Bringing innovation to life in healthcare: insights and strategies for non-clinical leaders
Innovation in healthcare doesn’t happen in a vacuum; the successful transformation of ideas into impactful solutions relies on collaboration across disciplines.
While clinicians and researchers often drive the development of new technologies and practices, it’s the partnership with non-clinical experts – from IT specialists to operational managers – that brings these innovations to life. GE HealthCare’s Prof Dr Mathias Goyen joined a panel of experts at HIMSS Europe 2025 to discuss bridging the gap between clinical needs and operational execution.
Experts designed this interactive session to help non-clinical leaders understand clinical breakthroughs, explore why innovations sometimes fail, and recognise their role in driving sustainable change.
Meet the panel
Prof Dr Mathias Goyen, Chief Medical Officer for Imaging and Advanced Visualization Solutions (AVS) at GE HealthCare, Germany
Mathias Goyen, Prof. Dr.med. | LinkedIn
Prof Maurizio Cecconi,* Head of the Department of Anaesthesia and Intensive Care, Humanitas Research Hospital and Hunimed, Rozzano, Italy
Prof Dr Marie Brevet,* CEO of BIWAKO and Technipath, and Clinical Pathologist at Synlab, France
The need to address operational pressures
The COVID-19 pandemic placed unprecedented strain on intensive care units and exposed deep vulnerabilities in healthcare systems. It served as a stress test, revealing how unprepared many countries were for a crisis of this scale.
However, the underlying issues go back further. Europe’s ageing population is reshaping healthcare demand, with more patients over 65 requiring complex, long-term care. At the same time, the healthcare workforce is shrinking, creating a growing imbalance: more patients, fewer professionals.
Maintaining current standards under these pressures will soon become unsustainable. Innovation is essential to rethink how care is delivered and to boost the efficiency and impact of healthcare’s limited human resources.
Research: the engine room of innovation
Medicine has long rested on three pillars: clinical excellence, research and education. Today, a fourth is emerging as essential—innovation. But innovation cannot thrive alone; it must be rooted in a strong research culture.
Unfortunately, many health systems now treat research as a cost rather than an investment. This is short-sighted. Research funding consistently delivers long-term benefits—not just through new treatments, but by fostering excellence in patient care.
Evidence from the NHS shows hospitals with higher research funding per bed have lower-than-expected mortality rates. This isn’t just about new drugs—it’s about working in research-active environments where professionals stay current, apply evidence-based practices and strive for continuous improvement.
Innovation, when built on research, strengthens medicine’s foundations and helps health systems stay responsive, efficient and resilient in the face of growing challenges.
In addition to ongoing research, successful innovation requires careful planning around costs and funding, technological and organisational maturity, strategic alignment, and a robust economic model. The speed of development and timing of implementation for an innovative project depend on multiple interrelated factors. A primary consideration is funding; these projects often incur significant costs, and financial support may come from public sources, research programmes or internal business models. In private organisations, a clear need for return on investment is typically required before such initiatives are undertaken.
Interconnectivity – bridging the gap between clinic and data
Digitizing laboratory workflows highlights both the challenges and benefits of healthcare innovation. In France, public hospitals have advanced thanks to government funding, while the private sector lags behind due to limited financial support and unclear ROI. Yet digitalisation and AI-enhanced workflows will soon be mandatory across all labs.
Some teams launch projects only after securing funding, while others start small and attract investment by proving viability. When leaders implement innovation wisely, they can recover costs through long-term savings and greater clinical efficiency.
Data Integration
No matter the budget or hospital type, the real challenge lies in connecting the systems, processes and data that support clinical care. Healthcare produces vast amounts of data, but much of it remains siloed and lacks interoperability—especially in time-sensitive areas like pathology, where delays can affect patient outcomes.
To enable precision medicine, data systems must be not only interoperable but also integrated in real or near-real time. This calls for open data environments and shared standards, where clinical, engineering and data science teams work together to build connected infrastructures.
Breaking down silos and fostering collaboration lays the groundwork for faster diagnostics, smarter treatment decisions and better patient outcomes.
Future Skills
To realise healthcare innovations, the next generation of medical professionals must gain expertise in both medicine and data science. Hospitals and universities are increasingly blurring traditional boundaries, shifting toward interdisciplinary education. For example, Humanitas University and Politecnico University of Milan jointly launched the MEDTEC degree, which integrates medical training with biomedical engineering from the start.
