Enhancing Research with Wearables in Clinical Trials

Enhancing Research with Wearables in Clinical Trials

by Ree AI Health Tech Med Tech

As clinical trials grow in number and complexity, wearables are becoming essential. They allow for remote patient monitoring (RPM) and can track multiple health metrics at once. This is crucial as the number of trial endpoints has increased by 10% in the last ten years. Let’s explore how using wearables in clinical trials helps accelerate medical research.

Contents

Wearables in Medical Research

What are wearables?

Wearables are small, smart devices like sensors that, combined with apps, collect health data. These devices can track everything from your heart rate to how well you sleep. They’re like having a mini-lab on your wrist or body. 

Wearables in clinical trials refers to all types of medical tech used in medical research.

Types of wearable devices used in clinical trials

Black woman gold top showing phone with glucose meter on arm

There’s a whole range of wearables being used in medical research:

The popularity of wearables in research

Wearables are taking the medical research world by storm. The use of wearables in clinical trials has grown by 50% from 2015 to 2020 (Marra et al., 2020). 

Wearable devices make collecting health data easier for medical researchers. They allow for real-time analysis of large data sets and help identify health trends, which brings ease and precision to clinical trials and medical studies.

Benefits of Using Wearables in Clinical Trials

Why are researchers so excited about wearables? Let’s break it down.

Real-time data collection and monitoring

Monitoring dashboard on a desk

Imagine getting a constant stream of health data from patients, 24/7. Wearables allow clinicians to monitor real-time data, so there’s no more waiting for patients to come in for check-ups or relying on their memory of symptoms.

Improved patient engagement and compliance

People are more likely to stick with a study when they’re using familiar devices. RPM systems often include medication reminders and tracking features, which can significantly improve adherence rates. 

Enhanced accuracy and objectivity of data

Wearables don’t forget or exaggerate. They provide hard data without human error or bias. Combining wearable sensors and advanced software in clinical trials is one of the best ways to make sure the data is accurate (Seitz, 2023).

Cost-effectiveness and efficiency in trial conduct

Wearable tech in healthcare shows promise for better data collection and analysis-–it can improve disease understanding, treatments, and clinical trials (Izmailova et al., 2018). 

By reducing the need for in-person visits and automating data collection, wearables can cut trial costs by up to 60% (Coravos et al., 2019).

How Wearables Are Used in Clinical Trials

How are wearables being used in real studies? Let’s look at some examples.

Continuous vital sign monitoring

Wearables can track heart rate, blood pressure, and even oxygen levels around the clock. This is especially useful in studies of heart conditions or respiratory diseases.

Activity and sleep tracking

Older woman asleep wearing smartwatch next to cell phone

These devices can measure how much you move and how well you sleep. This data is valuable for studies on conditions like insomnia or chronic fatigue syndrome.

Medication adherence tracking

Timed pill box

Some smart pill bottles can remind patients to take their medication and record when they do. This helps clinicians know if patients are following the treatment plan.

Remote patient monitoring and telemedicine integration

Wearables allow doctors to check on patients from afar. This is particularly helpful for patients who live far from research centers or have mobility issues.

In a study of patients with Parkinson’s disease, wearable sensors were used to track movement patterns. This allowed researchers to measure the effectiveness of a new treatment more accurately than traditional methods (Espay et al., 2016).

Challenges and Limitations of Wearables in Clinical Trials

While wearables offer many benefits, they also come with some challenges.

Data privacy and security concerns

Hacker in a red hoodie

With so much personal health data being collected, keeping it safe is a top priority. Researchers need to ensure that patient information is protected from hackers and unauthorized access.

Regulatory hurdles and FDA approval processes

Getting new devices approved for use in clinical trials can be a long and complex process. The FDA has strict rules about what devices can be used and how data can be collected.

Integration with existing clinical trial systems

Many research centers have established systems for collecting and analyzing data. Integrating wearable data into these systems can be tricky and time-consuming, but can be overcome.

Potential for data overload and interpretation issues

Wearables can generate massive amounts of data. Sorting through all this information and making sense of it can be overwhelming for researchers.

