Digital Pulse

About Digital Pulse

Digital Pulse explores AI, data, digital therapeutics, real-world evidence, remote patient monitoring, cybersecurity, clinical workflow automation, interoperability, digital health strategy and the infrastructure behind modern life sciences transformation.

Publisher

Pharmatica

Category

business · science · technology

Language

en

Explicit

No

First episode

8 Jun 2026

Latest episode

11 Nov 2026

Latest episodes

8 episodes in the feed.

1. 

   11 Nov 2026

   Why Biopharma Scientists Spend Up to 80% of Their Time on Data Cleanup

   Monoclonal antibodies and other biologics still anchor therapeutic pipelines across the industry, but the software behind them hasn't kept pace. On a recent episode of The Digital Pulse (https://pharmatica.io/interview-series/digital-pulse), host Trisha Pillay (https://www.linkedin.com/in/trisha-pillay-5445531b/) spoke with Lily Helfrich (https://www.linkedin.com/in/lilyhelfrich/), a product manager at Benchling (https://www.benchling.com/), about what it takes to build a data foundation that serves antibodies today and new modalities tomorrow. Helfrich says antibodies make up more than half of large-molecule pipelines, raising the stakes for getting the data right. Why Legacy Biopharma Data Systems Slow Antibody Discovery Legacy tools, Helfrich says, tend to treat an antibody as a flat object: a sequence, or a GenBank file. In reality, it is built in layers. She likens it to a Lego model, one large structure made up of bricks that each have their own properties. Flatten it, and scientists lose sight of how the parts connect. That is where lineage breaks down. A team might keep sequences in one registry, expression and purification results in an electronic lab notebook, and plate-based work in a third system. Piecing them back together takes time. Helfrich points to McKinsey research suggesting scientists and computational teams are “spending up to eighty per cent of their time finding and cleaning data.” Every hour spent on that is an hour not spent on experiments, and AI projects that need clean, connected inputs suffer too. Building a Biologics Data Language Layer Faced with that mess, many IT teams build their own registry on relational databases and cloud data lakes. Helfrich calls it an expensive trap. A schema designed around a standard IgG can buckle when it meets bispecifics, multispecifics or fusion proteins, and each new format brings more maintenance and more extension work. Roadmaps fall behind the science, and before long people are back in Microsoft Excel. Benchling’s answer is what Helfrich calls a language layer. Instead of hard-coding a fixed list of antibody types, the platform relies on Veritas, a nomenclature developed by industry figures at Amgen, to give the data structure while leaving customers free to define their own formats. A scientist types in a string and the system draws the antibody, works out how the chains and domains relate, and picks out the CDRs and frameworks within the variable regions. No custom code is needed. The same approach, Helfrich says, can stretch to other multi-component molecules, including antibody-drug conjugates. Lessons From an 80 000 Antibody Project Migration is one of the first questions prospective customers ask, because decades of data may be unstructured, uncharacterised or stored as flat sequences with no record of which chains belong together. Helfrich says AI is well suited to the problem. Native agents can analyse thousands of unclassified legacy antibodies, infer each format, generate a Veritas string, validate the data and register entities automatically. She described a customer that replaced six disconnected platforms, covering registration, notebooks, inventory, assay tracking and request management, across four research sites. The project took 15 months. The company onboarded about 250 scientists, standardized more than 250 antibody formats and migrated 80,000 legacy antibodies. Within a month of going live, it had registered nearly 4,000 new ones. With the data structured, Helfrich says, IT could shift from cleaning records to bigger goals such as "lab in a loop" workflows and predictive modelling. Unifying Biology and Chemistry Data Discovery is moving past the divide between small and large molecules. Antibody-drug conjugates, peptides and other hybrids combine both, yet most software still splits biology and chemistry into separate systems. For an ADC, antibody data, linker and payload chemistry, and conjugation details such as drug-to-antibody ratio can end up in different databases or in free-text fields. Peptides face a similar problem as registered in small molecule systems; they often lose the sequence-based view that helps scientists understand them. Benchling's response is a single data model built on HELM notation as a common standard, with domain-aware languages such as Veritas and SMILES layered on top so antibody scientists and chemists each see what they need. The company has already added support for modified RNA and oligos, is building ADC workflows that detect possible conjugation sites, and is extending the same foundation to peptides. Helfrich says the stakes are highest for AI. Models are only as good as their training data, and today's antibody models predict binding and developability well but have far less data on how a full antibody format affects drug success. Benchling's Model Hub brings tools such as AlphaFold, Boltz and Chai directly to scientists, and the company expects teams of agents, which it calls science foundries, to handle routine tasks while scientists review and QC the results. Her advice for biopharma leaders is to structure data at the source, make registration a natural part of everyday workflows, and invest in an adaptable enterprise language layer instead of temporary point solutions or rigid databases. If you would like to find out more about this, visit benchling.com (https://www.benchling.com/) or follow Lily Helfrich (https://www.linkedin.com/in/lilyhelfrich/) on LinkedIn. Listen to the full episode of The Digital Pulse (https://pharmatica.io/interview-series/digital-pulse) for more on modernising biopharma R&D. Takeaways Why fragmented systems leave scientists spending up to 80 per cent of their time managing data. Why complex antibodies need structured, adaptable data models. How AI can accelerate antibody data migration and validation. How one customer consolidated six platforms and migrated 80,000 antibodies. Why connecting biology and chemistry data matters for emerging modalities. How structured data supports more effective AI models. Bringing AI tools into everyday scientific workflows. Building adaptable data foundations for future drug discovery. Chapters 00:00 Why biopharma’s data foundation needs to keep pace 01:39 Why antibody records need more than flat sequences 04:54 The hidden cost of building custom biologics registries 08:27 Veritas as a flexible language layer for antibodies 11:51 AI-assisted migration of legacy antibody libraries 14:11 How one team consolidated six platforms and 80,000 records 16:16 Why hybrid modalities break siloed biology and chemistry systems 24:02 A shared data model for biological and chemical languages 29:02 Why AI models need complete molecular context 35:10 Model Hub, science foundries, and human review 38:13 Building adaptable software architecture for future pipelines

2. 

