Tony Hogben is the Immersive Studio Lead at Pfizer Digital Omnichannel Services & Solutions (OSS). Pfizer Digital Omnichannel Services & Solutions (OSS) is on the forefront of remodeling how Pfizer connects with patients, healthcare providers and professionals worldwide. Through revolutionary digital strategies, cutting-edge technology, and data-driven insights, OSS powers seamless, personalised, and impactful experiences. By integrating advanced analytics, automation, and AI-driven solutions, the team enhances engagement, optimises communication, and drives meaningful connections across all digital touchpoints.
You’ve had an in depth profession in digital innovation and immersive technologies. What first sparked your interest on this field, and the way did your journey lead you to your current role?
My path has been somewhat unconventional. After completing a level in ‘Latest Media’ on the turn of the century—when digital was still finding its footing—I established and ran my very own digital agency. Working through the emergence of Web 2.0 was truly exhilarating. We were pioneering SAAS solutions and early mobile applications in an environment where innovation wasn’t only a buzzword—it was our every day reality. Every project broke latest ground, and the entrepreneurial energy was infectious.
After successfully selling my business just before the pandemic, I initially enjoyed the downtime, but quickly realised I needed a brand new challenge that may leverage my expertise. Joining Pfizer Digital has allowed me to mix each my creative vision and technical capabilities, drawing on nearly twenty years of experience helping organisations of all sizes transform digitally.
Constructing the Immersive Studio from the bottom up has been particularly rewarding— creating an internal innovation hub that permits teams across the corporate to harness immersive and interactive technologies. Currently, I’m a part of a team spearheading our initiatives to integrate AI solutions across multiple departments and use cases, helping teams reimagine their workflows and capabilities.
What’s been most fulfilling about transitioning to healthcare is applying my passion for the intersection of technology and human experience in an environment where our work has tangible impact. Here, the precision, realism, and engagement we create through immersive technologies directly influences healthcare skilled education and, ultimately, patient outcomes. This connection between technological innovation and human wellbeing drives me daily.
Medical training is undergoing a shift with AI-driven simulations. How do these AI- powered immersive experiences compare to traditional training methods when it comes to effectiveness and accessibility?
I should start by addressing immersive experiences before exploring how AI is transforming the landscape.
Immersive training experiences fundamentally transform medical education by offering flexibility traditional methods cannot match. Learners can revisit complex scenarios from virtually anywhere, at their very own pace, and as again and again as needed. The evidence is compelling, knowledge retention rates for immersive learning are significant—as much as 76% higher than traditional training methods*
AI is now revolutionising these immersive experiences in 4 crucial ways:
In content creation, AI is democratising the event of high-fidelity simulations. What once required teams of specialized developers and months of labor can now be accomplished faster and by far fewer people – it will unlock development potential and permit content to be created at scale.
For learner experience, AI enables dynamic adaptation—adjusting scenarios in real- time based on decisions and skill level, creating authentic challenges that higher mirror clinical unpredictability.
On the feedback front, AI provides nuanced assessment beyond easy pass/fail metrics. It may possibly analyse the learners’ movements, decision sequences, and compare performance against 1000’s of previous sessions to supply personalised coaching.
Finally, AI enables collaborative learning through natural language processing and intelligent avatars that simulate realistic patient and team interactions.
The accessibility impact is profound—AI-driven immersive experiences could be deployed widely and cost-effectively, helping address training gaps globally. This powerful combination of immersive technology and AI has the potential to democratise access to high-quality medical training, particularly in underserved regions.
Are you able to share insights into how AI-driven medical simulations are being developed at your organization? What are a number of the biggest challenges in constructing these high- fidelity simulations?
We’re within the early stages of integrating AI into our approaches. We’ve a transparent vision of where we’re heading, however the heavily regulated healthcare space we work in necessitates methodical implementation and rigorous validation. This creates a tension between our desire to innovate quickly and our obligation to proceed rigorously—we might love to maintain pace with the frantic innovation happening with AI.