One study found that while 79% of clinical trials were interested in using wearables, only 39% felt confident in their ability to manage and analyze the data effectively (Walton et al., 2015).

Best Practices to Incorporate Wearables in Clinical Trials

To make the most of wearables in clinical trials, researchers should follow these best practices.

Monitor attached to back of a woman's left shoulder

Select appropriate wearable devices for specific trial needs

Not all wearables are created equal. Researchers must choose devices that are scientifically relevant to the study’s endpoints and can gather precise, valid data. 

The goal is to collect meaningful information that significantly contributes to the study’s outcomes and conclusions, rather than just monitoring for the sake of it (Rudo & Dekie, 2024). For example, a sleep study might need a device with advanced sleep-tracking capabilities.

Ensure data quality and validation

It’s crucial to verify that the data collected by wearables is accurate and reliable. This often involves comparing wearable data with data from traditional medical devices.

Train participants and researchers on proper device use

Both patients and research staff need to know how to use the wearables correctly. Good training can improve data quality and reduce errors.

Develop robust data management and analysis protocols

With so much data coming in, having a solid plan for managing and analyzing it is essential. This may involve using specialized software or working with data scientists.

Steinhubl et al. (2018) researched how heart failure patients used wearable sensors to track daily activity. By carefully selecting devices and training participants, the researchers collected high-quality data leading to new insights about the progression of heart failure.

What’s next for wearables in clinical trials? Let’s take a peek.

Smart watch illustration in blue and red

AI and machine learning integration for data analysis

As the amount of data grows, artificial intelligence (AI) and Internet of Things (IoT) will play a bigger role in making sense of it all. AI can help spot patterns and trends that humans might miss.

Multi-modal sensors

Multi-modal sensors in wearables combine different types of sensors in one device to give a more complete picture of a patient’s health (Sietz, 2023). It can include body sensors, environmental sensors, and even imaging tech to gather a wide range of data for clinical studies.

Expanded use of wearables in decentralized clinical trials

More trials are moving away from traditional research centers. Wearables make it possible to conduct studies with patients in their own homes, opening up research to a wider group of people.

Potential for personalized medicine and treatment optimization

By collecting detailed, individual health data, wearables help tailor treatments to each patient’s unique needs.

Conclusion

Wearables are becoming an integral part of clinical trials, offering new insights into patient health and treatment efficacy. These smart devices are likely to revolutionize medical research, leading to faster, more efficient, and patient-centric clinical trials. Who knows–the next big medical breakthrough might come from a small device you can wear.

References

Coravos, A., Khozin, S., & Mandl, K. D. (2019). Developing and adopting safe and effective digital biomarkers to improve patient outcomes. NPJ digital medicine, 2(1), 1-5.

Espay, A. J., Bonato, P., Nahab, F. B., Maetzler, W., Dean, J. M., Klucken, J., … & Papapetropoulos, S. (2016). Technology in Parkinson’s disease: Challenges and opportunities. Movement Disorders, 31(9), 1272-1282.

Izmailova, E. S., Wagner, J. A., & Perakslis, E. D. (2018). Wearable Devices in Clinical Trials: Hype and Hypothesis. Clinical Pharmacology & Therapeutics, 104(1), 42-52.

Marra, C., Chen, J. L., Coravos, A., & Stern, A. D. (2020). Quantifying the use of connected digital products in clinical research. NPJ digital medicine, 3(1), 50.

Seitz, S. (2023). Wearable sensors have already enhanced clinical trials and their impact in this market is only going to grow as technology advances. Find out what clinical trial applications and opportunities exist for your innovative wearable technology company. Sequenex. Retrieved from https://sequenex.com/blog/enhancing-clinical-trials-with-wearable-sensors-and-software-solutions/

Steinhubl, S. R., Waalen, J., Edwards, A. M., Ariniello, L. M., Mehta, R. R., Ebner, G. S., … & Topol, E. J. (2018). Effect of a home-based wearable continuous ECG monitoring patch on detection of undiagnosed atrial fibrillation: the mSToPS randomized clinical trial. Jama, 320(2), 146-155.

