Ambient intelligence in healthcare | Complete guide
Our society is aging – and that’s a problem.
Loneliness and chronic diseases have begun to dominate as one of the primary health challenges in many developed countries.
Who remains the most defenseless in this situation? Older adults.
The statistics leave no illusions: In the U.S., 27% of adults ages 60 and older live alone. Nearly 95% of them have at least one chronic health condition, and approximately 14% live with a mental disorder.
Traditional healthcare methods are simply not enough: How to provide older adults with personalized, predictive, and around-the-clock healthcare without involving huge amounts of money and burdening the healthcare system?
In this situation, a new branch of healthcare technology has emerged–solutions that can make healthcare surround almost us as imperceptibly as the air we breathe: ambient intelligence.
What is ambient intelligence in healthcare?
Ambient intelligence (AmI) in healthcare refers to an intelligent environment that uses sensors and AI to continuously and unobtrusively monitor patients’ health and surroundings.
It operates at the intersection of IoT devices, pervasive computing, AI, machine learning (ML), and human-computer interaction (HCI).
This technology uses contactless sensors and contact-based wearable devices (video, thermal, acoustic, etc.) to gather data (eg, imaging data of physical spaces, audio data, or body temperature), which can then inform AI-driven solutions to improve clinical workflows and decision-making.
AmI characteristics
An AmI system can be characterized by six traits.
- Context aware: It understands the situation you’re in and tailors its functionality accordingly.
- Personalized: It caters to your individual preferences and needs.
- Anticipatory: It can predict what you might need, even before you realize it yourself.
- Adaptive: It constantly adjusts to your evolving needs and circumstances.
- Ubiquitous: It’s seamlessly woven into your everyday surroundings, always there when you need it.
- Transparent: It works seamlessly in the background, without being intrusive or distracting.
Types of AmI
Ambient intelligence (AmI) in healthcare can be broadly categorized into two main types:
- Patient-centric AmI: This type of AmI focuses on monitoring individual patients using wearable and implantable sensors. These sensors track a wide range of health data including:
- Vital signs (heart rate, respiration rate, blood pressure, etc.)
- Blood sugar levels
- Activity levels
- Sleep patterns
- Medication adherence
- Environment-centric AmI: This type of AmI focuses on creating an intelligent healthcare environment using sensors embedded in the surroundings. These sensors can monitor things like:
- Room temperature and humidity
- Ambient noise levels
- Lighting conditions
- Occupancy levels
AI algorithms are used to analyze data from AmI systems to find patterns and potential health risks. This information can then be used to help patients and caregivers in a few ways. For instance, it can provide real-time feedback, alert healthcare professionals about possible problems, automate tasks like medication reminders, and even personalize care plans.
Healthcare AmI’s origins: RPM
The journey to ambient health monitoring began with the development of RPM (remote patient monitoring), a technology that–along with telemedicine–gained significant traction during the pandemic.
RPM allowed healthcare providers to track patients’ vital signs and health metrics remotely, reducing the need for in-person visits. However, as innovative as RPM was, it had its limitations—chief among them, the need for patients to wear or interact with devices actively.
The concept of ambient health monitoring evolved from this need for more passive, yet comprehensive, health tracking. Just like ambient music, which provides an unobtrusive backdrop to other activities, ambient health monitoring aims to create a seamless, continuous health tracking experience.
How ambient health monitoring works
Here’s a breakdown of how it works:
- Sensors gather data: AmI relies on a network of sensors embedded in medical devices and the environment itself. These sensors can track things like vital signs, activity levels, and even medication adherence, and can be integrated into everyday objects like lamps or mirrors, constantly collecting data on various health parameters.
- AI analyzes the data: Advanced algorithms analyze this data in real-time to provide insights and alerts to healthcare providers and patients alike.
Applications of AmI
The applications of ambient health monitoring are diverse and far-reaching.
- Home and daily living spaces: For the elderly, home and everyday activities are areas where ambient AI offers the most significant benefits.
- Chronic disease management: AI systems monitor conditions like Parkinson’s disease or diabetes.
- Ambient assisted living and working: Supports elderly and disabled individuals. Ambient sensors help monitor daily activities, detect falls, and provide timely alerts to caregivers.
- Mental health: AmI can be used to monitor sleep patterns, track mood changes, and encourage physical activity to support mental health.
