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Digital Pulse

Digital Pulse

By: Pharmatica
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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.Copyright 2026 Pharmatica Biological Sciences Economics Science
Episodes
  • How AI Is Opening New Opportunities in Fertility Care for Pharma

    How AI Is Opening New Opportunities in Fertility Care for Pharma
    Aug 25 2026
    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; Caroline Biddle, Founder at Working Through Fertility; and Dr Divpreet Sacha, Neko Health GP, Co-Founder at HHEI (The Hormonal Health Equity Initiative) and Digital Safety Officer.The discussion explored why fertility innovation cannot be limited to treatment alone. The next opportunity is actually in linking together the data, 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 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, digital therapeutics, companion technologies, diagnostics integration and data-driven care pathways.TakeawaysAI can streamline fertility data analysis and supportPolicy alone is insufficient without cultural changeLine manager training is crucial for supporting fertilitySupportive policies can improve retention and loyaltyTechnology can help break down taboos around fertilityChapters00:00 Introduction to Fertility in the Workplace02:56 Understanding the Gaps in Workplace Fertility Support06:01 The Role of Technology in Fertility Journeys08:58 The Impact of Disclosure on Career Progression11:50 Policy Gaps and Psychological Safety15:08 The Importance of Line Manager Training17:49 Cultural Change and Leadership in Fertility Support20:54 AI and Digital Tools in Fertility Management23:56 Creating Tailored Strategies for Fertility Policies26:50 The Future of Fertility Support in the WorkplaceFor more information, find Clara Toomb’s Fertilia AI platform at fertilia.ai, learn more about Caroline Biddel’s training and consultancy work through Working Through Fertility, and find out more about Dr Divpreet Sacha’s work with the Hormonal Health Equity Initiative (HHEI).
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    32 mins
  • AI in Pharma: Why Kate O’Reilly Says the Future of Healthcare Starts With Patients, Not Technology

    AI in Pharma: Why Kate O’Reilly Says the Future of Healthcare Starts With Patients, Not Technology
    Jul 2 2026
    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, sat down with Kate O’Reilly, President and Chair, Board of Directors, Healthcare Businesswomen’s Association Dublin Chapter and Healthcare Transformation Partner, Roche. 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 SciencesHow 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 FundingHow AI is changing patient outcomes in pharmaHealthcare 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'tKey TakeawaysTrue patient engagement ensures patients have a permanent seat at the decision-making tableWhen 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 ...
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    24 mins
  • AI in Pharma: Hype vs. What Actually Works

    AI in Pharma: Hype vs. What Actually Works
    Jul 2 2026
    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, 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, 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'tWhere AI Is Actually Delivering Value Right NowWhen asked where AI is truly making an impact in pharma, 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 SuperintelligenceWhere Leaders Should Focus for the Next DecadeLooking 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 FundKey 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.Chapters00:00 Introduction to AI in Pharma03:02 Understanding Machine Learning vs. Large Language Models06:00 AI's Role in Pharma Business Processes09:09 Challenges of AI in Regulated Environments12:11 Practical Applications of AI in Pharma15:12 Balancing Accuracy and Explainability17:55 Responsible Adoption of AI in Pharma20:57 ...
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    19 mins
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