Todd Rudo, T., & Dekie, L. (2024). The Future Fit of Wearables for Patient-Centric Clinical Trials. Applied Clinical Trials, 33(4).

Walton, M. K., Powers, J. H., Hobart, J., Patrick, D., Marquis, P., Vamvakas, S., … & Burke, L. B. (2015). Clinical outcome assessments: conceptual foundation—report of the ISPOR Clinical Outcomes Assessment–Emerging Good Practices for Outcomes Research Task Force. Value in Health, 18(6), 741-752.

Wearable Technology Clinical Trials: All You Need To Know About 5 Wearable Devices And Wearable Sensors. Learning Labb Research Institute. (n.d.) Retrieved from https://llri.in/wearable-technology-clinical-trials/

Williams, K. (2023). The Future of Clinical Trials: Embracing Wearables and Beyond. Datacubed Health. Retrieved from https://www.datacubed.com/the-future-of-clinical-trials-embracing-wearables-and-beyond-2/

How Digital Health Platforms Affect Healthcare Costs

by Ree AI Health Tech Med Tech

As healthcare costs continue to go up, digital health platforms are emerging as powerful cost-cutting tools. The global digital health market size was estimated at $240.9 billion in 2023 and is projected to grow at a compound annual growth (CAGR) of 21.9% from 2024 to 2030. 

These platforms are not just fancy apps or websites. From telehealth to AI-powered diagnostics, digital health applications are changing healthcare for the better. 

How do these platforms trim the fat from our bloated healthcare system? Let’s explore the ways digital health can make healthcare more affordable for everyone.

Contents

Telemedicine: Healthcare at Your Fingertips

Telemedicine brings healthcare right to your home, office, or wherever you are. It’s like having a doctor in your pocket! But how does this convenience translate to cost savings?

Woman in green sweater talking to doctor on Zoom

Virtual doctor visits reduce travel and waiting room costs

A study published in the Journal of Medical Internet Research found that telehealth visits saved patients an average of 100 minutes of travel time and $50 in travel costs per visit (Snoswell et al., 2020).

Think about the last time you went to the doctor. How much time did you spend traveling and sitting in the waiting room? With telehealth, those time and money costs disappear. 

Fewer ER visits

How often have you wondered if that late-night stomach ache was worth a trip to the ER? Telehealth tools like AI chatbots can help you make that decision without leaving home. 

Cost savings for both patients and healthcare providers

It’s not just patients who save money. Healthcare providers benefit too. Telehealth services have been found to reduce healthcare costs for providers and patients. Even better, many insurers now have an allowance to cover the cost of certain telehealth visits.

Preventive Care: Stopping Problems Before They Start

Have you ever heard the saying “an ounce of prevention is worth a pound of cure”? Digital health platforms are making this old adage more relevant than ever.

How digital platforms promote healthy habits

Fitness app in the gym

From step counters to diet trackers, digital health apps are helping us stay healthier. But do they really make a difference? A study by Ernsting et al. (2017) found that users of health and fitness apps were 34% more likely to meet physical activity guidelines compared to non-users.

Wearable devices and their impact on early detection

glucose monitor on arm with phone app showing glucose level

Smartwatches surpass the practical use of telling time–they’re becoming powerful health monitors. For example, Apple Watch’s ECG feature can detect atrial fibrillation with 98% accuracy, potentially preventing strokes and saving lives (Perez et al., 2019).

How AI and big data can predict health risks and reduce costs

Big Data Analytics in healthcare uses AI, machine learning and deep learning tools to help doctors find the best treatments for each patient, which can reduce waste. This lets doctors predict health problems  and start treatments early, which can save lives. This could change how common certain diseases are and save money on healthcare (Batko & Ślęzak, 202​​2).

Cost savings through prevention vs. treatment

Prevention isn’t just better for our health—it’s better for our wallets too. The Centers for Disease Control and Prevention estimates that chronic diseases that are avoidable through preventive care account for 75% of the nation’s healthcare spending.