- Outpatient clinics
- Ambient clinical intelligence: The sad reality of many healthcare systems is that medical staff spend a significant portion of their working time on bureaucratic tasks. For example, in the US, clinicians spend up to 35% of their time on documentation tasks. Ambient clinical intelligence uses AI to listen in on doctor-patient conversations and automatically document the visit in electronic health records. Ambient microphones reduced documentation time from 2 hours to 15 minutes in some cases.
- Virtual nurse assistants: Provide personalized care, monitor health status, and assist with medication management, both in hospitals and home settings.
- Inpatient hospital care
- ICU applications: Ambient AI can monitor things like movement, sleep patterns, and even vital signs.
- Operating rooms: Ambient cameras and computer vision can evaluate surgical skills, automate surgical tool counts, and improve overall surgical efficiency.
AmI case studies
Nobi smart lamp that detects patient falls
In senior care, companies like Sensi.AI use audio sensors to monitor the daily activities and well-being of elderly individuals living alone.
Another compelling application is in mental health. Platforms like Kintsugi Voice use voice biomarkers to detect signs of mental health issues in real-time. Ellipsis Health employs machine learning to assess the severity of anxiety and depression based on speech patterns.
Smart devices are also making their way into our homes, enhancing everyday objects with health-monitoring capabilities. The Nobi smart lamp, for instance, not only provides illumination but also detects falls and alerts caregivers if a patient becomes inactive.
Benefits & impact of ambient AI on healthcare
The benefits of ambient health monitoring are significant.
One of the main advantages is the seamless experience. Unlike traditional health monitoring devices that require active use, such as wearables or home medical equipment, ambient health monitors are designed to be passive and omnipresent.
Early detection of health issues, such as chronic conditions or potential complications, can lead to more timely interventions, reducing the severity of illnesses and improving overall health. For healthcare providers, this technology offers a cost-effective solution, as it reduces the need for frequent in-person visits and hospitalizations. This aligns perfectly with preventive care, one of the key healthcare trends for the upcoming years.
Moreover, ambient health monitoring empowers patients to take control of their health. With real-time data at their fingertips, individuals can make better decisions about their lifestyle and treatment options. This proactive approach to health management not only enhances the quality of care but also fosters a deeper sense of engagement and responsibility among patients.
Challenges
While ambient health monitoring offers amazing possibilities, several challenges remain.
A key focus will be on balancing continuous data collection with user-friendly technology that minimizes patient burden. Companies developing the next generation of remote health monitoring will need to prioritize devices that are both effective and aesthetically pleasing.
Another challenge involves improving sensor technology to achieve accurate, frequent measurements across a broader range of health indicators. Current solutions can monitor basic vitals like heart rate and movement, but more complex metrics like blood pressure require further development.
Additionally, translating gathered data into actionable insights to improve health outcomes remains a hurdle. This includes determining the best treatment plans based on the collected information.
Finally, as with any data-driven and AI-powered technology, privacy, ethics, and security concerns surrounding ambient health monitoring need to be addressed.
Conclusions. How to build an AmI product?
Ambient health monitoring will undeniably make healthcare more accessible and personalized than ever before.
However, how do you design and build a truly reliable and effective AmI product?
You need:
- Sensor integration & data acquisition systems: Hardware, data communication and security protocols.
- Database management: Handling large datasets generated by the sensors and ensuring efficient data storage and retrieval.
- User interface and user experience (UI/UX) design: Creating an intuitive and user-friendly interface is essential for user adoption.
- Experience with AI & ML: Developing algorithms for data analysis, pattern recognition, and generating health insights requires expertise in AI and machine learning.
- Domain knowledge in healthcare (ideally).
Here at Pragmatic Coders, we understand the challenges and complexities involved in building truly reliable and effective healthcare software.
Contact us today to discuss your vision and explore how we can help you build a reliable and effective product that transforms the way we monitor and manage health.
Sources
A Survey on Ambient Intelligence in Health Care
Current and Potential Applications of Ambient Artificial Intelligence
Illuminating the dark spaces of healthcare with ambient intelligence
Older people are more likely to live alone in the U.S. than elsewhere in the world
The Top 10 Most Common Chronic Conditions in Older Adults
Mental health of older adults
CBInsights, 11 Tech Trends To Watch Closely in 2023
Article author