   28 Oct 2026

   How AI Is Shaping the Next Generation of Clinical Trials

   Technology is changing faster than ever, with changes taking months that once took years to even start. Thanks to AI, researchers are identifying potential drug candidates more quickly, but faster discovery doesn’t automatically mean accelerated clinical trials. Clinical development still involves coordinating sites, reconciling data from different systems and collecting enough evidence to establish whether a treatment is safe and effective. For pharma companies, the challenge is carrying advances in research into the clinical development process. In a recent Digital Pulse (https://pharmatica.io/interview-series/digital-pulse) conversation, host Deepankar Nayak (https://www.linkedin.com/in/deepankar-nayak/) spoke with Lisa Moneymaker (https://www.linkedin.com/in/lisa-moneymaker/), Chief Operating and Strategy Officer at Medidata (https://www.medidata.com/en/), about why trials remain difficult to accelerate and where AI could make a practical difference. Lisa, who leads strategy at a company that has built clinical development systems for 27 years, describes an inflection point between what AI makes possible and what organisations are actually achieving. Her diagnosis is that the barriers are less about algorithms than about data, regulation and people. The AI Adoption Gap Lisa identifies two sets of barriers to adoption for the pharmaceutical industry. The first is general AI adoption, where interest is high but measurable return is scarce. She points to three causes, the first of which is data silos, which limit what models can do when information sits in disconnected systems. The second points out to narrow deployment, where only a few teams have access, confines gains to isolated pockets rather than the handoffs between user groups where trials actually slow down. Lastly, AI throttling, which means usage caps and token monitoring, forces companies to ration a tool that only pays off when it’s available across the workflow. The second set is industry-specific: trial complexity, regulation, and resistance to change. Trials now collect more data, involve more intricate site procedures and run more complex analyses than ever. And because the sector is regulated for good reason, sponsors are cautious. For a small biotech developing a single drug, changing established trial procedures can carry considerable risk. Even when regulators signal that they are open to AI, sponsors may hesitate if they are unsure how auditors and inspectors will assess its use. Lisa argues that regulators need to communicate their position more clearly, giving companies greater confidence to adopt new approaches without jeopardising their trials. Clinical trial data is often spread across multiple systems, making it difficult to identify inconsistencies or detect meaningful patterns. Researchers may need to compare wearable and sensor data with patient-reported outcomes, electronic health records and electronic data capture (EDC) data to understand what is happening during a trial. She shares that better data harmonisation and interoperability would make this easier. Approaches such as zero-copy data sharing can allow teams to access and analyse data across systems without having to move or duplicate it, reducing dependence on any single platform. Where AI Can Make a Difference in Clinical Trials Lisa also sees the greatest opportunity for AI before a trial begins, when decisions about endpoints, site demands and data collection can still be changed. By thinking carefully about endpoints, site burden and patient burden at the concept stage, teams can design tighter trials and better data-collection forms. Simulation can then estimate operational outcomes, such as likely dropout and site performance, and increasingly the safety and efficacy outcomes themselves. With roughly nine in ten trials still failing, she likens the status quo to flying in an industry where most flights don't land, and notes that aviation and automotive have long relied on simulation. During execution, she suggests the goal is not perfectly clean data but an accurate representation of the dataset for the analysis at hand, balanced against site burden and statistical relevance. Detecting signals in real time, rather than bunching work around database lock, lets teams confirm whether a trial is tracking its expected safety and efficacy profile. She also highlights global momentum. Australia is cited for some of the shortest study start-up times in the industry, China for its rapid rise in novel drug approvals, and, in the US, a recent Advanced Research Projects Agency for Health (ARPA-H) (https://arpa-h.gov/about) initiative that asks whether first-in-human to approval could be cut to six years. Lisa sees three technology pillars within that effort: trial simulation, data cleaning with signal detection, and agentification of workflows. Paired with adaptive, multi-arm study designs that effectively run several trials in one, these could shorten timelines. On risk, her position is unambiguous. Agents should move work to the next best action, handle document exchange and reduce the number of hands touching a trial, but critical decisions stay with people. Models must be auditable, showing which records they used and what they ignored, so experts can accept or challenge their logic. The industry's existing quality-by-design and risk-based quality management principles, she says, already provide the framework. Change Management Is the Real Bottleneck For Moneymaker, adopting AI also means revisiting the procedures that govern clinical trials. Standard operating procedures, work instructions and monitoring practices must be rewritten, with the quality function evolving alongside the technology. She also stresses the workforce dimension: employees are understandably anxious about AI, and leaders should be explicit about what lies on the other side. If trials become faster and cheaper, a discovery pipeline that AI is also expanding will refill the work to be done, and sites will need support for the growth in volume. Implementing these changes will require coordination between technology vendors, sponsors and CROs. Technology vendors track regulatory shifts and help sponsors control risk. Sponsors retain responsibility for oversight, including of the CROs that execute much of the work. CROs become value-adding partners when technology gives them efficiency and gives sponsors visibility. Collaboration with AI developers such as NVIDIA and Anthropic, she adds, pairs frontier research with the industry's understanding of what is clinically relevant and patient-safe. If you would like to learn more, visit medidata.com (http://medidata.com/) or follow Lisa Moneymaker on LinkedIn. Takeaways Data silos significantly hinder AI effectiveness in clinical trials. Regulatory postures are evolving to embrace AI, but risk aversion remains. Trial complexity has increased, requiring innovative design and simulation. AI can shift trial design left, reducing costs and increasing success rates. Agent-based workflows can streamline data management. Global regulatory differences impact AI implementation strategies. Chapters 00:00 Primer on AI’s Role in Clinical Development 01:57 Real-World AI Integration and Deployment Hurdles 04:11 Overcoming Isolated Data Systems and Interoperability 06:03 Navigating Regulatory Stances and Mitigating Risk 08:11 How Disconnected Information Throttles AI Power 11:08 Real-Time Signal Analysis and Preemptive Data Handling 15:07 Overhauling Legacy Processes for Modern Trials 17:48 Shifting Left: Simulation and Trial Architecture 21:00 Strategic Industry Partnerships and Long-Term Vision

