Tony Hogben is the Immersive Studio Lead at Pfizer Digital Omnichannel Companies & Options (OSS). Pfizer Digital Omnichannel Companies & Options (OSS) is on the forefront of reworking how Pfizer connects with sufferers, healthcare suppliers and professionals worldwide. By way of modern digital methods, cutting-edge know-how, and data-driven insights, OSS powers seamless, personalised, and impactful experiences. By integrating superior analytics, automation, and AI-driven options, the workforce enhances engagement, optimises communication, and drives significant connections throughout all digital touchpoints.
You’ve had an intensive profession in digital innovation and immersive applied sciences. What first sparked your curiosity on this subject, and the way did your journey lead you to your present position?
My path has been considerably unconventional. After finishing a level in ‘New Media’ on the flip of the century—when digital was nonetheless discovering its footing—I established and ran my very own digital company. Working throughout the emergence of Net 2.0 was actually exhilarating. We had been pioneering SAAS options and early cell purposes in an atmosphere the place innovation wasn’t only a buzzword—it was our day by day actuality. Each challenge broke new floor, and the entrepreneurial vitality was infectious.
After efficiently promoting my enterprise simply earlier than the pandemic, I initially loved the downtime, however rapidly realised I wanted a brand new problem that will leverage my experience. Becoming a member of Pfizer Digital has allowed me to mix each my artistic imaginative and prescient and technical capabilities, drawing on practically twenty years of expertise serving to organisations of all sizes remodel digitally.
Constructing the Immersive Studio from the bottom up has been notably rewarding— creating an inner innovation hub that allows groups throughout the corporate to harness immersive and interactive applied sciences. Presently, I am a part of a workforce spearheading our initiatives to combine AI options throughout a number of departments and use instances, serving to groups reimagine their workflows and capabilities.
What’s been most fulfilling about transitioning to healthcare is making use of my ardour for the intersection of know-how and human expertise in an atmosphere the place our work has tangible influence. Right here, the precision, realism, and engagement we create by way of immersive applied sciences straight influences healthcare skilled training and, in the end, affected person outcomes. This connection between technological innovation and human wellbeing drives me each day.
Medical coaching is present process a shift with AI-driven simulations. How do these AI- powered immersive experiences evaluate to conventional coaching strategies by way of effectiveness and accessibility?
I ought to begin by addressing immersive experiences earlier than exploring how AI is remodeling the panorama.
Immersive coaching experiences essentially remodel medical training by providing flexibility conventional strategies cannot match. Learners can revisit complicated situations from just about anyplace, at their very own tempo, and as many occasions as wanted. The proof is compelling, data retention charges for immersive studying are vital—as much as 76% higher than conventional coaching strategies*
AI is now revolutionising these immersive experiences in 4 essential methods:
In content material creation, AI is democratising the event of high-fidelity simulations. What as soon as required groups of specialized builders and months of labor can now be accomplished quicker and by far fewer individuals – this can unlock improvement potential and permit content material to be created at scale.
For learner expertise, AI allows dynamic adaptation—adjusting situations in real- time based mostly on selections and ability stage, creating genuine challenges that higher mirror medical unpredictability.
On the suggestions entrance, AI gives nuanced evaluation past easy go/fail metrics. It will possibly analyse the learners’ actions, resolution sequences, and evaluate efficiency in opposition to hundreds of earlier classes to supply personalised teaching.
Lastly, AI allows collaborative studying by way of pure language processing and clever avatars that simulate lifelike affected person and workforce interactions.
The accessibility influence is profound—AI-driven immersive experiences could be deployed extensively and cost-effectively, serving to tackle coaching gaps globally. This highly effective mixture of immersive know-how and AI has the potential to democratise entry to high-quality medical coaching, notably in underserved areas.
*Bonde, Mads & Makransky, Guido & Wandall, Jakob & Larsen, Mette & Morsing Bagger, Mikkel & Jarmer, Hanne & Sommer, Morten. (2014). Bettering biotech training by way of gamified laboratory simulations
Are you able to share insights into how AI-driven medical simulations are being developed at your organization? What are a number of the largest challenges in constructing these high- constancy simulations?
We’re within the early levels of integrating AI into our approaches. We’ve a transparent imaginative and prescient of the place we’re heading, however the closely regulated healthcare area we work in necessitates methodical implementation and rigorous validation. This creates a rigidity between our want to innovate rapidly and our obligation to proceed fastidiously—we would like to hold tempo with the frantic innovation taking place with AI.