This integrated programme combines medical training with biomedical engineering from the outset, rather than treating one as a supplement to the other. Courses are co-taught by physicians, biomedical engineers and scientists across disciplines, fostering a mutual understanding between fields. The goal is to develop a new professional profile – ‘doctor-engineers’ – capable of driving innovation by combining clinical insights with technological expertise. This model reflects the future of medicine, where engineering and data science are not just adjuncts, but rather core components of medical education and practice.
Innovation in intensive care
Critical illness often begins outside the ICU—on general wards or in emergency departments. Improving outcomes depends on early recognition of deterioration, immediate treatment, and rapid mobilisation of intensive care expertise.
In many cases, care must reach the patient before ICU transfer. Time is critical; even short delays in treatment can affect survival. To respond effectively, clinical data and monitoring systems must be interconnected across departments.
This integration enables real-time detection, decision-making and escalation of care. Extending intensive care beyond its traditional walls—via early warning systems, rapid response teams and connected platforms—can significantly improve the speed and quality of treatment for critically ill patients.
Personalised Care
Intensive care must also evolve from a one-size-fits-all model to personalised, physiology-informed care that recognises and responds to the complexity of each individual patient. This shift can be aided
by borrowing lessons from fields like oncology and haematology, which have made strides in personalised treatment. Rather than relying solely on disease labels, we should aim to identify physiological phenotypes based on real-time data and patient-specific physiological footprints. Stratifying patients according to how their bodies respond enables more precise, tailored interventions and increase the likelihood of meaningful positive outcomes.
The physical environment of the ICU also plays a profound role in the healing process, reducing stress, lowering the incidence of delirium, and improving the wellbeing of both patients and healthcare professionals. In some countries, where greater resources are available, we are beginning to see modern ICUs designed with natural light, green spaces and even virtual environments that offer a more human-centred experience. When integrated from the start, these enhancements may not significantly increase costs compared to traditional layouts. Even in resource-limited settings, where rebuilding an ICU is not feasible, innovation in this regard is still possible on a smaller and more gradual scale.
Pursuing precision medicine through data-driven research
More experts now recognise that traditional research and clinical trial methods—especially in intensive care—no longer meet today’s needs. . Unlike treating a single well-defined disease, intensive care often involves managing complex syndromes – clusters of clinical signs and symptoms that can stem from a multitude of underlying causes.
Additionally, to improve feasibility, reduce costs and save time, researchers often broaden inclusion criteria for clinical trials, which leads to highly diverse study populations. This variety dilutes potential treatment effects, as what may be beneficial for one subgroup could be neutral or even harmful for others. Such heterogeneity likely explains why up to 80 per cent of randomised controlled trials (RCTs) in intensive care yield negative results, meaning that potentially valuable treatments are prematurely dismissed.
Moreover, RCTs in critical care are notoriously expensive, difficult to execute and slow to translate into clinical practice. Randomising severely ill patients also raises ethical and practical concerns, making both patients and clinicians hesitant to participate. Machine learning techniques, such as federated learning, offer a promising approach for advancing scalable, data-driven research in intensive care. Federated networks enable collaborative data collection and analysis across institutions, while keeping patient data stored locally, preserving privacy and complying with data protection requirements.
Novel technologies enabling the shift to digital histology
Histology has long relied on manually examining tissue samples under a microscope—a workflow unchanged for decades. Digital histology transforms this by using whole slide scanners to create high-resolution virtual images, enabling faster, more flexible diagnostics.
A key component is the image management system (IMS), which displays patient data, slide details and supports case assignment, collaboration and reporting. For full functionality, the IMS must be seamlessly integrated with the laboratory information system (LIS), ensuring a cohesive workflow and reducing errors.
Without this integration, the IMS is just a viewer—not a diagnostic tool. Moving from microscopes to screens marks a major shift in histology, improving efficiency and elevating patient care. It positions healthcare for a more data-driven, innovative future.
AI in Diagnostics
Digital histology also paves the way for a connected, future-ready diagnostic environment, integrating AI tools that can further enhance precision and efficiency. AI algorithms can greatly alleviate strain in increasingly understaffed pathology labs, improving efficiency, standardising detection, and supporting more consistent and timely diagnoses. For instance, in cervical cancer screening, identifying HPV infection traditionally requires manually examining large numbers of cells under a microscope. Histologists must carefully scan each slide, visually assessing clusters of cells to detect signs of HPV infection or dysplasia, which is an extremely time-consuming and detail-oriented process.
