From Chatbots to Clinical Decision Support Systems: A Founder’s Journey

When I first explored conversational AI in health retail, I saw enormous promise in its ability to help consumers make smarter, more personalized choices. Simple chatbots could answer questions about store hours or recommend a multivitamin based on age and lifestyle. These solutions were helpful, but relatively basic.

As I continued to build Re:Healthify, it became clear that conversational AI was on the verge of something far more impactful — a transition from simple chatbots to clinical decision support systems (CDSS). This shift is more than a technical evolution; it represents a profound change in how we think about the role of AI in healthcare.

The Limits of Simple Chatbots

First-generation health retail chatbots were largely scripted. They offered FAQ-style answers or pulled product suggestions from a rules-based list. Their benefits were real: cost reduction, 24/7 coverage, and consistent messaging. But they struggled to handle more nuanced, medically complex scenarios.

For example, recommending a supplement without understanding a user’s medication history could create safety risks. Similarly, giving advice on managing mild gut health issues without context could blur the line between consumer wellness and regulated medical advice.

The Rise of Clinical Decision Support

Enter the concept of CDSS — systems designed to assist healthcare professionals, or advanced AI agents, in making evidence-based decisions. Instead of static scripts, these systems combine:

  • rich health data,

  • robust algorithms,

  • and medically validated guidelines.

A CDSS doesn’t just answer a question. It supports a decision. For example, it can flag interactions between medications, suggest follow-up testing, or recommend seeing a specialist. In other words, it goes from being a digital helper to an intelligent partner in care.

Conversational AI Meets CDSS

My vision with Re:Healthify is to fuse conversational interfaces — which people naturally find easy to use — with CDSS capabilities that maintain clinical rigor and safety. This is where the journey becomes challenging but incredibly promising.

  • Explainability is essential: consumers and regulators need to understand why an AI made a recommendation.

  • Compliance is non-negotiable: we must design within frameworks like HIPAA, GDPR, and future AI regulations.

  • Human-in-the-loop escalation is critical: no machine should replace a qualified clinician for high-risk or diagnostic decisions.

The Founder’s Perspective

Evolving a simple chatbot into a CDSS is like evolving a tricycle into a Formula 1 car. It is:

  • more complex

  • more regulated

  • and vastly more powerful

As a founder, this means:

  • partnering with clinical experts

  • building robust data and security architecture

  • investing in medical validation and evidence-based protocols

  • understanding jurisdictional differences (UK vs. US vs. EU)

It also means balancing commercial reality with ethical responsibility. There is a temptation to market “AI doctors” before the systems are truly ready. We must resist that shortcut.

The Future

Conversational AI, grounded in clinical decision support principles, has the potential to transform health retail, empower patients, and support clinicians in ways we could not have imagined a decade ago.

The journey from simple chatbots to advanced CDSS is challenging — but if done correctly, it can build trust, improve health outcomes, and reshape how consumers interact with their wellness choices.

That is the journey we are on at Re:Healthify. And I believe it is the future of health retail.

Re:HealthifyFrancesca Tabor