3. 

   22 Oct 2026

   How Sensor-Enhanced Patient Data Completes the Story of the Trial

   Think about the last time a doctor asked How is your sleep pattern? Chances are you probably said “not great” or “fine, mostly.” In some cases, you can’t even remember and might have been tossing and turning. In clinical trials, those answers do matter. But a summary relies on memory, and at that hour, memory isn’t always reliable, especially at 3 a.m. This is the gap where Dr Helen Brooker (https://www.linkedin.com/in/helen-brooker-794611173/), Director of Product and Clinical Data at Signant Health (https://signanthealth.com/), spends her time. A neuropsychologist by training, she joined Digital Pulse (https://pharmatica.io/interview-series/digital-pulse) host Trisha Pillay (https://www.linkedin.com/in/trisha-pillay-5445531b/) to explain why pairing questionnaires with wearable sensors may be the most practical upgrade clinical trials can make right now. Sensor-Enhanced eCOA Clinical outcome assessments (COAs) are still the backbone of trial endpoints. They’re how patients tell researchers how they feel and function, whether through a clinician interview or a self-reported diary. Brooker doesn’t argue against that, but she shares that questionnaires capture only what people can notice and recall. A wrist sensor works quietly in the background, day and night, with nothing to remember or fill in. Someone might report a poor night’s sleep, and the sensor can show how many times they woke and how long they actually slept. “You’re missing half of the data when you don’t have sensors,” Brooker said. She was clear that sensors aren’t more reliable than questionnaires, and that isn’t the pitch. The two answer different questions, and the value shows up when they sit side by side. Her best example comes from eczema research. She described a study of just over 100 patients, followed across roughly 1,600 nights. Each wore a wrist sensor to measure scratching, and each also reported daily how itchy they felt and how they slept. The results appeared in the British Journal of Dermatology in 2023. How itchy people said they were turned out to be a weak predictor of how much they actually scratched. Brooker was careful to say nobody was being dishonest. What we feel and what our bodies do while we sleep are simply different things. For a condition that wrecks sleep, work and home life, that difference is the whole point. A trial relying on the diary alone would be reading half the chapter. How Wearables Fit Into Clinical Trial Design If the value is so clear, why aren’t sensors routine? Brooker has seen this happen before. She spent years in computerised cognitive testing, when plenty of people wanted to stay with paper scales, and it took decades for digital to become standard. Some of the same caution has followed sensors; however, she thinks the fear is fading. Most of us wear a watch or fitness tracker already, so asking a participant to do the same isn’t a big leap. Setup means installing an app and pairing the device, and after that the data flows on its own. The ongoing burden, she said, is mostly charging, and sites can monitor adherence as they go. Her strongest argument is that none of this disturbs the trial itself. The protocol stays the same, existing COAs run as usual, and recruitment doesn’t change. Sensors ask nothing extra of patients beyond taking part and wearing the device. What does change is volume. A questionnaire might give you one number a week, or one per visit. A sensor gives you something every day and every night. Teams need to decide up front what a meaningful change looks like, just as they would for any traditional assessment, and plan for realities like the odd missed night of wear. Raw data arrives minute by minute, but device makers have built methods for turning it into measures that fit existing analytic models. Brooker compared it to cognitive testing, where many individual responses get rolled into averages that track change over time. In her view, this isn’t a rewrite of trial design. It’s a change, and it gives sponsors a stronger base for designing the next study. Regulatory Support for Wearable Endpoints Regulatory acceptance is often the question that stalls these conversations, and Brooker sounded optimistic. At the end of 2023, the FDA published dedicated guidance on using digital health technologies such as wearables to collect trial data. In Europe, the EMA has qualified a wearable-derived measure, stride velocity, as a primary endpoint in muscular dystrophy. “It’s not a one-off,” she said. She sees confidence building indication by indication, with each new qualification making the next easier. This momentum is part of why Signant Health acquired Ametris. Brooker credited the company’s CEO, Roger, with seeing early that sensors and wearables will be part of how trials are run. For her, the deal gives clients a fuller picture of the patient journey and supports broader goals like faster, more affordable drug development and sharper go/no-go decisions. Brooker suggested that sponsors could start by adding wearables to an existing eCOA study on an exploratory basis. This would allow them to collect sensor data alongside questionnaire responses without affecting the trial’s primary endpoint. It would also help build evidence for the use of sensor data in future clinical trials. “You’re not betting your trial on it,” she said. Brooker expects sensor data to become a standard part of clinical trials, with sponsors increasingly questioning why they ever relied on questionnaires alone. For more information on what we talked about, visit signanthealth.com (http://signanthealth.com/) and Ametris.com (http://ametris.com/). Takeaways Integration of sensors and wearables in clinical trials. Benefits of continuous passive data collection. Regulatory perspectives and approvals. Impact on trial design and data analysis. Case study: eczema trial with wrist sensors. Signant Health and Ametris partnership. Chapters 00:00 Introduction to sensors and wearables in clinical trials 00:54 Helen Brooker's background and passion for clinical research 03:42 The added value of sensors to clinical outcome assessments 04:06 How sensors provide invisible, objective data 07:40 Real-world example: eczema trial with wrist sensors 10:55 Why sensors are not yet routine in trials 13:52 Signant Health and Ametris partnership for innovation 16:49 Designing trials with continuous sensor data 19:01 Analysing sensor data in clinical trials 20:47 Patient recruitment and data accuracy 22:01 Regulatory perspectives and approvals 25:12 Integrating sensors with established assessments 28:23 Add sensors to existing ECOA studies 29:34 Closing thoughts and future outlook