Streamlined Administrative Processes

Paperwork is no one’s favorite part of healthcare. Digital platforms are making administrative tasks faster, easier, and more cost-effective.

Automated appointment scheduling and reminders

Have you ever forgotten a doctor’s appointment? Digital reminders can help. 

Smartwatch with phone and dumbbells

Ulloa-Pérez et al. (2022) found that sending an extra text reminder for high-risk appointments reduced no-shows in primary care and mental health offices, and same-day cancellations in primary care offices. 

Targeting reminders using risk prediction models (predictive analytics) can efficiently use healthcare resources, potentially preventing hundreds of missed visits monthly. This approach saves costs compared to messaging all patients, though implementing the risk model has some costs.

Digital health records reduce paperwork and administrative errors 

Nurse charting

Remember when doctors used to write prescriptions by hand? Digital health records make all kinds of admin work more efficient. A study in the Journal of the American Medical Informatics Association found that electronic health records with AI can reduce medication and billing errors.

Cost savings through improved workflow and resource allocation

Efficient workflows mean better care at lower costs. A study in the Journal of Medical Internet Research found that digital health platforms improved hospital workflow efficiency by 25%, leading to annual cost savings of $1.2 million for a mid-sized hospital (Luo et al., 2019).

Person looking at white overlay

Data-Driven Insights for Better Decision Making

In the age of big data, information is power. Healthcare is no exception. With all this digital information, doctors can make smarter choices about your health. 

How big data analytics improve treatment plans

A study in the Journal of Big Data found that big data analytics improved treatment efficacy by 30% and reduced treatment costs by 20% (Dash et al., 2019).

Cost savings from shorter and fewer hospital stays

Nurse standing in a recovery room

Have you ever wondered how hospitals decide how many beds they need? Predictive analytics is the answer. It can reduce hospital bed shortages and decrease operational costs.

Hospital stays are expensive, but RPM can help shorten them. RPM allows patients to be discharged an average of 2 days earlier, resulting in cost savings of $7,000 per patient.

Personalized medicine and its impact on cost reduction

One size doesn’t fit all in healthcare. Targeted treatments are more effective and cost-effective. 

  • Personalized treatment plans based on genetic data improve treatment efficacy and reduce adverse drug reactions (ADRs).
ECG monitor closeup on stomach

Remote Patient Monitoring: Reducing Hospital Stays

Sometimes, the best hospital care happens outside the hospital. 

Remote patient monitoring (RPM) allows health providers to keep an eye on patients without keeping them in the hospital. From smart pills to wearable sensors, remote monitoring technologies are diverse and growing. 

Impact on reducing hospital readmissions

Nobody likes going back to the hospital. Remote monitoring can help prevent that. A study in the New England Journal of Medicine found that remote monitoring reduced hospital readmissions for heart failure patients by 50% (Perez et al., 2019).

Management of chronic conditions from home

Gentleman taking his blood pressure in tan shirt

Chronic conditions are a major driver of healthcare costs. Remote monitoring can help manage these conditions more effectively. 

A 2024 study showed that telehealth reduces healthcare costs by cutting down on hospital visits, travel time, and missed work, especially for managing chronic conditions. This benefits both patients and healthcare systems financially (Prasad Vudathaneni et al., 2024).

Increasing Access to Specialized Care

Specialized care can be hard to access, especially in rural areas. Digital health isn’t just about general care – it’s also bringing expert help to more people.

Telehealth solutions for rural and underserved areas

Rural healthcare access is a major challenge. Telehealth can help bridge that gap. A study in Health Affairs found that telehealth increased access to specialty care in rural areas by 54%.

Telehealth also faces challenges like high setup costs and outdated payment models, especially in rural areas. Its success depends on cost distribution, clinical outcomes, and indirect savings. Hospitals need funding and strategies to reach underserved groups and ensure fair access to telehealth (Anawade et al., 2024).

Virtual second opinions and their impact on treatment decisions

Getting a second opinion can be life-changing. Virtual platforms make it easier than ever. Virtual second opinions can change the diagnosis or treatment plan in over one-third of cases, potentially avoiding unnecessary procedures and costs.