Currently, we’re focusing our AI efforts in three key areas:
- Content Creation Acceleration: We’re using AI to reinforce our content development pipeline, helping our medical and instructional design teams scale production of evidence-based scenarios, clinical variations, and patient models. This permits us to take care of quality while significantly expanding our library of simulations.
- Technical Development Acceleration: We’re leveraging AI to streamline our technical development processes, enabling faster prototyping, testing, and deployment of recent simulation features and capabilities. This helps us overcome resource constraints and speed up our innovation cycle.
- Learner-Adaptive Experiences: In parallel, we’re developing ways to include AI directly into our simulations to create more dynamic, responsive learning environments. This includes personalised feedback systems and adaptive difficulty based on learner performance patterns.
While progress requires patience on this domain, we’re enthusiastic about how these AI innovations will ultimately transform medical training and patient outcomes.
Your 360 degree experience, virtual laboratory, is an revolutionary approach to training healthcare professionals. How does it work, and what sort of feedback have you ever received from users thus far?
The 360-degree virtual laboratory gives healthcare professionals the experience of walking through an actual lab environment, interacting with medical equipment, practicing procedures, and solving real-world challenges in a completely immersive digital space.
The virtual lab was designed to enrich in-person tours of working laboratories that exhibit best practices. We recognised that physical lab visits involve complicated logistics and scheduling limitations, so we created a digital alternative accessible 24/7 from anywhere on this planet.
Healthcare professionals navigate through detailed, interactive simulations that test their knowledge and enhance their understanding of laboratory procedures. The platform is designed for multiple devices, ensuring flexibility in how and where learning takes place. We have expanded our offering to incorporate virtual labs for varied medical conditions and have translated these experiences into many languages to support global education needs.
The feedback has been overwhelmingly positive. Users consistently praise three facets:
- Realism: The high-fidelity environment creates an authentic sense of presence in a working laboratory
- Engagement: Interactive elements maintain interest and focus throughout the educational experience
- Flexibility: The flexibility to access training at their convenience and pace
Most significantly, healthcare professionals report feeling more confident of their skills and retaining information higher than with traditional training methods. This improved knowledge retention translates directly to higher patient care in real-world settings.
AI and immersive tech could make training more accessible, but do you see any barriers—reminiscent of regulatory concerns, adoption hesitancy, or technical limitations—that have to be overcome?
Relating to implementing latest technologies in healthcare training, the barriers differ significantly between immersive experiences and AI applications.
The first challenges with immersive technology include:
- Development Costs: Traditionally, creating high-quality immersive experiences has been expensive. Nevertheless, AI is definitely helping us address this by accelerating content creation and reducing production time.
- Accessibility: We ensure our immersive training stays accessible by developing for multiple platforms, as demonstrated with our Virtual Lab which works across various devices. This approach allows learners to have interaction no matter their technical setup.
- Adoption Hesitancy: This is maybe our most persistent challenge, particularly amongst experienced healthcare professionals. Our strategy is incremental exposure—starting with familiar formats like our Virtual Lab that introduce spatial learning concepts without requiring a steep learning curve. This builds comfort with immersive concepts before advancing to more complex technologies.
For AI integration, we face different obstacles:
- Technical Limitations: We’re actively working through these by constructing robust platforms and approaches that can function foundations for future developments.
- Regulatory Concerns: This represents our most important challenge. Regulatory bodies have valid questions on the accuracy and validity of AI- generated content in healthcare education. Our approach is to develop internal use cases first, creating concrete examples we will use to have interaction regulatory teams constructively. We recognise we’d like to support their understanding while collaboratively developing appropriate guardrails.
By addressing these barriers systematically and recognising their distinct characteristics, we’re creating pathways for responsible innovation that maintains the high standards required in healthcare education.
With AI accelerating at an unprecedented pace, do you foresee a degree where AI could tackle a more lively role in real-time patient care, moderately than simply being a support tool?
This steps barely outside my area of experience, but I feel we will see that AI is already moving beyond support roles in healthcare, with examples like AI-assisted diagnostics and real-time surgery guidance. In the subsequent five years, I expect AI to tackle a far more lively role in patient care, but it surely won’t fully replace humans. As an alternative, AI will work alongside healthcare professionals in a “human-in-the-loop” framework, offering assistance without taking complete control. This shift raises ethical concerns around trust and accountability—while AI might suggest diagnoses or treatment plans, the ultimate decision will still be made by humans to make sure patient safety. AI will enhance decision- making, but human judgment will remain essential.