4. 

   25 Aug 2026

   How AI Is Opening New Opportunities in Fertility Care for Pharma

   Fertility care is beginning to serve as an example of where the next wave of healthcare innovation will be developed. The opportunities for pharmaceutical companies go well beyond the development of new treatments, since AI, digital health platforms and workplace technologies are providing new methods of supporting patients, clinicians and employers throughout what is still a fragmented fertility journey. In the recent episode of the Digital Pulse podcast, host Shubhangi Dua was joined by Clara Toombs, Founder at Fertilia AI (https://www.linkedin.com/in/claratoombs/); Caroline Biddle, Founder at Working Through Fertility (https://www.linkedin.com/in/carolinebiddle1/); and Dr Divpreet Sacha, Neko Health GP, Co-Founder at HHEI (The Hormonal Health Equity Initiative) and Digital Safety Officer (https://www.linkedin.com/in/dr-divpreet-sacha-mbchb-mrcgp-dipoccmed-22506b11a/). The discussion explored why fertility innovation cannot be limited to treatment alone. The next opportunity is actually in linking together the data (https://pharmatica.io/interviews/how-ai-reshaping-data-integrity-pharma-manufacturing), the people, and the support associated with treatment. Fertility Care: A Digital Health Opportunity? The pharmaceutical industry has always looked at fertility in terms of drugs, diagnostic tools and clinical results, but the podcast pointed out a wider ecosystem in which technology could help patients deal with their treatment and enable clinicians to interpret the information. Fertilia AI by Toombs is intended to assist couples in understanding their fertility data by incorporating their test results onto a platform on which AI can support patients before they have their clinical consultation. Toombs explained that the platform is being built from the ground up for couples, not just for women. When asked about how the initiative plans to make an impact, she says, "If I had to get great minds in a room, I'd be looking at it from a tech perspective. How can we take all this knowledge, apply it into policies that can be easily distributed?” Especially when “looking at the macro situation that we're in at the moment with declining birth rates, not just in the UK, but globally across many, many countries,” she added. It is also important from a commercial point of view. Fertility is not something that relates only to one user. Treatment may involve two partners, a number of clinicians, various diagnostics, medications and a series of decision points. AI serves as the layer (https://pharmatica.io/interviews/ai-pharma-hype-vs-what-actually-works) that connects those interactions. The aim is to use "expert humans in the loop" in order to get answers more quickly and thus enable patients and clinicians to work with a clearer picture of fertility health. For pharmaceutical companies, this indicates possible opportunities in the areas of patient engagement (https://pharmatica.io/interviews/ai-pharma-why-future-healthcare-starts-patients-not-tech), digital therapeutics, companion technologies, diagnostics integration and data-driven care pathways (https://pharmatica.io/interviews/how-should-pharma-companies-audit-ai-vendors). Takeaways AI can streamline fertility data analysis and support Policy alone is insufficient without cultural change Line manager training is crucial for supporting fertility Supportive policies can improve retention and loyalty Technology can help break down taboos around fertility Chapters 00:00 Introduction to Fertility in the Workplace 02:56 Understanding the Gaps in Workplace Fertility Support 06:01 The Role of Technology in Fertility Journeys 08:58 The Impact of Disclosure on Career Progression 11:50 Policy Gaps and Psychological Safety 15:08 The Importance of Line Manager Training 17:49 Cultural Change and Leadership in Fertility Support 20:54 AI and Digital Tools in Fertility Management 23:56 Creating Tailored Strategies for Fertility Policies 26:50 The Future of Fertility Support in the Workplace For more information, find Clara Toomb’s Fertilia AI platform at fertilia.ai (http://fertilia.ai), learn more about Caroline Biddel’s training and consultancy work through Working Through Fertility (https://workingthroughfertility.com/), and find out more about Dr Divpreet Sacha’s work with the Hormonal Health Equity Initiative (HHEI) (https://www.hhei.org.uk/).

5. 

