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AI in Healthcare: Anticipatory, Adaptive, & Human-Aware Systems

  • Writer: James McGreggor
    James McGreggor
  • May 28
  • 5 min read

Updated: 2 days ago

Medical staff with patient.
Medical staff with patient.

Overview


This use case explores how AI is enabling healthcare systems to move from reactive to proactive care—supporting patients, caregivers, and staff with context-aware, emotionally attuned responses. From anticipating delays to optimizing the environment, AI has the ability to create smoother, more human-centered experiences. Within this, Ambient Intelligence (AmI) emerges as a powerful frontier: using sensors and environmental data to create responsive care spaces that intuitively adapt to human needs.


The Challenge


Navigating a hospital can be stressful—not just for patients, but for families and care teams. Information delays, rigid scheduling, or unresponsive spaces create emotional strain that erodes trust and satisfaction. Often, these non-clinical moments are overlooked, even though they impact health outcomes and staff effectiveness.


The Opportunity


AI Today: Human-Aware Automation


AI technologies already in use in many other business applications has the ability to fully transform these high-friction touch points:

  • Deliver context-aware notifications to caregivers and family members (e.g., “Patient is will not be out of surgery for another hour; here are some recommendation to get your kids dinner while you wait.”).


  • Guide staff toward emotionally strained individuals based on real-time sentiment analysis from conversations or environmental cues.


  • Predict arrivals and dynamically prepare rooms or reroute patient flows using machine learning, reducing wait times and congestion.


  • Monitor and communicate with a patient to get critical information to medical staff, especially when short staffed) or to create log entries alleviating burden on medical staff.

“AI is creating opportunities to make patient care more contextual and emotionally supportive—not just efficient.”— Allison Matthews, Design Lead, Mayo Clinic

These applications rely on:


  • Natural Language Processing (NLP)

  • Predictive Analytics and ML

  • Computer Vision and Audio Signal Analysis

  • Robotic Process Automation (RPA)



Ambient Intelligence (AmI): Emerging Sensory Ecosystems


As healthcare environments become increasingly digitized, Ambient Intelligence (AmI) offers the next leap forward—combining AI with IoT and environmental control to elevate emotional care.


AmI-enabled systems have the capability to:


  • Adjust lighting and soundscapes to reduce anxiety in waiting areas.


  • Air Quality & Ventilation: Sensors track CO₂, humidity, and airflow to ensure proper ventilation—key in reducing airborne transmission.


  • Use Bipolar Ionization within HVAC systems to purify the air and neutralize contaminants at their source.


  • Surface Contamination Risk Detection: UV sensors or contact detection systems can identify high-touch surfaces needing more frequent cleaning.

“The future of healthcare isn’t just smart—it’s sensitive. When a room can respond to the needs of its occupants before they ask, that’s real progress.”— Serene Almomen, CEO & Co-Founder, Attune


Generative UI (GenUI) and Just-in-Time UI (JIT UI): Outcome-Oriented Design


In the evolving landscape of digital health, Generative UI (GenUI) and Just-In-Time UI (JIT UI) are transforming how patients interact with apps—by making interfaces context-aware, adaptive, and emotionally intelligent.


With Generative UI (GenUI), an application dynamically assembles an interface tailored in real time to a patient’s specific context.


JIT UI adds real-time emotional and behavioral responsiveness. JIT UI doesn’t just change the interface—it changes how and when the interface is delivered, at the exact moment of need. Two examples leveraging GenUI and JIT UI are:


  • Drawing on an understanding of the patient’s needs, the interface presents only the most relevant options—removing clutter, simplifying tasks, and in some cases, adapting to the patient’s emotional state to reduce stress or confusion.


  • If the system detects signs of frustration or confusion—through pause patterns, repeated actions, help-seeking behavior, or biometric feedback—it may respond with a visual prompt or a voice-enabled message, asking if the patient needs assistance.


When used together GenUI and JIT UI remove friction, enhance accessibility, and humanize digital health tools by making them responsive. This is more than UX optimization, it’s Real-time Emotional Architecture, allowing digital systems to adjust to the person, not the other way around.



To underscore the importance of GenUI and JIT UI...