Conclusion

Digital health platforms are powerful allies to counteract rising healthcare costs. By leveraging technology for prevention, efficiency, and data-driven insights, these platforms are making healthcare more accessible and affordable. From applications like telehealth reducing unnecessary ER visits to catching illnesses early with AI-powered diagnostics, the potential for cost savings is huge. 

As patients, we can embrace these digital tools to take control of our health and potentially lower our healthcare expenses. For healthcare providers, adopting these platforms could lead to more efficient operations and better patient outcomes. 

What do you think about these digital health innovations? Have you used any of these technologies in your own healthcare journey? 

References

Anawade, P. A., Sharma, D., & Gahane, S. (2024). A Comprehensive Review on Exploring the Impact of Telemedicine on Healthcare Accessibility. Cureus, 16(3). doi.org/10.7759/cureus.55996

Batko, K., & Ślęzak, A. (2022). The use of Big Data Analytics in healthcare. Journal of Big Data, 9(1). doi.org/10.1186/s40537-021-00553-4

Centers for Disease Control and Prevention. (2021). Chronic diseases in America. Retrieved from https://www.cdc.gov/chronicdisease/resources/infographic/chronic-diseases.htm

Dash, S., Shakyawar, S. K., Sharma, M., & Kaushik, S. (2019). Big data in healthcare: Management, analysis and future prospects. Journal of Big Data, 6(1), 1-25. doi.org/10.1186/s40537-019-0217-0

Ernsting, C., Dombrowski, S. U., Oedekoven, M., & Kanzler, M. (2017). Using smartphones and health apps to change and manage health behaviors: A population-based survey. Journal of Medical Internet Research, 19(4), e101.

Grand View Research. (2024). Digital Health Market Size, Share & Trends Analysis Report By Technology (Healthcare Analytics, mHealth), By Component (Hardware, Software, Services), By Application, By End-use, By Region, And Segment Forecasts, 2024 – 2030. Retrieved from https://www.grandviewresearch.com/industry-analysis/digital-health-market

Luo, L., Li, J., Liang, X., Zhang, J., & Guo, Y. (2019). A cost-effectiveness analysis of a mobile-based care model for community-dwelling elderly individuals. Journal of Medical Internet Research, 21(5), e13563.

Perez, M. V., Mahaffey, K. W., Hedlin, H., Rumsfeld, J. S., Garcia, A., Ferris, T., Balasubramanian, V., Russo, A. M., Rajmane, A., Cheung, L., Hung, G., Lee, J., Kowey, P., Talati, N., Nag, D., Gummidipundi, S. E., Beatty, A., Hills, M. T., Desai, S., … Turakhia, M. P. (2019). Large-scale assessment of a smartwatch to identify atrial fibrillation. New England Journal of Medicine, 381(20), 1909-1917.

Personalized Medicine Coalition. (2020). The personalized medicine report: Opportunity, challenges, and the future. Retrieved from http://www.personalizedmedicinecoalition.org/Userfiles/PMC-Corporate/file/The-Personalized-Medicine-Report1.pdf

Prasad Vudathaneni, V. K., Lanke, R. B., Mudaliyar, M. C., Movva, K. V., Kalluri, L. M., & Boyapati, R. (2024). The Impact of Telemedicine and Remote Patient Monitoring on Healthcare Delivery: A Comprehensive Evaluation. Cureus, 16(3). doi.org/10.7759/cureus.55534

Snoswell, C. L., Taylor, M. L., Comans, T. A., Smith, A. C., Gray, L. C., & Caffery, L. J. (2020). Determining if telehealth can reduce health system costs: Scoping review. Journal of Medical Internet Research, 22(10), e17298.