   2 Jul 2026

   AI in Pharma: Why Kate O’Reilly Says the Future of Healthcare Starts With Patients, Not Technology

   Artificial intelligence (AI) is now making waves in the pharmaceutical and life sciences industry. Healthcare leaders are now dealing with the question: How can AI make patient outcomes better without losing sight of the people it’s designed to serve? In the recent episode of Digital Pulse by Pharmatica, host Shubhangi Dua, Podcast Producer and B2B Journalist (https://www.linkedin.com/in/shubhangi-dua-14a825154/), sat down with Kate O’Reilly, President and Chair, Board of Directors, Healthcare Businesswomen’s Association Dublin Chapter and Healthcare Transformation Partner, Roche (https://www.linkedin.com/in/kate-o-reilly-82150369/). They talk about the future of AI in pharmaceuticals and life sciences, patient engagement, and healthcare transformation. O’Reilly draws from her experience as a pharmacist, her studies in neuroscience, digital health and healthcare system innovation. She tells Dua that healthcare needs a foundational mindset shift. Particularly, the industry is encouraged to move to patient-first rather than technology-forward. This means keeping technology as a secondary step to ensuring deeper, more structural patient inclusion. “Patients really need to have a permanent seat at the decision-making table,” O’Reilly stated, alluding to the fact that healthcare innovation should be anchored in the experiences of the people it ultimately serves. The former pharmacist believes that technology solely will not determine if healthcare innovations are successful. The vision O’Reilly depicts is of patients becoming active participants across the end-to-end care journey, rather than passive recipients. Why is Patient Inclusion Key to Successful Innovation in Pharma? “Patient-centricity” is evidently one of the most used terms in healthcare and pharma over the past decade. However, according to O’Reilly, patient inclusion continues to evolve and remains one of the greatest enablers to the development of successful innovation. Healthcare requires a systemic change that puts patients at the centre of the decision-making process, O’Reilly discusses. By virtue of living with their conditions, they are experts in their own care and the only individuals involved in every single decision made along the end-to-end care journey. They hold critical insights about their care needs that may not be visible to clinicians or researchers. This is spotlighted as the “missing data,” O’Reilly explains, which can change the trajectory of outcomes using technology. Also Read: Strategic Pharma Intelligence Drives Better Decisions in Life Sciences (https://pharmatica.io/insights/strategic-pharma-intelligence) How can AI drive more patient-centred healthcare? Instead of bringing lists of symptoms, patients can now bring clinicians' recommendations, summaries and individualised health insights, with the advent of AI platforms like ChatGPT. However, the real opportunity for AI lies in how patients communicate with their clinical teams, O’Reilly believes. One example of this is its potential role in shared decision-making, an important component of “patient-centred care”, she explains. In this context, there is a potential role for AI when it comes to better informing patients and supporting them in taking a more active role in their care journeys, and also for clinicians when it comes to preparing medical information in a format that is more “patient-oriented”. However, AI will not replace clinicians, O’Reilly asserts. AI is an augmentation tool: “It's not a substitute for medical expertise,” she explained. Also Read: First Quarter 2026 State of Digital Health Funding (https://pharmatica.io/insights/Q1-2026-health-tech-investment) How AI is changing patient outcomes in pharma Healthcare Transformation Partner at Roche illustrates favourable outcomes for patient-first technologies. One of the greatest values of digital innovation is its capacity for continuous data collection. Conditions like MS often involve symptom fluctuations over time. This means that isolated appointments often only capture a discrete snapshot of the patient's experience at a particular moment in time. Continuous data collection can help create a more accurate and nuanced understanding of patients' well-being. As excitement for AI grows in healthcare, organisations risk getting distracted by the technology itself. Instead, O’Reilly calls for “needs-led” innovation, which involves rigorous, evidence-based assessment of the problem being solved for. As she summarised, "We need to fall in love with the problem" in healthcare; creating great solutions means little if the initial problem was misunderstood. Also Read: Where Healthcare AI Investment Is Going — And Where It Isn't (https://pharmatica.io/insights/AI-healthcare-funding) Key Takeaways True patient engagement ensures patients have a permanent seat at the decision-making table When it comes to advancing patient-centred healthcare, some of the greatest potential for AI lies in its capacity to transform how clinicians and patients communicate, and how patients engage in decisions about their care Healthcare transformation should be underpinned by a “needs-led”, rather than solution-driven approach; otherwise we risk building brilliant solutions for problems that may not exist. The user experience of the clinician cannot be ignored. Innovation only moves as fast as clinical adoption. Their readiness to embrace new tools is the bridge to patient impact. Chapters 00:00 Introduction to Healthcare Transformation 03:49 The Role of Patient Engagement 08:50 AI's Impact on Patient-Provider Communication 12:49 Operationalising Patient Engagement 14:12 AI and Digital Health Innovations 18:31 Collaborative Initiatives in Healthcare 20:48 Future of Pharma and Patient Interaction 22:46 Integrating Real-World Insights 25:26 Patient-Centric Clinical Studies 27:28 Key Takeaways for Pharma Leaders #PharmaAI #MachineLearning #Pharmatica #GenerativeAI #DigitalPulse #RegulatoryAffairs #HealthTech #AIValidation

6. 