New AI tools that can capture and analyse slide images, automatically highlighti suspicious areas for the histologist to review, have significantly streamlined this workflow , reducing the time required to interpret slides by a factor of four.
IT as the Backbone
The IT infrastructure is obviously the foundational pillar of a digital histology ecosystem, as poor IT performance – such as long slide loading times or image quality issues – can derail the entire initiative, and pathologists will quickly revert to traditional methods if the novel tools are inefficient or unreliable. This underscores the need for early and continuous involvement of IT professionals, whose expertise will ensure that the infrastructure supports high resolution imaging, efficient data storage, software interoperability and user satisfaction.
Human-centric AI development
AI offers opportunities to visualise complex data, identify patterns and flag early signs of deterioration, such as a patient trending toward sepsis or developing electrolyte imbalances. However, caution is essential; AI excels at finding correlations, but not all associations imply causation. Without clinical context and expert interpretation, reliance on raw algorithmic outputs can lead to flawed conclusions. This is where the concept of augmented intelligence becomes critical; AI should enhance, rather than replace, human judgement to combine computational speed with clinical insights, helping clinicians make better, faster and safer decisions.
This approach will require a clear social contract to be established for the implementation of AI in healthcare. Many researchers and healthcare professionals believe that individuals’ medical data should be used to benefit the larger healthcare community and future patients – provided it is anonymised to protect patient privacy – but it is important to strike a balance between innovation and compliance with regulatory frameworks such as the EU Artificial Intelligence Act. While Europe is often criticised for being overly focused on regulation at the expense of innovation, this challenge should not become an excuse for inaction. The time to act is now; Europe must push forward, building on its ongoing efforts to establish frameworks for data exchange and patient information portability. Ultimately, this should enable cross-border interoperability for health equity and quality, improving both individual patients’ lives and population health.
Bringing innovation to life through collaboration
Successful integration of new tools or workflows requires more than funding and executive support. Clear project goals must be defined early, outlining the purpose and expected outcomes of the transformation.
Equally important is assembling the right team by involving clinical, technical and managerial roles from the start. t. Understanding stakeholder responsibilities, setting realistic timelines and fostering collaboration across functions are key to success.
Project leaders should involve non-clinical teams—especially IT and technical staff—early to ensure infrastructure and systems align with operational needs. . A dedicated project manager can be pivotal, ideally someone outside daily lab operations who brings fresh perspective and objectivity.
This role includes coordinating teams, tracking milestones and maintaining regular, focused follow-ups. Asking tough questions and challenging assumptions helps keep the project on track and aligned with its goals.
Integrating new technology meaningfully into clinical practice also requires strong governance. Clinical governance goes beyond simply providing bedside care; it’s about fostering a collective responsibility across the entire healthcare team – including non-clinical roles – to ensure that care is safe, effective, equitable and centred on the patient’s best interests. It also encompasses a commitment to the wellbeing of the healthcare professionals themselves. Clinical governance is therefore not a bureaucratic add-on, but a vital framework that holds the healthcare system together, guiding interdisciplinary collaboration and innovation to support more sustainable and humane care delivery.
Looking ahead to a more connected healthcare future
In today’s clinical environment, many practitioners find themselves spending more time navigating electronic systems than interacting with patients, all while managing increasingly demanding workloads and long hours. Technological innovations have the potential to alleviate some of these pressures by streamlining workflows, reducing administrative burdens and enhancing clinical decision-making, as well as enabling medical professionals to spend more time with patients and their families. Thanks to the continuous development and adoption of novel tools such as robotics, AI and
digital pathology, healthcare will no doubt look very different in the future, laying the groundwork for streamlined, personalised and more connected care.
However, the integration of innovative technologies and tools undoubtedly brings with it several challenges – technical, organisational, human and financial. Healthcare innovation therefore relies on cross-disciplinary collaboration, often requiring partnerships with industry under strict regulatory frameworks. Non-clinical professionals play a critical role in the successful uptake of new technologies by designing robust, scalable systems, anticipating technical failures and ensuring tools operate reliably in real-world settings, demonstrating that cross-functional teamwork is essential to delivering effective, sustainable and innovative healthcare solutions.
*Footnote: Prof Maurizio Cecconi, Prof Dr Marie Brevet and GEHC do not have a contractual relationship beyond the fact of being GEHC product end users. The statements by GEHC customers are based on their own opinions, and on results that were achieved in their unique settings.