Ulloa-Pérez, E., Blasi, P. R., Westbrook, E. O., Lozano, P. , Coleman, K. F., & Coley, R. Y.  (2022). Pragmatic Randomized Study of Targeted Text Message reminders to Reduce Missed Clinic Visits. The Permanente Journal, 26(1), doi/10.7812/TPP/21.078

Winstead, E. (2023). Telehealth Can Save People with Cancer Time, Travel, and Money. National Cancer Institute. Retrieved from https://www.cancer.gov/news-events/cancer-currents-blog/2023/telehealth-cancer-care-saves-time-money

The Future of Telehealth: Trends and Predictions for 2025 and Beyond

The Future of Telehealth: Trends and Predictions for 2025 and Beyond

by Ree AI Health Tech Med Tech

In 2020, the COVID-19 pandemic sparked a 78% uptick in telehealth usage. As we look to the future, telehealth is poised to become an integral part of healthcare delivery. 

This article explores the exciting innovations and trends that will shape the future of telehealth, promising to enhance patient care, improve accessibility, and streamline healthcare operations.

To understand the future of telehealth, we first need to look at the new technologies that are changing how we provide care.

Contents

Emerging Technologies in Telehealth

The future of telehealth is closely tied to advancements in technology. Several cutting-edge innovations are set to reshape virtual care in the coming years.

Artificial intelligence and machine learning in diagnostics

Phone with chatbot conversation

AI and machine learning (ML) can analyze large amounts of medical data to assist healthcare providers in making more accurate diagnoses and treatment recommendations.

For example, AI-powered diagnostic tools can examine medical images like X-rays or MRIs and flag potential issues for review by human doctors. 

AI chatbots are also being developed to conduct initial patient screenings and triage. These chatbots can ask patients about their symptoms and medical history, then direct them to appropriate care options whether that’s a virtual doctor visit, in-person visit, or emergency services.

Internet of Medical Things for remote patient monitoring

The Internet of Medical Things (IoMT) refers to connected medical devices and applications that can collect and transmit health data. This technology enables continuous remote monitoring of patients’ vital signs and other health metrics.

Some examples of IoMT devices include:

5G networks enabling real-time, high-quality video visits

The rollout of 5G networks dramatically improves the quality and reliability of video-based telehealth services. 5G offers much faster data speeds and lower latency compared to 4G networks.

In fact, 5G technology can reduce video latency to less than 2 milliseconds, enabling real-time interaction during virtual doctor visits comparable to in-person visits.

For telehealth, this means:

  • Higher-quality video and audio for virtual visits

  • The ability to transmit large medical files like MRIs quickly

  • More reliable connections in rural or remote areas

  • Support for bandwidth-intensive applications like augmented reality

Take a look at a diagram that shows how connected medical devices interoperate across different systems (Deloitte, 2021).

How connected medical devices interoperate across different systems
Source: Deloitte

Virtual and augmented reality applications in telemedicine

Virtual reality (VR) and augmented reality (AR) have exciting potential applications in telehealth:

For instance, a 2018 study in the Journal of Visualized Experiments found that VR-based physical therapy for stroke patients greatly improved upper limb function compared to conventional therapy (Choi & Paik, 2018).

While technology is important, telehealth’s real strength is in making specialized care available to more people.

Expanding Access to Specialized Care

One of telehealth’s greatest promises is improving access to specialized medical care, especially for underserved populations.

Telepsychiatry bridging the mental health treatment gap

Mental health care has long suffered from accessibility issues, with many areas facing severe shortages of psychiatrists and therapists. Telepsychiatry is helping to bridge this gap.

A 2016 study in the World Journal of Psychiatry found that telepsychiatry was as effective as in-person care for treating depression, with the added benefit of increased patient satisfaction and engagement (Hubley et al., 2016).

Telepsychiatry is particularly valuable for:

  • Rural communities with few local mental health providers

  • Patients with mobility issues or transportation barriers

  • People seeking specialized treatments not available locally

  • Those who prefer the privacy and convenience of at-home care

Remote visits with specialists for rural and underserved areas

Telehealth is bringing specialized medical expertise to areas that previously had little or no access. This includes:

  • Remote dermatology visits using high-resolution images

  • Virtual neurology assessments for stroke patients

  • Tele-oncology services for cancer patients in rural areas

School-based telehealth programs improving pediatric care

School-based telehealth programs are emerging as a powerful tool for improving children’s health, especially in underserved communities. These programs typically involve:

Halterman et al (2017) found that school-based telehealth programs reduced emergency department visits and improved asthma outcomes for children in rural communities.

