A buyer’s guide to ambient intelligence

October 21, 2024

What is ambient intelligence?

Technologies that include unobtrusive solutions, sensors, and devices embedded in the physical environment that continuously collect data and enable autonomous detection, prediction, intervention and other intelligent support. AmI systems are context-aware, adapting to changing needs, and offering real-time support like alerts or recommendations.

Ambient intelligence relies on a combination of technologies, including:

Internet of things (IoT): Connecting everyday objects to the internet to collect and exchange data.
Artificial intelligence (AI): Enabling devices to learn from interactions and improve over time.
Sensor networks: Using sensors to gather data about the environment and user activities.
Natural user interfaces: Allowing interaction through voice, gestures, and other non-intrusive methods.

Solution framework

Ambient observation and monitoring: Technologies that passively collect data from the surrounding environment to gather health-related insights without requiring active input from individuals.
Inpatient ambient monitoring: Systems that passively track patient vital signs, movements, and health indicators in hospital settings using unobtrusive sensors and devices, requiring no direct intervention from patients or staff. Vital sign monitoring: Continuous, non-invasive tracking of patients’ essential physiological measurements (e.g., heart rate, blood pressure, temperature) using ambient sensors and technologies. Patient movement tracking: Systems that monitor and analyze patient mobility patterns within healthcare facilities to assess recovery progress, fall risk, and overall well-being.	At-home ambient monitoring: Technologies that passively collect health data in patients’ homes through smart home devices and wearables, enabling continuous health monitoring without active participation from the user. Smart living integration: The incorporation of health monitoring capabilities into everyday living environments, using interconnected IoT devices to create a comprehensive health-aware ecosystem. Medication adherence monitoring: Systems that passively track and verify patient compliance with prescribed medication regimens using smart pill bottles or other ambient technologies. Passive wearable tracking: Unobtrusive wearable devices that continuously collect health data without requiring user interaction, providing long-term insights into activity levels, sleep patterns, and physiological trends.

Ambient observation and monitoring: Technologies that passively collect data from the surrounding environment to gather health-related insights without requiring active input from individuals.

Inpatient ambient monitoring:
Systems that passively track patient vital signs, movements, and health indicators in hospital settings using unobtrusive sensors and devices, requiring no direct intervention from patients or staff.

Vital sign monitoring: Continuous, non-invasive tracking of patients’ essential physiological measurements (e.g., heart rate, blood pressure, temperature) using ambient sensors and technologies.

Patient movement tracking: Systems that monitor and analyze patient mobility patterns within healthcare facilities to assess recovery progress, fall risk, and overall well-being.

At-home ambient monitoring:
Technologies that passively collect health data in patients’ homes through smart home devices and wearables, enabling continuous health monitoring without active participation from the user.

Smart living integration: The incorporation of health monitoring capabilities into everyday living environments, using interconnected IoT devices to create a comprehensive health-aware ecosystem.

Medication adherence monitoring: Systems that passively track and verify patient compliance with prescribed medication regimens using smart pill bottles or other ambient technologies.

Passive wearable tracking: Unobtrusive wearable devices that continuously collect health data without requiring user interaction, providing long-term insights into activity levels, sleep patterns, and physiological trends.

Ambient clinical intelligence: AI and machine learning algorithms that analyze current and historical patient data in real time, providing actionable clinical insights without requiring manual input or immediate analysis by healthcare professionals.
Clinical intelligence: Advanced data analytics tools that continuously generate insights, predict outcomes, and support clinical decision-making by processing ambient data streams without direct user intervention.

Ambient documentation: Technologies that capture and analyze conversations and activities in clinical settings to generate accurate medical records, eliminating or significantly reducing the need for manual documentation.
Ambient scribes: Systems that automatically transcribe, summarize, and structure clinical conversations and observations in real-time, without requiring active input from healthcare providers.

Ambient assistants: AI-driven tools that passively support various aspects of healthcare delivery, workflows, and management by providing proactive task assistance and contextual information.
Voice activated assistants: AI-powered systems that respond to voice commands in clinical settings, facilitating hands-free access to information and workflow support.
Workflow orchestration: Intelligent systems that autonomously manage and optimize clinical workflows, automatically assigning tasks, prioritizing activities, and coordinating resources based on real-time data and predefined protocols.
Audio and speech recognition: AI-powered systems that handle patient calls, appointments, and inquiries using natural language processing, providing seamless communication without human intervention while also assessing the quality of the call.

Ambient experience: Technologies that passively respond to human actions and needs, dynamically adjusting the physical and digital environment to optimize patient comfort and provider efficiency.
Intelligent environments: Integrated systems of sensors, displays, and interfaces that autonomously transform a physical space to adapt to user needs, preferences, and clinical requirements in real time.
Asset tracking and resource management: Systems that automatically monitor the location and status of medical equipment, personnel, and resources within a healthcare facility, optimizing utilization and workflow efficiency.
Gesture-based control systems: Technologies that allow healthcare professionals to interact with medical devices, information systems, and environmental controls using intuitive hand gestures, reducing the need for direct physical contact.

