multiple chronic conditions, leveraging the nurse-monitored system to extend their care team’s capabilities.

To learn more, visit:

Athelas

4. Health Recovery Solutions 

Health Recovery Solutions RPM system

Health Recovery Solutions (HRS) offers a clinically-focused RPM platform designed to improve patient satisfaction, reduce hospital readmissions, and optimize clinical workflows.

Key Features:

  • Comprehensive suite of RPM solutions

  • Electronic medical records (EMR) integration

  • 24/7 customer and tech support
ProsCons
Proven results over 10 yearsMay be more complex for smaller practices
Customizable solutionsHigher-end pricing
Strong support system

Use case 

A large health system implements HRS across multiple specialties, using the platform’s customization options to tailor the RPM program for each department’s needs.

To learn more, visit:

Health Recovery Solutions

5. Accuhealth

Accuhealth RPM system

Accuhealth provides a user-friendly RPM platform that emphasizes ease of use for both patients and providers.

Key Features:

  • Intuitive patient and provider interfaces

  • AI-powered risk stratification

  • Integrated telehealth capabilities
ProsCons
Easy to use for all agesMay have fewer advanced features
AI-enhanced patient monitoringLimited customization options
Built-in telehealth

Use case 

A family medicine practice adopts Accuhealth to monitor patients with diabetes, using the integrated telehealth feature for quick follow-ups when blood glucose levels are out of range.

To learn more, visit:

Accuhealth

Implementing RPM in Your Healthcare Practice

Implementing an RPM system in your practice requires careful planning and execution. Here are some key steps and considerations.

nurse and doctor pointing at computer

How to choose the right RPM system

Follow these steps to choose the right RPM system for your healthcare practice.

  1. Assess your practice’s needs and goals.

  2. Evaluate potential systems based on their key features.

  3. Request demos from top contenders.

  4. Consider scalability and future needs.

  5. Review pricing models and ROI potential.

Train staff and patients on using the system

Healthcare staff and patients must understand how to use their RPM systems correctly. Steps to take include:

  • Develop a comprehensive training program for your staff.

  • Create easy-to-follow guides for patients.

  • Offer ongoing support and refresher training.

  • Consider designating RPM champions within your practice.

Overcoming common implementation challenges

Best practices for successful RPM adoption

  • Start with a pilot program and gradually expand.

  • Regularly collect and act on feedback from staff and patients.

  • Monitor key performance indicators to measure success.

  • Stay up-to-date with RPM technology advancements and regulations.

By carefully considering these factors and following best practices, you can successfully implement an RPM system that enhances patient care and improves your practice’s efficiency.

Conclusion

Remote patient monitoring systems are rapidly becoming essential tools for healthcare providers seeking to improve patient outcomes and streamline operations. 

By choosing the right RPM solution, you can enhance the quality of care, reduce hospital readmissions, and empower patients to take an active role in managing their health. Take the first step towards a more connected and efficient practice by exploring the top RPM systems available today.

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

Population Health Management Strategies with AI

Population Health Management Strategies with AI

by Ree AI Health Tech

Population health management (PHM) is key to effective healthcare. Using population health management strategies with AI creates new ways to help patients. In a 2023 study by Deloitte, 69% of people using generative AI said it could improve healthcare access, and 63% said it could make healthcare more affordable. 

This article explores cutting-edge insights on how this PHM-AI combo enhances patient care, reduces costs, and improves overall health outcomes across diverse communities.

Let’s first define PHM and how AI fits into this approach.

Contents

Understanding AI in Population Health Management

PHM diagram

What is Population Health Management?

PHM focuses on improving the health outcomes of a group by monitoring and identifying individual patients within that group. The primary goals of PHM are:

What’s the difference between PHM and public health?

Don’t confuse population health with public health. Public health tries to stop diseases and injuries before they happen, by:

  • Teaching people about health
  • Reaching out to communities
  • Doing research
  • Changing standards or laws to make health-related matters safer

Population health issues 

Things that affect community health range from physical to social, such as:

  • Environmental factors (like pollution)
  • Income and education levels
  • Gender and racial inequality
  • Social connections
  • Community involvement
  • Access to clean water

People working in population health need to understand how these factors affect communities and interact with each other. For example, low-income groups might struggle to access healthy food or safe places to exercise, even if these are available nearby. Understanding these connections can help us create better strategies to improve overall community health (Tulane University, 2023).

