AI-Native and Plug-In AI Digital Care Systems: What You Need To Know

Artificial intelligence (AI) is changing care administration and delivery, offering opportunities to enhance productivity, and improve care outcomes. These are two separate use cases and must be considered differently. Administrative tasks (note taking, action lists and automation, for example) are easy and tooling for this is widely available, low risk and inexpensive. AI, which understands healthcare and adds value to the delivery of care, is quite different. When selecting care software, it's vital to distinguish between "AI-native" capabilities and those with "plug-in AI" features. This blog post will explain these terms, highlight their significance, and illustrate their impact on real-world care scenarios.

See AI-Native in Action

What Is AI-Native Software?

An AI-native platform (like PredicAire) is designed and built with artificial intelligence deeply integrated into the product, whilst still delivering compliance with all applicable laws and regulations applicable to data and AI. This fundamental approach provides a platform with a significant advantage, enabling more relevant input to user experiences, accelerated innovation cycles, and the development of proprietary models trained on your specific data. In the care context, this means the platform goes beyond simple admin, automation and data storage as it understands, learns, and provides real-time health and care specific recommendations.

What Is Plug-In AI?

Plug-in AI refers to the retrospective addition of AI tools to existing systems. These tools might include chatbots or dashboards displaying data from multiple other tools. While they can offer surface-level insights and features like chat functionalities and data summarisation, they lack deep integration. Consequently, these types of platforms often rely on the plug-in AI system for innovation and governance, limiting their control over the AI development roadmap, and using your data in ways which are potentially unknown.

Why This Matters In Care

Many existing care platforms are incorporating AI as an afterthought, rather than fundamentally rethinking core workflows and user experiences. This often translates to AI features such as chatbots, relegated to sidebars and perceived as removed from core workflows and tasks. Truly embedded AI operates seamlessly in the background, becoming an intuitive and integral part of the user's workflow.

At PredicAire, we encourage widespread AI access within care settings, to encourage a culture of experimentation, learning, and rapid iteration. This collaborative approach allows us to gather real-world feedback and implement into our product development roadmap.

In an AI-native system like PredicAire, intelligence is baked in the architecture from the outset, not merely sprinkled on top. This integrated approach results in a dynamic and intuitive user experience, where alerts, recommendations, and proactive problem-solving occur seamlessly. The focus shifts from focussing on adding AI features for the sake of it, to leveraging AI to address pain points and provide value.

Our Virtual Nurse, Flo, exemplifies this approach. As with some plug-in AI systems, Flo can transform complex data into natural language summaries, enabling carers to quickly digest crucial information and review any trends in the service users’ care in the matter of seconds and deliver care proactively. Our workflows are built as “human in the loop” ensuring the final decision on care delivery always lies with the human being.  

AI-native vs Plug-in AI: Summary

Criteria	Plug-in AI	AI-native
Value	Limited to single use-case	Solves pain points
Insights	Prompt-based	Automated, multivariate analytics
Architecture	Multiple tools or widgets	End-to-end workflows
User Experience	Fragmented, more clicks	Like magic
Control over AI	Reliant on plug-in provider	Full ownership

Recommendations

For care providers investing in digitisation, choosing an AI-native system like PredicAire is a future-proof decision. These systems offer smarter workflows, and ultimately, better user adoption. The tangible benefits range from enhanced safety to increased staff satisfaction.

1. Prioritise AI-Native: Seek platforms where AI is deeply integrated into core workflows, not just added as an afterthought.
2. Ask for Transparency: Ask providers about their AI model development and training.
3. Focus on Outcomes: Evaluate how AI addresses specific pain points and improve care delivery, rather than leading with features.

The choice between AI-native and plug-in AI is a critical one for care providers. While plug-in solutions may offer some initial benefits, AI-native systems provide a more robust, integrated, and ultimately more effective approach to leveraging the power of artificial intelligence in care. By prioritising integration, demanding transparency, focusing on outcomes, and considering long-term value, care providers can make informed decisions that will lead to safer care, happier staff, and a future-ready technology infrastructure.

Choosing between AI-native and plug-in AI is a key decision. While some plug-in AI tools might seem like a quick fix, AI-native systems are built to proactively understand and improve how you deliver care, which in turn enables your care staff to potentially early intervene in a resident’s care, as well run your business more efficiently. What if your care uniform looked great, but the pockets were stitched on as an afterthought? You’d feel the difference all shift long.