Ambient Intelligence in healthcare represents a paradigm shift in how we interact with our environment and deliver care. By enabling environments to be responsive, adaptive, and anticipatory, we can reduce the cognitive load on our clinicians and enhance patient experiences. The key is to implement these technologies thoughtfully, always keeping the human element at the center of our design and decision-making process.

— Sonia Singh, Chief Inights Officer at AVIA

The case for ambient intelligence

Healthcare systems are plagued with a myriad of problems that require a multitude of solutions to address concerns in patient safety, clinician burnout, efficiency, patient experience, and data management. Ambient intelligence (AmI) emerges as a transformative solution that offers health systems the potential to do more with less while reducing the need for fragmented solutions. By leveraging emerging technologies such as intelligent sensors and monitors, AmI enables healthcare providers to collect and analyze data more efficiently, reducing the burden on human resources. This capability allows for the augmentation of human decision-making with autonomous systems, leading to more informed and timely interventions. Additionally, by automating routine tasks and providing real-time, data-driven insights, AmI has the potential to significantly reduce clinician burnout, allowing healthcare professionals to focus on high-value patient interactions and complex decision-making processes. This overall approach not only enhances patient care and safety but also streamlines operations, potentially reducing costs and improving the overall healthcare experience for both patients and providers.

Patient safety and outcomes
One of the most pressing concerns in healthcare is patient safety and outcomes. Despite advances in medical knowledge and technology, preventable medical errors and hospital-acquired conditions continue to pose significant risks. Approximately 400,00 hospitalized patients experience some form of preventable harm each year, with more than 200,000 patient deaths occurring annually due to preventable medical errors.¹ Ambient intelligence addresses these challenges by introducing advanced monitoring systems that operate seamlessly in the background. These systems can continuously track patient vital signs, analyze patterns, and alert staff to potential issues before they become critical. By enabling proactive care, AmI has the potential to prevent complications, reduce hospital-acquired conditions, and ultimately improve patient outcomes.

Clinical experience
Clinician burnout has reached epidemic proportions, driven in large part by administrative burdens and documentation requirements that pull healthcare providers away from direct patient care. The constant need to input data into electronic health records, manage paperwork, and navigate complex workflows leaves clinicians feeling overwhelmed and dissatisfied.² Ambient intelligence offers a solution by automating many of these tasks. Through voice-activated assistants and natural language processing, AmI can capture and document clinical encounters automatically, freeing clinicians to focus on patient interactions. This shift not only reduces the administrative burden but also allows healthcare providers to practice at the top of their license, potentially improving job satisfaction and work-life balance.

Efficiency and cost management
Efficiency and cost management are perennial challenges for healthcare organizations. With tightening budgets and increasing demand for services, there’s a constant pressure to do more with less.³ Workflows are often suboptimal, leading to wasted time and resources. Ambient intelligence addresses these issues by streamlining operations through intelligent workflow optimization. AmI systems can analyze patterns in patient flow, staff utilization, and resource allocation to suggest more efficient processes. For example, predictive staffing algorithms can forecast patient needs and ensure appropriate staffing levels, while smart resource allocation systems can optimize the distribution of medical equipment and supplies. These improvements can lead to reduced wait times, shorter lengths of stay, and significant cost savings.

Patient experience
Patient experience is another area where many healthcare systems struggle to meet rising expectations. Patients increasingly expect personalized, responsive care environments similar to what they experience in other service industries.⁴ Traditional hospital rooms and care processes often fall short, feeling impersonal and outdated. Ambient intelligence transforms the patient experience by creating adaptive environments that respond to individual needs and preferences. Smart room systems can automatically adjust lighting, temperature, and entertainment options based on patient preferences. Moreover, AmI can facilitate better communication between patients, families, and care teams, providing easy access to relevant information and enhancing patient engagement in their own care.

Data management
Finally, healthcare organizations face challenges in leveraging the vast amounts of data they collect to drive meaningful improvements. Despite the wealth of information available, many struggle to turn this data into actionable insights. Although four out of five healthcare leaders believe most of their data is accurate, 47% of healthcare data, on average, is underutilized when making clinical and business decisions.⁵ Ambient intelligence systems excel in this area, using advanced analytics and machine learning to process and interpret complex datasets in real time. This capability enables data-driven decision-making at both the individual patient level and for population health management. AmI can provide clinicians with contextual information at the point of care, support clinical research with rich, comprehensive datasets, and enhance quality improvement initiatives through detailed performance metrics.