   2 Jul 2026

   AI in Pharma: Hype vs. What Actually Works

   Pharma leaders are investing a lot in large language models (LLMs) for issues that a simpler, cheaper machine learning model could handle more quickly and validate with less effort. This is the warning from Lara Masad, an AI and Data Innovation independent consultant (https://www.linkedin.com/in/laramasad/), who spoke on the Pharmatica podcast hosted by Shubhangi Dua. The main issue, according to Masad, is that the industry confuses two fundamentally different technologies. "Traditional machine learning models are trained to do one specific task very well," she explained, alluding to examples like deviation rates, batch records, and sensor readings. These models are deterministic, explainable, and highly auditable, which makes them well-suited to a GXP environment. In the recent episode of the Digital Pulse podcast, host Shubhangi Dua, Podcast Producer and B2B Journalist (https://www.linkedin.com/in/shubhangi-dua-14a825154/), sat down with Masad to clarify the key distinctions in AI and machine learning models. Furthermore, Masad explains how pharmaceutical companies benefit from using AI models. LLMs function differently. They are trained on vast amounts of text and generate probabilistic outputs rather than fixed ones. They are designed to be generalists. "The same model that can draft a deviation investigation, for example, can also rewrite a poem," she said. This flexibility is also a challenge: "You can't create a fixed qualification protocol for a model that can respond in a thousand different ways." Masad points out that this difference has real financial implications. "I have seen organisations default to large language models for everything because that’s what’s in the news, when in reality, a well-trained machine learning model would have been faster to validate, cheaper to maintain, and more defensible in front of an inspector." Also Read: Where Healthcare AI Investment Is Going — And Where It Isn't (https://pharmatica.io/insights/AI-healthcare-funding) Where AI Is Actually Delivering Value Right Now When asked where AI is truly making an impact in pharma (https://pharmatica.io/insights/strategic-pharma-intelligence), Masad provided a practical answer: "more areas than people think, but fewer than what vendors would like to suggest." In quality assurance, she highlighted inspection readiness and deviation management as key applications. Identifying risk signals from CAPA records and trending data "before it becomes a 483 observation by the US FDA," she stated, is genuinely valuable. Masad described document processing tasks like gap analysis against changing guidelines, regulatory intelligence monitoring, and drafting response narratives as the current focus. However, she is realistic about the limits: "Full-scale regulatory submission support is still a few years away from being valid in most markets and for most regulatory bodies." The use case she finds most promising is inspection risk prediction. She believes it offers "a clear return on investment, a clear validation pathway, and a clear regulatory rationale" because it relies on machine learning instead of generative AI. Also Read: Reimagine AI-driven Drug Discovery with Pharmaceutical Superintelligence (https://pharmatica.io/insights/pharmaceutical-superintelligence) Where Leaders Should Focus for the Next Decade Looking ahead, Masad identified three priorities for pharma organisations serious about long-term AI capability, ranked in order: people, regulatory understanding, and localisation. Regarding people, she argued that the main barrier is not access to technology but having scientists, QA professionals, and leaders who can use it responsibly. On regulatory understanding, she predicted that future leaders "won't just be the ones with the best models" but those who can advance models through validation and approval the quickest. She also shared insights from her health-tech startup GeneAId Ltd, which applies machine learning to genetic variant classification for underrepresented Gulf and Arab populations. She emphasised that AI designed for US and/or European markets does not automatically apply elsewhere, making localisation a critical blind spot for global pharma companies. Her final message tied the three priorities together: "We build a foundation, not a series of one-off projects. AI should compound over time, but only if you have created something worth compounding on." Also Read: What Pharma R&D Tech Investment Committees Actually Fund (https://pharmatica.io/insights/pharma-R%26D-tech-investment-committees) Key Takeaways: ML is deterministic and auditable; LLMs are probabilistic and harder to validate. The real AI value today is in deviation management and document processing, not full regulatory submissions. Validation needs scoped use cases and human review gates instead of fixed test sets. "Human in the loop" isn't enough without clear rubrics and continuous monitoring. People, regulatory fluency, and localisation, not models, will determine who wins. Chapters 00:00 Introduction to AI in Pharma 03:02 Understanding Machine Learning vs. Large Language Models 06:00 AI's Role in Pharma Business Processes 09:09 Challenges of AI in Regulated Environments 12:11 Practical Applications of AI in Pharma 15:12 Balancing Accuracy and Explainability 17:55 Responsible Adoption of AI in Pharma 20:57 Regulatory Oversight and Human Review 23:58 Continuous Monitoring and Validation 26:55 Building Long-term AI Capability in Pharma For more industry-leading insights on AI in life sciences, visit pharmatica.io (https://pharmatica.io/). Reach out to Lara Masad here: https://www.linkedin.com/in/laramasad/ (https://www.linkedin.com/in/laramasad/) Follow Pharmatica.io (http://pharmatica.io/): Pharmatica.io (http://pharmatica.io/) YouTube: @Pharmatica_io Pharmatica.io (http://pharmatica.io/) LinkedIn: https://www.linkedin.com/company/pharmatica-io/?viewAsMember=true (https://www.linkedin.com/company/pharmatica-io/?viewAsMember=true) #PharmaAI #MachineLearning #Pharmatica #GenerativeAI #DigitalPulse #RegulatoryAffairs #HealthTech #AIValidation

7. 

