Healthcare Innovation Models

AstraZeneca and Eli Lilly and Company launch digital health and AI innovation hubs in the Philippines and India >> 🇵🇭AstraZeneca will launch a Health Innovation Hub in the Philippines to attract investment and position the country as an ASEAN center for pharma, R&D, and digital health 🇵🇭 Its first project, an Oncology Innovation Center, will use AI for early cancer detection, expand patient support, and build healthcare workforce capacity 🇵🇭 The hub will drive collaboration and investment through business forums, B2B match-making, and regulatory support within ecozones 🇮🇳Eli Lilly has opened a Technology and Innovation Centre in Hyderabad’s Hitech City, set to expand from 100 staff to 1,500 by 2026–27, serving as a global hub for digital and technology capabilities 🇮🇳 The centre will focus on AI, automation, cloud, and software engineering, acting as a nerve centre linking Lilly’s global sites and accelerating medicine discovery and delivery 🇮🇳 Positioned as a centre of innovation rather than back-office support, it will bring together top talent in AI, data science, and engineering to drive digital transformation in pharma 💬 It’s great to see pharma innovation expanding across south and south east Asia, let’s hope the fruits are transformative vs just incremental #digitalhealth #ai #pharma

AI’s impact on medicine is no longer theoretical—it’s redefining daily clinical practice, medical research, and the very fabric of physician training. Breakthroughs like Google DeepMind’s AlphaFold2 have let researchers predict the structure of nearly every known protein, accelerating new drug development and igniting a wave of biotech innovation. AI models are now outperforming traditional methods—detecting cancer, forecasting disease progression, and driving efficiencies in active compound discovery. On the operational side, hospitals are leveraging large language models to automate clinical documentation and summarize complex records. The result: clinicians spend less time on paperwork—and more time with patients—helping combat burnout and improve satisfaction for both sides. Medical education is also evolving. Universities such as Stanford and Mount Sinai are weaving AI training into their curricula, recognizing that tomorrow’s doctors need to not only master clinical knowledge but also the critical thinking to collaborate with AI tools effectively. Simulated surgical training, AI-powered feedback, and new pharmacy protocols show that the skillset for modern medicine is expanding—and institutions are responding accordingly. Caution is warranted: Algorithmic bias, data privacy, and the need for robust validation remain real concerns. Yet the pace of deployment and the scope of benefit make clear that AI is not a distant disruptor; it’s a core enabler of the industry’s future. Now is the time for healthcare leaders, educators, and innovators to shape policies, invest in talent, and reimagine workflows. Let’s ensure that AI’s integration into medicine truly elevates care, training, and research for all. https://lnkd.in/gwi3htAJ #AIinMedicine #HealthcareInnovation #MedicalResearch #ClinicalAI #HealthTech #AIEducation #FutureOfMedicine #DigitalHealth #MedTech #HealthcareLeadership

How many adverse drug reactions could be prevented if prescribers used pharmacogenetic testing before prescribing? --- A study from last month looked into the adverse drug reaction (ADR) reports for the United Kingdom (in the comments). Of the over 1 million ADRs in the system, 9% were associated with drugs whose side effects could be prevented using genetic testing (AKA #pharmacogenetics / #pharmacogenomics (PGx)) to guide prescribing. From #pharmacy school, you learn that most drugs are metabolized by a limited number of enzymes. These enzymes can vary with patient genetics. 75% of the 9% of ADRs came from just 3 enzyme gene variations (CYP2C19, CYP2D6, SLCO1B1) that have a PGx panel test available. --- In the attached image from the study, you can see that a major opportunity for PGx testing is psychiatry drugs. 47% of the ADRs could be mitigated by PGx testing for these drugs. This would be a major improvement in #MentalHealth treatment instead of relying on trial and error, which requires 4-6 weeks for each drug the patient has to try. --- There's still huge potential for PGx to be better implemented as part of the US healthcare system. Very few providers utilize PGx routinely in practice. This is a major step in #PrecisionMedicine. The next step to make this a part of practice is checking the financial reality--how much do these tests cost and how much value do they provide in reduced adverse reactions, untreated patients, prevented hospitalizations, etc. Even with the psychiatry drugs being cheap generics, the ROI may be worth making these tests a routine part of prescribing.