Presently, we’re focusing our AI efforts in three key areas:
- Content material Creation Acceleration: We’re utilizing AI to reinforce our content material improvement pipeline, serving to our medical and educational design groups scale manufacturing of evidence-based situations, medical variations, and affected person fashions. This permits us to keep up high quality whereas considerably increasing our library of simulations.
- Technical Improvement Acceleration: We’re leveraging AI to streamline our technical improvement processes, enabling quicker prototyping, testing, and deployment of recent simulation options and capabilities. That is serving to us overcome useful resource constraints and speed up our innovation cycle.
- Learner-Adaptive Experiences: In parallel, we’re growing methods to include AI straight into our simulations to create extra dynamic, responsive studying environments. This contains personalised suggestions methods and adaptive issue based mostly on learner efficiency patterns.
Whereas progress requires persistence on this area, we’re enthusiastic about how these AI improvements will in the end remodel medical coaching and affected person outcomes.
Your 360 diploma expertise, digital laboratory, is an modern strategy to coaching healthcare professionals. How does it work, and what sort of suggestions have you ever obtained from customers up to now?
The 360-degree digital laboratory offers healthcare professionals the expertise of strolling by way of an actual lab atmosphere, interacting with medical gear, practising procedures, and fixing real-world challenges in a totally immersive digital area.
The digital lab was designed to enrich in-person excursions of working laboratories that reveal greatest practices. We recognised that bodily lab visits contain difficult logistics and scheduling limitations, so we created a digital different accessible 24/7 from anyplace on the planet.
Healthcare professionals navigate by way of detailed, interactive simulations that take a look at their data and improve their understanding of laboratory procedures. The platform is designed for a number of units, guaranteeing flexibility in how and the place studying takes place. We have expanded our providing to incorporate digital labs for quite a few medical circumstances and have translated these experiences into many languages to help world training wants.
The suggestions has been overwhelmingly constructive. Customers persistently reward three features:
- Realism: The high-fidelity atmosphere creates an genuine sense of presence in a working laboratory
- Engagement: Interactive components keep curiosity and focus all through the educational expertise
- Flexibility: The flexibility to entry coaching at their comfort and tempo
Most significantly, healthcare professionals report feeling extra assured of their abilities and retaining info higher than with conventional coaching strategies. This improved data retention interprets straight to higher affected person care in real-world settings.
AI and immersive tech could make coaching extra accessible, however do you see any obstacles—akin to regulatory considerations, adoption hesitancy, or technical limitations—that should be overcome?
In terms of implementing new applied sciences in healthcare coaching, the obstacles differ considerably between immersive experiences and AI purposes.
The first challenges with immersive know-how embrace:
- Improvement Prices: Historically, creating high-quality immersive experiences has been costly. Nonetheless, AI is definitely serving to us tackle this by accelerating content material creation and decreasing manufacturing time.
- Accessibility: We guarantee our immersive coaching stays accessible by growing for a number of platforms, as demonstrated with our Digital Lab which works throughout numerous units. This strategy permits learners to interact no matter their technical setup.
- Adoption Hesitancy: That is maybe our most persistent problem, notably amongst skilled healthcare professionals. Our technique is incremental publicity—beginning with acquainted codecs like our Digital Lab that introduce spatial studying ideas with out requiring a steep studying curve. This builds consolation with immersive ideas earlier than advancing to extra complicated applied sciences.
For AI integration, we face totally different obstacles:
- Technical Limitations: We’re actively working by way of these by constructing sturdy platforms and approaches that can function foundations for future developments.
- Regulatory Issues: This represents our most vital problem. Regulatory our bodies have legitimate questions in regards to the accuracy and validity of AI- generated content material in healthcare training. Our strategy is to develop inner use instances first, creating concrete examples we are able to use to interact regulatory groups constructively. We recognise we have to help their understanding whereas collaboratively growing applicable guardrails.
By addressing these obstacles systematically and recognising their distinct traits, we’re creating pathways for accountable innovation that maintains the excessive requirements required in healthcare training.
With AI accelerating at an unprecedented tempo, do you foresee a degree the place AI might tackle a extra lively position in real-time affected person care, fairly than simply being a help software?