   9 Jun 2026

   How Pharmacy Innovation Is Changing Healthcare Delivery

   A product receives market authorisation. It is clinically validated, commercially ready, and genuinely needed. Then it waits for a guideline review, cost evaluation, formulary decisions and three years later, it reaches an NHS patient. For pharma executives who have spent a decade developing that product, the timeline is not a surprise. But it is increasingly a choice, not an inevitability. Judit Mora (https://www.linkedin.com/in/juditmora), CEO and co-founder of Nuumad (https://www.nuumad.co.uk/), joined Trisha Pillay (https://www.linkedin.com/in/trisha-pillay-5445531b/?skipRedirect=true) on Digital Pulse to make the case that the route through the NHS is not the only route and that the channel most pharma companies have systematically ignored is precisely where the opportunity sits. The Blindspot That Is Costing Market Access Mora opens with a structural diagnosis that should concern any commercial or market access leader. When products are developed, the thinking defaults to two audiences: the patient and the clinician. Pharmacy which dispenses the product most of the time is an afterthought. This is due to innovation funding following the same logic as product development thinking; the gap compounds. "Because this blind spot exists for big pharma, innovation funding doesn't even flow there," Mora says. "It's a self-perpetuating cycle, a lot of tech innovation comes out from incubators run by big pharma, and it's all linked to expectations on their product pipelines." The second failure point is equally familiar to those who have watched digital health initiatives stall: patient-facing innovation built without clinical pathways behind it. Mora's example is Babylon Health, a platform that positioned itself as AI-driven, relied on large volumes of healthcare professionals doing manual work behind the interface, and ultimately couldn't scale because real clinical triage doesn't follow a simple decision tree. "When companies launch 'this is a great patient app', what happens when you actually need clinical intervention? It's always an afterthought." Why the NHS Timeline Is a Strategic Problem, Not Just a Regulatory One Mora is measured about public healthcare systems. They are not broken. They are stretched, and the consequences of that stretch land directly on patient access timelines. The evaluation process the NHS runs is thorough by design: guideline fit is assessed first, cost is scrutinised after, with multiple steps between authorisation and formulary inclusion. For blockbuster products, the biologics in immunology that represent genuine step-changes in patient outcomes, even those remain second-line treatments not because the clinical evidence is weak, but because they are too expensive to deploy at a population scale. "A new product might take three years to get into the NHS and get in front of patients. If you're ill and there's a product that will change your quality of life, that's a significant burden." The private route Nuumad operates within doesn't displace the NHS pathway. It runs alongside it. Get an independent medical evaluation, make the product available through pharmacies or private clinics, and let patients who want earlier access make that choice. "It's a much quicker market access, and it opens up the possibility." The equity question is real, and Mora acknowledges it. But availability is a precondition for access of any kind. The Mechanism: A Prescription Without a Prescription The model Nuumad has built centres on a Patient Group Direction, the same legal framework that enables NHS flu and COVID vaccination services to be delivered by pharmacy technicians and nurses without individual prescriptions. A PGD defines inclusion and exclusion criteria; a clinician who works through those criteria can dispense the product directly. No GP referral. No prescriber in the chain. What Nuumad adds is the clinical user experience layer that turns that legal document into a functional digital platform, one that guides a pharmacist or pharmacy technician through a gold-standard consultation, building clinical confidence as it does. "What we want to do is instil process thinking, even for non-prescribing clinicians who may have never run these types of services." The design goal is explicit: the platform should reduce anxiety, not create it. A clinician using it for the first time should feel guided, not exposed. On AI: Why Nuumad Is Deliberately Not Going There Mora's position on AI in clinical workflows is a useful corrective to the current market noise. Nuumad uses AI for operational purposes only. Clinical decision support runs on deterministic, rule-based algorithms, and the reasoning is worth understanding. AI outputs vary when models are retrained or updated. In a clinical decision context, that inconsistency is not acceptable. European healthcare data models differ materially from the US datasets most large AI systems are trained on. And critically, AI tools in clinical settings still require human validation, which undermines the efficiency case entirely. "If you outsource your own healthcare thinking to a tool that may not give you the right answer, and in the process is not sustainable, why are you using it?" The accountability point is the one most operators haven't fully absorbed: when an AI tool contributes to a clinical error, the liability sits with the clinician who used it, not the manufacturer. "Very often when I ask pharmacists about this, they say: I didn't know that." The Commercial Opportunity Pharma Keeps Overlooking Mora's closing argument is aimed directly at commercial strategy. Pharmacy is an underserved, high-frequency patient touchpoint. The regulatory infrastructure to use it as a market access channel exists. Patient demand is there. What has been missing is the organisational willingness, particularly in larger businesses, to pursue a route that doesn't have the NHS as the primary buyer. "Pharmacy is a big opportunity for businesses that want to innovate and are forward-thinking. Not to ignore pharmacy because it has its struggles, it is definitely ripe for new things." For pharma executives looking at three-year NHS timelines and asking if there is a faster way: there is. For more information on what was spoken about, visit Nuumad (https://www.nuumad.co.uk/) or connect with Judit Mora on LinkedIn (https://www.linkedin.com/in/juditmora). Takeaways Market access and new models for therapies. The untapped potential of pharmacy in healthcare. The role of user experience and product design in healthcare. Impact of public healthcare systems on innovation. Use of AI and digital tools in healthcare. Chapters 00:00 Introduction to Market Access in Pharma 03:53 Judith Mora's Journey in Pharma 07:53 Gaps in Pharmaceutical Innovation and Patient Access 11:53 Public Health Systems and Innovation Barriers 16:07 Innovative Models for Non-Prescribing Healthcare Professionals 19:58 User Experience and Product Design in Pharmacy 24:10 AI in Healthcare: Opportunities and Cautions 30:04 Closing Thoughts on Pharmacy Innovation

8. 

