AI Care Evolution – Part 3: Implement AI in Long-Term Care: 5 Practical Steps for Care Providers
Successfully implementing AI in long-term care requires a step-by-step approach from needs assessment and staff training to daily workflow integration. (Source: Fotor AI)
As aging populations surge and workforce shortages grow increasingly acute, long-term care (LTC) providers worldwide are turning to artificial intelligence (AI) as a strategic solution. From fall prevention systems and nighttime monitoring to generative AI assisting in care plan documentation, technology is steadily making its way into the day-to-day operations of elder care. Yet, successful integration of AI depends not only on adopting the right tools but on aligning them with care processes and infrastructure.
This article explores how care institutions in Taiwan are embracing AI and offers a clear five-step framework that global LTC providers can adapt when embarking on their own AI transformation.
Strategic Implementation Begins with System Integration
In Taipei, one of Taiwan’s leading eldercare groups has been proactively deploying AI-powered technologies across its facility. By integrating fall detection sensors and wearable biometric monitors into a centralized cloud-based platform, the team ensures real-time data analysis through a unified interface. These systems work in tandem, combining IoT devices with generative AI tools that streamline nursing documentation and detect subtle health trends.
Importantly, this facility doesn't apply a one-size-fits-all approach. Instead, residents are categorized by cognitive and physical risk levels. For example, individuals with dementia are safeguarded with geofencing and tracking systems, while others with higher mobility use bed-exit alerts and fall detection sensors.
The leadership emphasizes that without precise needs assessment and thoughtful integration, AI investments risk becoming obsolete. Integration, not just installation, is the real key, ensuring flexibility for scaling and reducing redundancy.
Building Technological Resilience: Infrastructure First
Elsewhere in Taipei, another LTC provider has built a robust architecture to support AI operations. This includes sensor networks, edge computing, centralized dashboards, and real-time alerts. Devices such as smart mattresses, millimeter-wave radars, and bladder scanners feed data into a monitoring system, even allowing families to track loved ones’ vitals through LINE Bot integration.
However, the lesson learned is clear: hardware is only as good as the infrastructure behind it. Rushing into AI adoption without a solid foundation, like reliable Wi-Fi or power routing, can result in idle systems and breakdowns. That’s why this facility invested early in Wi-Fi 6, commercial routers, and a mesh network system, along with IP address management to ensure system stability.
Device longevity is another concern. Choosing equipment with over-the-air (OTA) update capabilities helps extend usability and avoid frequent replacements, an important consideration for any LTC operator with limited IT resources.
AI as a Collaborative Tool, Not a Replacement
AI’s most promising role in eldercare is not to replace staff, but to enhance and support them. When embedded thoughtfully into daily routines, AI becomes a “second set of eyes,” aiding in real-time monitoring and improving workflow management.
For example, in one care home, smart beds and fall sensors automatically update hallway dashboards, reducing the need for routine room checks during overnight shifts. During the COVID-19 pandemic, wearable monitors were vital in tracking blood oxygen levels, helping identify early warning signs without overwhelming staff. Yet, simplicity of use is non-negotiable—if a system is too complex, it simply won’t be adopted by frontline caregivers.
A phased rollout strategy also contributes to successful implementation. Facilities are starting with pilot floors or specific resident groups, training AI-literate supervisors as “seed personnel” to lead adoption efforts. As the system matures, data uploads and documentation become increasingly automated, thereby minimising human error and enhancing consistency.
5 Key Steps for AI Implementation in Long-Term Care Facilities
- Focus on risk hotspots and repetitive tasks within the care environment (e.g., fall alerts, dementia wandering, repositioning records).
- Assess layered needs based on resident profiles and facility size; evaluate implementation items and sensing parameters.
- Establish a "Minimum Viable Pilot Unit" — e.g., a single floor or resident group trial.
- The assessment process should be led by operators and care managers, involving cross-departmental teams (caregivers, admin, IT staff, technology consultants) to ensure practical fit with actual workflows.
- Networks and power are core to AI technology. Recommended: Cat6 wired networks, commercial-grade mesh routers, and fixed IP addresses for devices.
- Hardware setup should include:
- Complete socket and network cabling paths.
- Ensured data transmission logic: IoT → Gateway → Backend.
- Network segmentation and permission management for cybersecurity.
- Review and build available data infrastructure:
- Data should be accurate, interpretable, and truly reflect on-site situations.
- Include diverse data types (numeric, image, audio).
- Data must have real-time or near-real-time feedback to enhance dynamic decision-making.
- Data Governance:
- Compliance: Follow Personal Data Protection and Medical Laws; define data collection, use, and storage policies (e.g., resident data requires authorization with access/deletion rights).
- Sensitivity Protection: Access restrictions to prevent leaks or misuse (e.g., identity authentication).
- Data must directly support care decisions and management goals — avoid collecting irrelevant data (e.g., sleep data not integrated into care plans has no real value).
- Select a single technology or area for a short-term Proof of Concept (PoC) to test stability and integration.
- Set evaluation indicators:
- False alarm rate, miss rate, maintenance difficulty.
- Feedback from caregivers and managers.
- Compatibility with existing care information platforms.
- Establish cross-functional feedback and error logging mechanisms during the pilot to guide scaling decisions.
- Standardized Operation Training: Create simple manuals to help caregivers and admin staff operate, maintain, and handle data; recommend task-oriented learning (e.g., how to check fall incidents or update abnormal data).
- Advanced Emergency Response Training: Teach troubleshooting and abnormal condition assessment; simulate scenarios for new staff to ease learning pressure.
- Enhance Data Literacy: Guide frontline staff in interpreting system data and applying it to case evaluations and care plan adjustments.
- Train Internal Key Users: Select core AI users for "coach-style deployment" to spread experience and reduce reliance on consultants.
- Use measurable criteria for results evaluation on three levels:
- User Level: False alarm rate, satisfaction, operation success rate.
- Process Level: Handover efficiency, reduction in paper tasks, data retention rate.
- Decision Level: Data visualization usage, quality monitoring, abnormal response time.
- Redesign processes to match changes after implementation:
- Abnormal reporting: e.g., report within 3 minutes, auto-generate logs.
- Digital handover: Sensor records automatically linked to handover reports to reduce manual errors.
- Manual annotation mechanisms: Allow human review/notes when sensor data misinterprets situations.
The Future: From Monitoring to Decision-Making
Generative AI is now emerging as a strategic partner in care management. Experts envision tools that not only automate documentation and predict health events but also support personalized care through data synthesis and voice-enabled interfaces.
Some Taiwanese care homes have already begun using tools like ChatGPT to co-develop Individualized Service Plans (ISPs). The AI drafts an initial framework based on case data, which is then refined by human caregivers. Others are experimenting with feeding AI both textual SOPs and video demonstrations to train it in best-practice caregiving techniques.
Looking ahead, AI could become a real-time coaching assistant, offering feedback on task execution, supporting advanced training, or even reading residents’ emotional states via facial expression analysis. In this vision, AI is no longer a passive processor but an active, localized care partner embedded directly into facility infrastructure, capable of learning, adapting, and making intelligent care decisions on the ground.
This is AI Care Evolution series Part 3, continuing our series on how AI is transforming the care industry. Part 1 highlighted 3 game-changing trends reshaping care, while Part 2 showcased real-world applications and emerging innovations. Coming up in Part 4, we’ll turn to international experience and expert insights to explore new strategies for AI-powered transformation in the care sector.
Part 1: AI Care Evolution: Part 1 — 3 Game-Changing Trends Rewiring the Care Industry
Part 2: AI Care Evolution – Part 2: Product Categories Driving the Future of AI in Care
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