Virtual second opinions from leading medical experts

Telehealth is making it easier for patients to get second opinions from top specialists, regardless of geographic location. This can be particularly valuable for complex or rare conditions.

Several major medical centers now offer formal virtual second opinion programs. For example, the Mayo Clinic’s eConsults program provides written second opinions from Mayo Clinic specialists based on a review of medical records and test results.

Telehealth is also changing how we approach personalized care and monitoring for patients.

Personalized Medicine and Remote Monitoring

The integration of telehealth with other digital health technologies is enabling more personalized and proactive care.

Wearable devices for continuous health tracking

Monitor attached to back of a woman's left shoulder

Wearable devices like smartwatches and fitness trackers are increasingly being used for medical monitoring. These devices can track:

  • Heart rate and rhythm

  • Blood oxygen levels

  • Sleep patterns

  • Physical activity levels

  • Stress indicators

This continuous data collection allows for more comprehensive health monitoring between doctor visits.

Monitoring services are poised to continue incredible growth over the next several years, as depicted in the following chart (Gupta, 2024).

Source: Appinventiv

AI-powered predictive analytics for early intervention

By analyzing data from wearables, electronic health records (EHRs), and other sources, AI algorithms can predict health risks and recommend early interventions.

Some applications can help clinicians to:

  • Predict heart attacks or strokes based on subtle changes in vital signs

  • Identify patients at risk of developing diabetes

  • Forecast mental health crises based on behavioral patterns

Genomics and telehealth integration for tailored treatments

genetic markers

The combination of telehealth and genomic medicine is opening up new possibilities for personalized treatment plans. Patients can now receive genetic counseling and testing remotely, with results informing tailored treatment recommendations.

For example, pharmacogenomic testing can help determine which medications are likely to be most effective for a particular patient based on their genetic profile. 

Remote medication management and adherence monitoring

Poor medication adherence is a major challenge in healthcare, contributing to worse health outcomes and increased costs. Telehealth-enabled medication management tools can help by:

  • Sending reminders to take medications

  • Tracking medication usage through smart pill bottles or ingestible sensors

  • Allowing remote adjustments to medication regimens

  • Providing education about medications and potential side effects

As telehealth grows, we need to update the rules and regulations that guide its use.

Regulatory Landscape and Telehealth Adoption

Law books and scales with plant and shield

The rapid growth of telehealth has prompted significant regulatory changes, with more likely to come as the technology continues to evolve.

Evolving reimbursement policies for virtual care

One of the biggest barriers to telehealth adoption has been inconsistent reimbursement policies. However, the COVID-19 pandemic led to significant policy changes:

  • Medicare expanded coverage for telehealth services.

  • Many private insurers increased telehealth coverage.

  • Some states mandated payment parity between in-person and virtual visits.

As we move forward, key questions include:

  • Will expanded telehealth coverage become permanent?

  • How will reimbursement rates for virtual care compare to in-person visits?

  • What types of telehealth services will be covered?

Data privacy and security considerations in telehealth

medical papers and stethoscope

The growth of telehealth raises important questions about patient data privacy and security. Key concerns include ways to:

  • Ensure secure transmission of sensitive medical information

  • Protect patient data stored in telehealth platforms

  • Maintain privacy during video visits

Healthcare providers and telehealth companies must comply with regulations like HIPAA in the U.S.

Licensing and cross-state practice regulations

Traditionally, healthcare providers have been limited to practicing in states where they hold a license. This poses challenges for telehealth, which can easily cross state lines.

Some recent developments include:

  • The Interstate Medical Licensure Compact, which streamlines licensing for doctors in multiple states

  • Temporary waivers of state licensing requirements during the COVID-19 pandemic

  • Proposals for a national telemedicine license

Global telehealth initiatives and international cooperation

People around a globe

Telehealth has the potential to improve healthcare access globally, particularly in developing countries with limited medical infrastructure.

