From Conversation to Clinical Insight: The Role of Clinical Decision Support in Ambient Voice Technology
We previously spoke with Phil Verplancke, FDB’s Product and ITS Director, about AI and healthcare. Since then, FDB has been exploring exciting developments in the ambient voice technology (AVT) space, particularly around how it can enhance medicines clinical decision support (CDS).
Amy Smith, who is our AVT expert and leads FDB’s clinical decision support strategy, spoke to us about the opportunities emerging at the intersection of conversational AI and medicines safety.
Amy, as ambient voice technology becomes more widely adopted across the NHS, why is it important that medicines clinical decision support evolves alongside it?
We’re seeing AI adoption accelerate across clinical settings in the NHS with AVT increasingly being used to capture consultations and clinical conversations in real time. Used appropriately, these technologies have the potential to reduce administrative burden for clinicians and support more natural, patient-centred consultations through documentation and note-taking.
However, from FDB’s perspective, capturing the conversation is only a small part of the story! If AVT is going to become embedded within prescribing and wider clinical workflows, then trusted medicines safety logic also needs to become part of that process.
What’s particularly exciting is that AVT creates an opportunity for CDS to engage earlier and more naturally within the clinical workflow. Traditionally, decision support has operated at the point of prescribing, but conversational AI opens the possibility of surfacing explainable, evidence-based medicines intelligence as decisions are being formed.
As organisations explore the use of AVT within prescribing and documentation workflows, it’s essential that these technologies support rather than disrupt established clinical safeguards and decision-making processes. While AVT can help capture clinical language more efficiently, medicines CDS within these workflows must remain clinically robust, fluid and non-disruptive to the user experience.
This is something we at FDB are actively exploring: how to combine the benefits of conversational AI with decades of medicines knowledge expertise to support safer, more informed clinical decision-making.
Why is medicines clinical decision support so important as ambient voice technology becomes more widely used in clinical workflows?
We see significant potential for conversational data, in conjunction with structured EPR data, to support safer clinical decision-making, clearer auditability and ultimately better clinical insight within healthcare workflows.
From the research and early adoption we’re seeing across the market, AVT has the potential to identify safety signals earlier in the workflow, while also helping to reduce cognitive burden and alert fatigue for clinicians. By recognising key clinical phrasing and context in real time, these systems can surface relevant medicines guidance and safety information earlier and more efficiently during the consultation process.
For example, AVT integrated with medicines CDS could help identify potential drug-drug interactions, allergies or contraindications as conversations are taking place, therefore enabling clinicians to make more informed decisions without interrupting the natural flow of the consultation.
Importantly, this allows clinicians to focus more fully on the patient interaction itself, rather than dividing attention between documentation, prescribing systems and administrative tasks. At the same time, patients may feel more listened to and engaged because clinicians can maintain more natural conversations while still capturing accurate clinical information.
There is also an opportunity to provide faster, more contextual medicines feedback within the workflow, helping to support safer prescribing decisions and reduce the risk of patient harm. The key is ensuring that medicines CDS within AVT remains explainable, evidence-based, and integrated in a way that complements rather than interrupts clinical practice.
What innovations is FDB exploring in AVT and medicines CDS?
At FDB, we’ve been exploring how medicines clinical decision support can engage earlier and more naturally within conversational clinical workflows, while still respecting established safeguards and the realities of clinical practice.
One of the most exciting developments for us has been the ability to move beyond relying solely on structured, coded data. Historically, medicines CDS has depended heavily on coded information within clinical systems. With ambient voice technology and advances in AI, we can now begin to understand and analyse unstructured clinical conversations and free text in a clinically meaningful way. This unlocks an entirely new capability for medicines intelligence.
We’re now exploring how proven, clinically validated medicines CDS can be integrated into conversational workflows so clinicians can access clear, explainable, evidence-based guidance exactly when they need it. Our AI-enabled approach is about applying trusted medicines safety checks to clinical conversations and free text, helping to identify risks such as drug interactions, allergies, or contraindications to support safer decision-making and better patient outcomes.
Importantly, we believe these capabilities should integrate seamlessly into existing healthcare platforms and workflows. The goal is to support clinicians with timely, contextual medicines intelligence in a way that complements rather than disrupts the clinical workflow, while allowing organisations to retain control of the overall user experience.
This forms part of a broader area of innovation we’re currently referring to as ‘Augmented Medicines Intelligence’, an emerging concept that reflects how explainable, evidence-based medicines expertise can evolve alongside AI-driven clinical workflows.