This steps barely exterior my space of experience, however I believe we are able to see that AI is already transferring past help roles in healthcare, with examples like AI-assisted diagnostics and real-time surgical procedure steerage. Within the subsequent 5 years, I count on AI to tackle a way more lively position in affected person care, but it surely received’t absolutely exchange people. As a substitute, AI will work alongside healthcare professionals in a “human-in-the-loop” framework, providing help with out taking full management. This shift raises moral considerations round belief and accountability—whereas AI may recommend diagnoses or therapy plans, the ultimate resolution will nonetheless be made by people to make sure affected person security. AI will improve decision- making, however human judgment will stay important.
In a world the place AI-generated medical insights might in the future outperform human professionals in sure duties, how ought to the healthcare trade put together for this shift?
With each technological transformation, we see job displacement fairly than individuals substitute. The healthcare trade must reframe AI not as a substitute for professionals however as a collaborator. It is a easy equation, Human + AI is larger than Human or AI alone.
This shift will probably be gradual and task-specific—possible starting in areas like image-based diagnostics, pathology screening, and predictive analytics for affected person deterioration. These are areas the place sample recognition at scale offers AI a pure benefit, whereas extra complicated medical reasoning will stay human-led for the foreseeable future.
We have to begin with small, focused duties that ship instant worth fairly than the standard all-or-nothing strategy of monolithic options. This iterative strategy permits clinicians and sufferers to construct belief in AI capabilities over time.
Fairly than resisting change, the healthcare trade ought to proactively form how AI is embedded into the healthcare ecosystem, guaranteeing it enhances fairly than diminishes the human components that stay central to therapeutic.
In the end, step one any organisation ought to take is democratising AI publicity. Give your workers private challenges to open their eyes to the probabilities—have them create a picture, write an e-mail, or construct a presentation utilizing AI instruments. As soon as they expertise the facility firsthand, they’re going to convey that pleasure again to determine significant purposes of their day by day work. Backside-up innovation usually produces probably the most sensible and impactful options.
Many corporations battle with scaling AI options past pilot tasks. What methods have you ever used to efficiently implement AI at scale?
For me, efficiently AI scaling any know-how challenge entails addressing two important challenges: know-how infrastructure, and consumer adoption.
In healthcare’s closely regulated atmosphere, establishing sturdy technical foundations is important earlier than scaling any AI initiative. We want safe, compliant infrastructure that balances innovation with affected person security necessities.
With new know-how, adoption usually turns into the best barrier to scale. We have discovered that making AI as invisible as attainable is essential to widespread adoption. For instance, being confronted with a clean display and needing to put in writing an efficient immediate creates vital friction for many customers. As a substitute, we’re designing options the place customers can merely click on pre-configured buttons or use acquainted workflows that leverage AI behind the scenes.
Our strategy prioritises beginning small however constructing with scale in thoughts from day one. Fairly than creating one-off options, we design modular elements that may be prolonged and repurposed throughout a number of use instances. This permits profitable pilots to turn into templates for broader implementation.
You consider AI is ready to rework healthcare in ways in which had been as soon as thought of science fiction. What particular developments do you assume may have probably the most profound influence over the subsequent 5 years?
As a baby of the 80s, I bear in mind the Six Million Greenback Man and Bionic Lady TV exhibits from the Seventies. These exhibits featured characters bodily augmented by know-how, the true revolution with AI, nonetheless, will probably be cognitive augmentation. This excites me probably the most.
Over the subsequent 5 years, I consider a number of different particular developments will essentially remodel healthcare:
- Administrative Automation: The bureaucratic burden that presently consumes a lot of our healthcare skilled’s time will probably be dramatically lowered. This is not nearly effectivity—it is about placing the care again into healthcare by redirecting human consideration to affected person interactions.
- Drug Discovery Acceleration: The timeline from figuring out therapeutic targets to growing efficient remedies will compress from a long time to years and even months. AlphaFold, created and open sourced by Google’s DeepMind, has already revolutionised our understanding of protein constructions—fixing in days what beforehand took years of laboratory work.
- Precision Diagnostics at Scale: AI methods will dramatically enhance early detection of circumstances like most cancers, heart problems, and neurological issues by way of sample recognition throughout huge datasets.
- Personalised Therapy: Therapy plans will probably be repeatedly refined based mostly on particular person affected person information, adjusting in real-time to maximise effectiveness and sufferers’ engagement in their very own care.
The tempo of those adjustments will probably be startling. AI improvement is like canine years—however with exponential acceleration. We’re going to see what might need taken 50 years of typical analysis and implementation.
These aren’t distant science fiction situations—they’re already rising in early kinds, it’s not the long run, it’s now.