How AI enhances PHM

AI technologies, such as machine learning and predictive analytics, can process large datasets quickly and accurately. AI is a great asset in PHM because it can find at-risk individuals more quickly and accurately. This can help healthcare providers create better intervention strategies to improve patient outcomes, manage chronic diseases, and prevent illnesses. 

The key benefits of integrating AI into PHM include:

  • Improved accuracy: AI can analyze complex datasets to identify patterns that may be missed by human analysts.
  • Efficiency: Automated processes reduce the time and effort required for data analysis.
  • Personalization: AI can tailor interventions to individual patient needs, improving outcomes.

Companies using big data for PHM

Another PHM diagram

Some examples of companies offering data solutions for health systems:

  • 1upHealth – They created Population Connect, which makes it easier to get and share health data, and cuts down on paperwork and manual tasks. It also gives clinicians a full picture of their patients’ health.
  • Arcadia – Arcadia’s software tracks patient health over time and makes care notes easy to find. The system constantly updates, helping teams set goals and measure their progress for different patient groups.
  • Amitech – Amitech uses health information to manage community health. They combine physical and mental health data to spot risks and get patients more involved in their own care.
  • Linguamatics – Their platform uses natural language processing (NLP) to find hidden data in health records to improve community health. They use smart tech to analyze patient notes, predict health risks, and find patients who need extra care.
  • Socially Determined – This company helps healthcare groups understand social risks, called social determinants of health (SDoH). Their SocialScape platform measures things like patient housing and food access, which can help health providers create better care plans for different communities.

One of the most powerful applications of AI in PHM is its ability to identify and predict health risks across populations.

Risk Stratification and Predictive Analytics using AI

Risk stratification involves categorizing patients based on their risk of developing certain conditions. Predictive analytics uses historical data to indicate future health outcomes. Together, these techniques enable proactive healthcare management.

Identifying high-risk individuals

AI algorithms can analyze electronic health records (EHRs), lab results, and other data sources to identify individuals at high risk for conditions such as diabetes, heart disease, or chronic obstructive pulmonary disease (COPD). 

For example, the PRISM model provides individual risk scores and stratifies patients into different risk levels based on their health data (Snooks et al., 2018).

Predictive modeling

Predictive modeling uses AI to forecast disease progression and health outcomes. For instance, AI can predict which patients are likely to develop complications from chronic diseases, allowing for early intervention. 

Researchers at Cedars-Sinai Medical Center developed an AI algorithm to measure plaque in arteries. They found that AI algorithms could predict heart attacks within 5 years by analyzing coronary CTA images. This significantly reduced the time required for diagnosis (Lin, et al., 2022).

In another example, Stanford University used AI to monitor ICU patients’ mobility, improving patient outcomes by alerting staff to potential issues (Yeung et al., 2019).

With AI’s ability to analyze large amounts of data, healthcare providers can now create highly tailored care plans for individuals within a population.

Personalized Interventions and Care Plans

Personalized care plans are tailored to meet the specific needs of individual patients. AI algorithms can analyze patient data to recommend the best treatments and interventions. Let’s look at some of those applications.

People in waiting room wearing face masks

Tailoring interventions

AI can analyze various data points, including genetic information, lifestyle factors, and medical history, to create personalized care plans. For example, machine learning algorithms can recommend specific medications or lifestyle changes based on a patient’s unique profile.

Treatment recommendation systems

AI-powered treatment recommendation systems can help healthcare providers choose the best treatments for their patients. These systems use data from clinical trials, patient records, and medical literature to provide evidence-based recommendations.

Balancing personalization with population-level strategies

While personalization is crucial, it’s also essential to consider population-level strategies. AI can help balance these by identifying common trends and patterns within a population, allowing for targeted interventions that benefit individuals and the broader community.

Remote monitoring and telehealth integration

Remote patient monitoring (RPM) and telehealth technologies are important when managing population health. For example, AI can analyze data from wearable health devices, such as heart rate monitors and glucose sensors, to detect early signs of health issues. This allows for timely interventions and reduces the need for hospital visits.

Telehealth platforms

Elderly woman on Zoom with health provider

Telehealth platforms enhanced by AI can provide virtual consultations, remote diagnostics, and personalized treatment plans. These platforms help address healthcare access disparities by providing services to rural and underserved communities. By providing remote consultations and monitoring, these technologies reduce the need for travel and make healthcare more accessible.

Overcoming data silos

Effective population health management requires data from various sources. However, data silos and interoperability issues can hinder this process.

Organizations often manage risks in various silos by department. This makes it difficult to see all the risks in the organization, and also makes it tough to create plans that work together to reduce these risks.