Some notable international telehealth initiatives include:

  • The World Health Organization’s Global Strategy on Digital Health

  • The European Union’s eHealth Network

  • The African Alliance of Digital Health Networks

Even with its many benefits, telehealth faces challenges that we must tackle to make it work for everyone.

Overcoming Challenges in Telehealth Implementation

While telehealth offers tremendous potential, several challenges must be addressed to ensure its effective and equitable implementation.

Addressing the digital divide and ensuring equitable access

The “digital divide” the gap between those who have access to technology and those who don’t poses a significant challenge for telehealth adoption.

Key issues include:

  • Lack of broadband internet access in rural areas

  • Limited digital literacy among some patient populations

  • Affordability of devices needed for telehealth

Potential solutions include:

  • Government initiatives to expand broadband access

  • Programs to provide telehealth-enabled devices to underserved populations

  • Digital literacy training for patients

Training healthcare providers in virtual care best practices

Many healthcare providers lack formal training in delivering care via telehealth. This can lead to suboptimal patient experiences and outcomes.

Key areas for provider training include:

  • Effective communication in virtual settings

  • Conducting remote physical exams

  • Managing technical issues during visits

  • Ensuring patient privacy and data security

Integrating telehealth with existing healthcare systems

For telehealth to reach its full potential, it needs to be seamlessly integrated with existing healthcare systems and workflows. This includes:

  • Integrating telehealth platforms with EHRs

  • Developing protocols for when to use telehealth vs. in-person care

  • Ensuring continuity of care between virtual and in-person visits

  • Adapting billing and administrative processes for telehealth

Health providers are set to invest heavily in virtual health applications in the next 5 to 10 years, as shown in the following chart (Gupta, 2024).

Source: Appinventiv

Managing patient expectations and building trust in virtual care

For many patients, telehealth represents a significant shift in how they receive care. Building trust and managing expectations is crucial for successful adoption.

Key considerations include how to:

A recent Health Information National Trends Survey found that 70% of U.S. adults with recent telehealth visits used audio-video, and 75% felt their telehealth visits were as good as in-person care (Spaulding et al., 2024). 

Conclusion

As technology advances and adoption grows, we can expect more personalized, accessible, and efficient care. However, success will depend on addressing challenges such as the digital divide and regulatory hurdles. 

By embracing AI and other technological innovations, we can create a healthcare system that truly meets the needs of patients in the digital age. Patients, providers, and policymakers must work together to shape this exciting future of healthcare.

References

Choi, H., & Paik, J. (2018). Mobile Game-based Virtual Reality Program for Upper Extremity Stroke Rehabilitation. Journal of Visualized Experiments: JoVE; (133). doi.org/10.3791/56241

Deloitte. (2021). Medtech and the Internet of Medical Things: How connected medical devices are transforming health care. Retrieved from https://www2.deloitte.com/content/dam/Deloitte/global/Documents/Life-Sciences-Health-Care/gx-lshc-medtech-iomt-brochure.pdf

General FAQs About the Compact. (n.d.). Interstate Medical Licensure Compact. Retrieved from https://www.imlcc.org/faqs/

Gupta, D. (2024). 7 Telemedicine Trends Shaping the Future of Healthcare. Appinventiv. Retrieved from https://appinventiv.com/blog/top-telehealth-trends/

Halterman, J. S., Tajon, R., Tremblay, P., Fagnano, M., Butz, A., Perry, T., & McConnochie, K. (2017). Development of School-Based Asthma Management Programs in Rochester, NY Presented in Honor of Dr. Robert Haggerty. Academic Pediatrics; 17(6), 595. doi.org/10.1016/j.acap.2017.04.008 

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Marley, R. (2021). 8 key trends driving the future of telehealth. Healthcare Transformers. Retrieved from https://healthcaretransformers.com/digital-health/current-trends/future-of-telehealth/

More care close to home. (2024). MayoClinic. Retrieved from https://www.mayoclinic.org/about-mayo-clinic/care-network/more-care-close-to-home

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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