Meredith Ringel Morris, a principal scientist at Google DeepMind, has extensively explored the intersection of AI and human-computer interaction (HCI). In her paper "The Design Space of Generative Models," she and her co-authors propose:

"Developing design spaces for emerging pre-trained, generative AI models is necessary for supporting their integration into human-centered systems and practices."


Yannis Paniaras, a principal design lead at Microsoft, discusses the evolution of user experience in the context of AI. In his article "The Shift to Just-In-Time UX: How AI is Reshaping User Experiences," he describes:

"We’re moving away from the legacy models of interaction and navigation of static product topologies. We’re transforming the user experience through AI and copilot-based experiences and creating new paradigms of interaction and navigation across a complex topology of services and products."

These perspectives highlight the need for transition towards dynamic, context-aware interfaces that adapt in real-time to user needs, something that health organizations, businesses, designers, and engineers need to be aware of.



The Approach


With the direct impact to humans, building any solution within the health services space is complicated - rightfully so. Protection of patients, care-givers, and medical staff must be the highest priority.


While additional regulations may exist, the approach to building a solution in the health services space is not too different than in other industries. These steps are high level and may be tailored to your environmental needs, but this approach has been used within the health services sector, manufacturing, telecom, and logistics, and has been highly successful every time.


  1. Identify the "why", a clear and compelling reason for change.

  2. Craft a charter that includes the opportunity or problem statement with a discrete quantifiable goal, your data collection plan, and list of compliance monuments that must be adhered to.


  3. Assuming that you have approval, go through a discovery exercise to establish a baseline understanding of the target process area.


  4. Analyze the data, identify gaps and risks, and design a high-level solution.


  5. Use agile, pilot-first methods: develop the proof-of-concept, pilot, and refine.


Most importantly, establish privacy-preserving protocols, especially around sensor and identity data. Feedback loops from staff and patients are critical for long-term adoption.



To support this type of project, you’ll need:


  • Inputs: highly specific to the use case.


  • Expertise in: AI/ML and behavioral UX, process improvement, product design, change management, data governance; beyond that it is dependent on the solution (e.g., sensor fusion, IoT integration)


  • Collaborators: Patient experience leads, IT, clinical operations, facilities teams, care coordinators, compliance specialists



What to Watch Out For


  • Privacy & transparency: Ambient sensing must align with HIPAA and be clearly disclosed to patients and staff.

  • System accuracy: Both AI and sensor systems must be reliable, redundant, and tested across diverse environments.


  • Frontline engagement: Without visible, meaningful feedback, staff may disengage from the tools or ignore alerts.


  • Bias or overgeneralization: Models must adapt to individual and cultural differences—especially in emotionally sensitive environments.



The Impact


AI, AmI, and JIT & GenUI help shift healthcare from passive care delivery to anticipatory, human-aware systems. While results will be quantifiable (e.g., efficiency, decreased errors), the greatest impact is to the patients and caregivers and staff: alleviating stress, strain, and simply elevating their total experience.



Partner With Us


While we (Blue Forge Digital) are here to provide premium digital services to many industries, the wellbeing of healthcare providers and patients is something we are deeply passionate about - something that was part of the inspiration of forming Blue Forge Digital to begin with.


We are experts in planning, designing and architecting digital solutions for humans and our specialty is in the science of how we use data.


If you are facing a challenge that you believe can be solved with some level of science and technology and a bit of creativity, we are here to help.







References


Almomen, Serene. “The Future of Healthcare Isn’t Just Smart—It’s Sensitive.” Attune, https://www.attuneiot.com/. Accessed 26 May 2025.

Matthews, Allison. “Empathy by Design: Innovating for Patient Experience.” Mayo Clinic Insights, https://www.mayoclinic.org/. Accessed 26 May 2025.


Morris, Meredith Ringel, et al. “The Design Space of Generative Models.” arXiv, 2023, https://arxiv.org/abs/2305.10478. Accessed 27 May 2025.


Paniaras, Yannis. “The Shift to Just-In-Time UX: How AI is Reshaping User Experiences.” LinkedIn, 2024, https://www.linkedin.com/pulse/shift-just-in-time-ux-how-ai-reshaping-user-experiences-yannis-paniaras. Accessed 27 May 2025.


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