AI can help break down data silos by standardizing and integrating data from different sources. This ensures that healthcare providers have a comprehensive view of patient health.

Standardizing and analyzing diverse health data

AI solutions can standardize data formats and analyze diverse datasets, making it easier to identify trends and patterns. This improves the accuracy and efficiency of population health management strategies.

Ensuring data privacy and security

Data privacy and security are critical in AI-driven PHM. Robust encryption methods and secure data storage solutions are essential to protect patient information.

Beyond medical data, AI can also incorporate socioeconomic and environmental factors that significantly impact health outcomes.

Social Determinants of Health and AI

Things like money, education and where people live affect their health. These are called SDoH. AI can incorporate these factors into predictive models to predict health problems and find people who might need help. This lets healthcare providers make better plans to keep communities healthy.

Social determinants of health diagram

Incorporating social and environmental factors

AI algorithms can analyze data on SDoH such as income, education, and housing conditions, to predict health outcomes and identify at-risk populations.

Predictive analytics for SDoH

Predictive analytics can help healthcare providers develop targeted interventions to address SDoH. For example, AI can identify communities at risk for certain diseases and recommend preventive measures.

Collaborative AI Approaches for community health improvement

Collaborative AI approaches involve partnerships between healthcare providers, community organizations, and technology companies to improve community health. These collaborations can lead to more effective and sustainable health interventions.

Now that we understand SDoH and ways to deal with them, it’s crucial to track how effective those efforts are, and continuously improve our approaches.

Measuring and Improving Population Health Outcomes

Measuring and improving population health outcomes requires continuous monitoring and refinement of strategies. AI-powered tools can provide real-time insights and help healthcare providers make data-driven decisions.

AI-powered dashboards and visualization tools

Dashboards and visualization tools using AI can display population health metrics in an easily understandable format. These tools help healthcare providers track progress and identify areas for improvement.

Continuous learning systems

Continuous learning systems use AI to analyze new data and refine PHM strategies. This ensures that interventions remain effective and relevant over time.

Ethical considerations for patient data

Ethical considerations are crucial when using AI with PHM. Ensuring that AI algorithms are free from bias and that patient data is used responsibly is essential for maintaining trust and achieving equitable health outcomes.

Conclusion

Combining AI with population health management is a big step forward in taking care of communities better and faster. AI helps healthcare providers spot and solve health problems early, instead of waiting until people get sick, by:

  • Predicting health issues before they happen
  • Creating personalized care plans
  • Using data to make smarter decisions

As we get better at using AI in healthcare, we can:

  • Help more people stay healthy
  • Lower the cost of healthcare
  • Improve life for whole communities

We’re just starting to use AI in population health management. Healthcare leaders and policymakers need to use these AI tools. It’s not just a choice – it’s necessary to build healthier communities that can handle health challenges better.

Robot looking at the globe in black

References

Dhar, A., Fera, B., & Korenda, L. Can GenAI help make health care affordable? Consumers think so. (2023). Deloitte. Retrieved from https://www2.deloitte.com/us/en/blog/health-care-blog/2023/can-gen-ai-help-make-health-care-affordable-consumers-think-so.html

Lin, A., et al. (2022). Deep learning-enabled coronary CT angiography for plaque and stenosis quantification and cardiac risk prediction: an international multicentre study. The Lancet. doi.org/10.1016/S2589-7500(22)00022-X

Population Health Management: A Healthcare Administration Perspective. (2023). Tulane University. Retrieved from https://publichealth.tulane.edu/blog/population-health-management/

Predictive Analytics for Risk Management: Uses, Types & Benefits. (n.d.). PREDIK Data-Driven. Retrieved from https://predikdata.com/predictive-analytics-for-risk-management/

Snooks, H., Bailey-Jones, K., & Burge-Jones, D., et al.. (2018). Predictive risk stratification model: a randomised stepped-wedge trial in primary care (PRISMATIC). Southampton (UK): NIHR Journals Library; (Health Services and Delivery Research, No. 6.1.) Chapter 1, Introduction. https://www.ncbi.nlm.nih.gov/books/NBK475995/

Yeung, S., Rinaldo, F., Jopling, J., Liu, B., Mehra, R., Downing, N. L., Guo, M., Bianconi, G. M., Alahi, A., Lee, J., Campbell, B., Deru, K., Beninati, W., & Milstein, A. (2019). A computer vision system for deep learning-based detection of patient mobilization activities in the ICU. Npj Digital Medicine, 2(1), 1-5. doi.org/10.1038/s41746-019-0087-z