The future of medicines intelligence
Ambient voice technology is changing how clinical information is captured, but the real opportunity lies in how that information is interpreted and acted upon. At FDB we believe medicines intelligence has a critical role to play in ensuring these new workflows remain safe, clinically relevant and actionable. We’re looking forward to working with NHS organisations and technology partners to help define what good looks like and unlock the next generation of clinical decision support.
Amy, as ambient voice technology becomes more widely adopted across the NHS, why is it important that medicines clinical decision support evolves alongside it?
We’re seeing AI adoption accelerate across clinical settings in the NHS with AVT increasingly being used to capture consultations and clinical conversations in real time. Used appropriately, these technologies have the potential to reduce administrative burden for clinicians and support more natural, patient-centred consultations through documentation and note-taking.
However, from FDB’s perspective, capturing the conversation is only a small part of the story! If AVT is going to become embedded within prescribing and wider clinical workflows, then trusted medicines safety logic also needs to become part of that process.
What’s particularly exciting is that AVT creates an opportunity for CDS to engage earlier and more naturally within the clinical workflow. Traditionally, decision support has operated at the point of prescribing, but conversational AI opens the possibility of surfacing explainable, evidence-based medicines intelligence as decisions are being formed.
As organisations explore the use of AVT within prescribing and documentation workflows, it’s essential that these technologies support rather than disrupt established clinical safeguards and decision-making processes. While AVT can help capture clinical language more efficiently, medicines CDS within these workflows must remain clinically robust, fluid and non-disruptive to the user experience.
This is something we at FDB are actively exploring: how to combine the benefits of conversational AI with decades of medicines knowledge expertise to support safer, more informed clinical decision-making.
Why is medicines clinical decision support so important as ambient voice technology becomes more widely used in clinical workflows?
We see significant potential for conversational data, in conjunction with structured EPR data, to support safer clinical decision-making, clearer auditability and ultimately better clinical insight within healthcare workflows.
From the research and early adoption we’re seeing across the market, AVT has the potential to identify safety signals earlier in the workflow, while also helping to reduce cognitive burden and alert fatigue for clinicians. By recognising key clinical phrasing and context in real time, these systems can surface relevant medicines guidance and safety information earlier and more efficiently during the consultation process.
For example, AVT integrated with medicines CDS could help identify potential drug-drug interactions, allergies or contraindications as conversations are taking place, therefore enabling clinicians to make more informed decisions without interrupting the natural flow of the consultation.
Importantly, this allows clinicians to focus more fully on the patient interaction itself, rather than dividing attention between documentation, prescribing systems and administrative tasks. At the same time, patients may feel more listened to and engaged because clinicians can maintain more natural conversations while still capturing accurate clinical information.
There is also an opportunity to provide faster, more contextual medicines feedback within the workflow, helping to support safer prescribing decisions and reduce the risk of patient harm. The key is ensuring that medicines CDS within AVT remains explainable, evidence-based, and integrated in a way that complements rather than interrupts clinical practice.
What innovations is FDB exploring in AVT and medicines CDS?
At FDB, we’ve been exploring how medicines clinical decision support can engage earlier and more naturally within conversational clinical workflows, while still respecting established safeguards and the realities of clinical practice.
One of the most exciting developments for us has been the ability to move beyond relying solely on structured, coded data. Historically, medicines CDS has depended heavily on coded information within clinical systems. With ambient voice technology and advances in AI, we can now begin to understand and analyse unstructured clinical conversations and free text in a clinically meaningful way. This unlocks an entirely new capability for medicines intelligence.
We’re now exploring how proven, clinically validated medicines CDS can be integrated into conversational workflows so clinicians can access clear, explainable, evidence-based guidance exactly when they need it. Our AI-enabled approach is about applying trusted medicines safety checks to clinical conversations and free text, helping to identify risks such as drug interactions, allergies, or contraindications to support safer decision-making and better patient outcomes.
Importantly, we believe these capabilities should integrate seamlessly into existing healthcare platforms and workflows. The goal is to support clinicians with timely, contextual medicines intelligence in a way that complements rather than disrupts the clinical workflow, while allowing organisations to retain control of the overall user experience.
This forms part of a broader area of innovation we’re currently referring to as ‘Augmented Medicines Intelligence’, an emerging concept that reflects how explainable, evidence-based medicines expertise can evolve alongside AI-driven clinical workflows.
The future of medicines intelligence
Ambient voice technology is changing how clinical information is captured, but the real opportunity lies in how that information is interpreted and acted upon. At FDB we believe medicines intelligence has a critical role to play in ensuring these new workflows remain safe, clinically relevant and actionable. We’re looking forward to working with NHS organisations and technology partners to help define what good looks like and unlock the next generation of clinical decision support.