In a world where AI-generated medical insights could at some point outperform human professionals in certain tasks, how should the healthcare industry prepare for this shift?
With every technological transformation, we see task displacement moderately than people alternative. The healthcare industry must reframe AI not as a alternative for professionals but as a collaborator. It’s an easy equation, Human + AI is bigger than Human or AI alone.
This shift will probably be gradual and task-specific—likely starting in areas like image-based diagnostics, pathology screening, and predictive analytics for patient deterioration. These are areas where pattern recognition at scale gives AI a natural advantage, while more complex clinical reasoning will remain human-led for the foreseeable future.
We’d like to start out with small, targeted tasks that deliver immediate value moderately than the same old all-or-nothing approach of monolithic solutions. This iterative approach allows clinicians and patients to construct trust in AI capabilities over time.
Moderately than resisting change, the healthcare industry should proactively shape how AI is embedded into the healthcare ecosystem, ensuring it enhances moderately than diminishes the human elements that remain central to healing.
Ultimately, step one any organisation should take is democratising AI exposure. Give your staff personal challenges to open their eyes to the chances—have them create a picture, write an email, or construct a presentation using AI tools. Once they experience the ability firsthand, they’ll bring that excitement back to discover meaningful applications of their every day work. Bottom-up innovation often produces probably the most practical and impactful solutions.
Many firms struggle with scaling AI solutions beyond pilot projects. What strategies have you ever used to successfully implement AI at scale?
For me, successfully AI scaling any technology project involves addressing two critical challenges: technology infrastructure, and user adoption.
In healthcare’s heavily regulated environment, establishing robust technical foundations is crucial before scaling any AI initiative. We’d like secure, compliant infrastructure that balances innovation with patient safety requirements.
With latest technology, adoption often becomes the best barrier to scale. We have found that making AI as invisible as possible is crucial to widespread adoption. For instance, being faced with a blank screen and needing to write down an efficient prompt creates significant friction for many users. As an alternative, we’re designing solutions where users can simply click pre-configured buttons or use familiar workflows that leverage AI behind the scenes.
Our approach prioritises starting small but constructing with scale in mind from day one. Moderately than creating one-off solutions, we design modular components that could be prolonged and repurposed across multiple use cases. This permits successful pilots to develop into templates for broader implementation.
You think AI is ready to rework healthcare in ways in which were once considered science fiction. What specific advancements do you think that may have probably the most profound impact over the subsequent five years?
As a baby of the 80s, I remember the Six Million Dollar Man and Bionic Woman TV shows from the Seventies. Those shows featured characters physically augmented by technology, the true revolution with AI, nevertheless, will probably be cognitive augmentation. This excites me probably the most.
Over the subsequent five years, I consider several other specific advancements will fundamentally transform healthcare:
- Administrative Automation: The bureaucratic burden that currently consumes a lot of our healthcare skilled’s time will probably be dramatically reduced. This is not only about efficiency—it’s about putting the care back into healthcare by redirecting human attention to patient interactions.
- Drug Discovery Acceleration: The timeline from identifying therapeutic targets to developing effective treatments will compress from many years to years and even months. AlphaFold, created and open sourced by Google’s DeepMind, has already revolutionised our understanding of protein structures—solving in days what previously took years of laboratory work.
- Precision Diagnostics at Scale: AI systems will dramatically improve early detection of conditions like cancer, heart problems, and neurological disorders through pattern recognition across vast datasets.
- Personalised Treatment: Treatment plans will probably be repeatedly refined based on individual patient data, adjusting in real-time to maximise effectiveness and patients’ engagement in their very own care.
The pace of those changes will probably be startling. AI development is like dog years—but with exponential acceleration. We’re going to see what might need taken 50 years of conventional research and implementation.
These aren’t distant science fiction scenarios—they’re already emerging in early forms, it’s not the longer term, it’s now.