   8 Jun 2026

   How AI and Digital Innovation Are Reshaping Clinical Trials in HealthTech

   Across many parts of Africa, clinical research still depends heavily on paper-based workflows. For pharmaceutical companies managing global trial portfolios, this can create operational challenges around data quality, monitoring, and regulatory readiness, particularly when studies contribute to submissions in the US and Europe. Adriaan Kruger, founder and CEO of nuvoteQ (https://www.nuvoteq.io/) (https://www.nuvoteq.io/)has spent twelve years building the infrastructure to close that gap. His conversation with Trisha Pillay on Digital Pulse is one of the more candid accounts available of what digital adoption in clinical research actually looks like when the constraints are real. Why Paper Persists and What It Actually Costs The persistence of paper in LMIC clinical research settings is not a technology problem. The technology exists. "Our industry is so hesitant and so resistant to change," he says. "They are so risk-averse — which we totally get in this highly regulated world. These researchers are so worried about potentially using a system and then the data gets lost, or the system is down. So they go back to their tried and tested way of doing things, which is on paper." The financial dimension compounds the risk-aversion. Funding does not flow to research sites in sufficient volumes to support digital infrastructure investment. The result is double data entry, a risk mitigation practice where patient data recorded on paper is manually typed into a centralised system twice by different operators, then compared for discrepancies. In developed markets, this practice has largely been eliminated. Across much of Africa, it remains the norm. The downstream cost is significant as data science teams spend extensive time converting inconsistent paper records, Excel files, and incorrectly formatted entries into globally standardised formats before a single submission can be filed. Real-Time Visibility Changes the Risk Calculation The operational case for digital infrastructure is sharpest when Kruger describes what real-time data visibility actually enables at the trial level. Clinical trials running across multiple countries like South Africa, Mozambique, Ghana, Finland, and Australia simultaneously have historically operated with delayed information flows. A serious adverse event occurring overnight in South America might take days to trigger a coordinated global response. The pharmaceutical company, the investigators, and the data safety monitoring board are all operating on different information timelines. "If they don't have real-time data, often there's a delayed reaction on how you deal with things," Kruger says. "Now if they have a real-time view of everything happening globally, they can make insightful decisions, let's pivot, let's change the dosage, or there was a serious adverse event, it happened last night in South America. The morning we wake up, everyone knows." For pharma executives managing trial costs, trials are expensive precisely because they run long, across many sites, with significant overhead. The ability to make earlier decisions about dosage adjustment, site performance, or trial termination is a direct cost lever. "You can make decisions on whether to move faster, change direction, or potentially stop a study so that you don't spend more money on it." AI Where It Works in Clinical Research Kruger's position on AI in clinical research is one of the more technically grounded takes in the current conversation. nuvoteQ (https://www.nuvoteq.io/) (https://www.nuvoteq.io/)is not AI-averse, but the firm is precise about where AI delivers and where the industry's enthusiasm is running ahead of its readiness. The clearest proof point is a regulatory review tool built for South Africa's medicines regulator, SAHPRA, in partnership with a Seattle-based philanthropic funder. The challenge was a generic drug dossier, submissions that can run to 15,000 pages, were sitting in regulatory queues for up to nine years across Africa, delaying the availability of affordable medicines for the patients who need them most. The tech company deployed an open-source LLM locally within SAHPRA's data centre, with no external internet access, trained to analyse dossiers and produce a two-page risk summary with hyperlinked references to the source document. "That has reduced the timeline to review from where it typically takes five to six weeks. We can do it in about 45 minutes now." The model works because the data never leaves a closed environment, the task is well-defined and repetitive, and human reviewers remain in the loop for final decisions. That last point is deliberate. "You will always have a human in the loop. There will always be a healthcare professional. AI will help do some of the number crunching, but ultimately, when it comes to patient care, the ethical component is how patients are being treated. I don't think AI is going to be there for a long time." The liability question Kruger raises for clinical AI tools mirrors what is emerging elsewhere in regulated industries: the responsibility for an AI-assisted decision sits with the clinician or researcher who acted on it, not the software provider. Africa as a Strategic Asset Kruger closes with a reframe that deserves attention from anyone structuring a global trial portfolio. South Africa's clinical research infrastructure, built over decades around institutions like the University of the Free State in Bloemfontein, is world-class, not despite its context but in part because of it. "Because of our diverse population just in our country, we are world leaders when it comes to clinical research. During the COVID pandemic, the fact that we discovered some of those strains that's not by chance. It's because we are world-class." Africa's genetic diversity is, he argues, an asset that the global pharmaceutical industry has consistently undervalued. As precision medicine and genomic-driven treatment personalisation move from oncology into broader therapeutic areas, that diversity becomes a competitive advantage for trial design and drug development that no other region can replicate. The infrastructure to unlock it is digital, connected, and validated to FDA and EMA standards, which is what Kruger is building. The industry's willingness to fund and support it is what will determine how quickly African patients stop waiting. Learn more about nuvoteQ (https://www.nuvoteq.io/) (https://www.nuvoteq.io/)and connect with Adriaan Kruger on LinkedIn (https://www.linkedin.com/in/adriaankruger/). Takeaways Digital transformation in clinical research Barriers to digital adoption in pharma Role of AI in clinical trials Future trends in personalised medicine Data sovereignty and privacy in healthcare Chapters 00:00 Transforming Clinical Research: The Digital Shift 03:01 Barriers to Digital Adoption in Pharma 08:13 Data Accuracy and Double Data Entry Risks 11:07 The Role of AI in Clinical Research 19:21 Real-Time Data Visibility in Trials 23:44 Scaling Tech Solutions in Africa 28:36 The Future of Clinical Research: A Vision for 5 Years

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