Key attributes of ambient intelligence solutions

Effective ambient intelligence (AmI) solutions in healthcare share several critical attributes that enable them to seamlessly integrate into and enhance the care environment. When evaluating AmI solutions, healthcare organizations should look for the following key characteristics:

Context-awareness	Utilizes contextual and situational information from the environment Understands the current usage scenario and provides appropriate responses Integrates data from multiple sources to create a comprehensive view of the care situation Example: An AmI system that adjusts alarm thresholds based on a patient’s current activity level and medical history.
Multisensory and multimodal intelligence	Processes and integrates data from multiple sources (e.g., microphones, cameras, sensors) Has a comprehensive understanding of varying complex healthcare environments Provides appropriate responses in various modalities (speech, text, visual outputs) Enhances situational awareness and context-based decision making Example: An AmI system in an ICU that combines audio, visual, and biosensor data to detect early signs of patient deterioration and alert staff through their preferred notification method.
Personalization	Tailors interactions and interventions to the needs of each individual Learns from user preferences and behaviors over time Adapts to different user roles (e.g., patients, nurses, physicians) and their specific needs Example: A room environment system that automatically adjusts lighting, temperature, and entertainment options based on patient preferences and clinical requirements.
Anticipatory capabilities	Predicts user needs without requiring explicit input Leverages historical data and patterns to forecast future events or requirements Proactively offers suggestions or initiates actions to prevent issues Example: A clinical decision support system that anticipates potential drug interactions based on a patient’s medication history and current condition.
Adaptability	Evolves and adjusts in response to changes in the environment or user behavior Continuously learns from new data and experiences to improve performance Flexibly accommodates different clinical scenarios and workflows Example: An AI-driven scheduling and staffing system that sends certain clinicians based on changing patient acuity levels and staff availability.
Ubiquity and seamless integration	Embeds unobtrusively into everyday healthcare environments Integrates with existing systems and workflows without disruption Provides consistent experience across different devices and platforms Example: Voice-activated clinical documentation that works across various areas of the hospital, from patient rooms to operating theaters.
Natural and intuitive interaction	Offers multimodal interaction methods (e.g., voice, gesture, touch) Minimizes the learning curve for users through intuitive design Reduces cognitive load by presenting information in easily digestible formats Example: A gesture-controlled imaging system that allows surgeons to manipulate scans without breaking sterility.
Robust privacy and security	Ensures strong data protection and compliance with healthcare regulations Provides granular access controls and audit trails Maintains patient privacy while enabling necessary data sharing for care coordination Example: An AmI system that only shares patient information with relevant care team members.
Scalability and interoperability	Scales effectively from individual rooms to entire healthcare systems Interoperates seamlessly with other healthcare IT systems and devices Supports open standards to facilitate integration and future expansion Example: An ambient clinical documentation system that can be deployed across multiple hospitals and integrates with various EHR platforms.
Reliability and fault tolerance	Maintains functionality even in degraded network conditions Provides fallback mechanisms to ensure critical functions remain available Offers clear indicators of system status and any limitations Example: A patient monitoring system that continues to function and store data locally during network outages, syncing once connectivity is restored.
Measurable impact	Provides clear metrics and analytics to demonstrate ROI and clinical impact Offers tools for continuous quality improvement and performance optimization Aligns with organizational goals and key performance indicators Example: An AmI solution that tracks and reports on its impact on patient outcomes, clinician efficiency, and operational costs.

Organizing for success with ambient intelligence

To successfully implement ambient intelligence solutions, health systems must consider several key factors. By keeping these principles in mind, organizations can effectively tailor their ambient intelligence initiatives to meet the needs of providers, patients, and the organization as a whole.

Choose the right spaces, environments, and use cases.
1. Carefully select provider populations and clinical areas, considering factors such as potential for impact and environmental readiness and capabilities to support an ambient use case.
2. Prioritize use cases that offer the most significant benefits in terms of workflow improvement, efficiency and patient care.
Establish clear consent processes and patient education.
1. Develop a consent process that balances efficiency with legal requirements. Options include:
  1. Verbal consent initiated by the provider, emphasizing efficiency and patient focus.
  2. Written consent, potentially with separate consent for data used to train the model.
  3. Embedding ambient clinical recording language in the universal consent process.
2. Proactively educate patients on the positive outcomes of the solution, such as increased provider focus during visits.
Plan for data management and security.
1. Establish policies for data retention, such as timed access to recordings and data post-capture.
2. Ensure compliance with relevant federal and state specific privacy laws and regulations.
3. Implement secure storage and access protocols for captured data and insights.
Define accountability and response protocols.
1. Clearly delineate responsibilities for responding to signals and alerts generated by the ambient intelligence system.
2. Establish protocols for managing false positives and autonomous alerts.
3. Implement a system of checks and balances to ensure appropriate use and response to AI-generated insights.
Plan for workflow changes and staffing impacts.
1. Analyze how ambient intelligence will alter existing workflows across different specialties and roles.
2. Assess the impact on staffing needs and role definitions.
3. Provide robust support during implementation, including “at-the-elbow” assistance from clinical training specialists.
Develop comprehensive training programs.
1. Create tailored training programs for different user groups (e.g., physicians, nurses, administrative staff).
2. Include training on system use, new workflows, and how to interpret and act on AI-generated insights.
3. Offer ongoing education to keep staff updated on system capabilities and best practices.
Address legal considerations.
1. Consult with legal experts to ensure compliance with relevant laws and regulations.
2. Develop protocols for managing legal risks associated with false positives and autonomous alerts.
3. Establish clear policies on data ownership, access, and use.

Sources:

Visit AVIA Marketplace ahead of your next purchasing decision for unbiased third-party information, ratings, and reviews for hundreds of the leading digital health companies and solutions.