Your HealthTech startup isn’t a tech company. Treating it like one can be fatal. I’ve watched brilliant founders from SaaS, fintech, and AI stumble in healthcare. Not because they lacked skill, but because they assumed healthcare works like every other industry. It doesn’t. Here’s what makes HealthTech a world of its own: 1. You’re selling to institutions, not individuals. Hospitals, insurers, and regulators move carefully, not quickly. Procurement in large systems can take 18+ months, with decisions driven by risk and compliance over hype. Committees replace single decision-makers, and the biggest competitor is often the status quo. 2. Trust is everything. In healthcare, one misstep - clinical, ethical, or regulatory - can destroy credibility overnight. I’ve seen startups lose traction after minor compliance lapses. The rules around AI and digital health evolve constantly, and staying ahead of regulation is now a core competency, not a checkbox. 3. Adoption is the hardest challenge. Clinicians spend roughly 40% of their day on admin tasks. Patients are already overloaded. If your product doesn’t fit seamlessly into existing workflows, it won’t get used... no matter how elegant the tech. True adoption takes empathy, support, and time. 4. Solve a mission-critical problem. In healthcare, survival depends on necessity, not novelty. “Nice-to-have” tools don’t last. Clinical validation through peer-reviewed studies and real-world evidence matters more than hype. Evidence earns trust—and trust drives growth. 5. Investors now expect proof of outcomes. Funding is shifting toward startups that demonstrate measurable clinical impact and sustainable revenue models, especially in high-need areas like maternal health and chronic disease management. Impact now trumps velocity. 6. Partnerships power growth. Strategic collaborations, like those between pharmaceutical companies and AI imaging startups, are shaping healthcare innovation. They help new entrants navigate regulation, gain credibility, and scale responsibly. 7. Play the long game. Healthcare rewards patience, resilience, and humility. Quick hacks and blitz-scaling don’t work here. The founders who listen, learn, and adapt to the system’s realities are the ones who thrive. HealthTech is healthcare. With just enough technology to make it work better, not worse. What would you add?

🌮 Food for thought 🧠 #PublicHealth is complex. This has already been discussed in plenty of papers. Consequently, the use of #digital tools to achieve typical Public Health Goals (aka #DigitalPublicHealth) could not be less complex. Right? Together with my colleagues Hans-Henrik Dassow, Daniel Diethei, Merle Freye, Jasmin Niess & Stefanie Do, I worked on mapping the fields related to Digital Public Health. The result is this colourful sunburst diagram ☀️ 🔎 What did we find? 🔍 1) Digital Public Health (DiPH) - as for our purpose defined as the overall field - includes the clinical & individual-centered #DigitalHealth 🎯 2) DiPH is colourful! It consists of the overarching fields: Humanities, Environmental Sciences, Social Sciences, Engineering & the Natural Sciences. These can be split into individual disciplines, which also come with their subfields (we kept the figure simple and did not portray every subdiscipline here) 🖍️ 3) Due to this, DiPH is extremely #interdisciplinary - but also the individual subdisciplines are interdisciplinary (especially in the Environmental Sciences). 💫 💥 What does this mean for public health practice?💥 1) The interdisciplinarity of DiPH-related projects should be reflected in the aim, methodology, and selection of team members 🪞 2) Multidisciplinary & interdisciplinary teams may use different language & methods --> be inclusive and communicate clearly! 💬 3) Allow for (in-)formal exchange between team members. Different approaches spark interest. Talk about this! 🥂 4) Be open towards different approaches from another field of research or practice. Your gold-standard methodology might not be the best fit for a specific aspect of the project 🤗 5) Be patient! All these different approaches combined in one project will take longer than projects from one discipline. BUT they have the power to produce more robust & holistic results 🦾 📚 Read more on our thoughts in our open-access handbook here: https://lnkd.in/eeaFS83V

Microsoft has unveiled a suite of generative AI tools aimed at reducing administrative burdens in healthcare. These innovations include: - Patient Timelines: Utilizing AI to extract and chronologically organize data from electronic health records, providing clinicians with a comprehensive view of patient histories. - Clinical Report Simplification: Employing AI to translate complex medical terminology into patient-friendly language, enhancing understanding and engagement. - Radiology Service: Implementing AI for quality checks in radiology, identifying follow-up recommendations and clinical findings within documentation. These tools are designed to streamline workflows, allowing healthcare professionals to focus more on patient care. Microsoft emphasizes the development of high-impact, low-risk AI applications to address challenges such as clinician burnout and inefficiencies in the healthcare system. #AIinHealthcare #DigitalHealth #HealthcareInnovation #GenerativeAI #HealthTech #EHR https://lnkd.in/dtGrCGzg

🔬 Biostatistics & Epidemiology: A Powerful Partnership in Public Health 🧠📊 Epidemiology and biostatistics are not just allied disciplines—they’re inseparable pillars of modern public health. Their synergy fuels scientific discovery, guides policy, and shapes interventions that save lives. 🎯 Complementary Roles 🔍 Epidemiology: The Question Generator Epidemiologists identify public health problems, generate hypotheses, and design studies to explore disease patterns and risk factors. • Study Design: Choosing between cohort, case-control, or cross-sectional designs. • Hypothesis Generation: Spotting unusual disease clusters and exploring environmental or genetic links. 📈 Biostatistics: The Analytical Engine Biostatisticians bring rigor and structure to analysis, transforming raw data into actionable insights. • Data Analysis: Regression models, survival analysis, hypothesis testing. • Bias Control: Using stratification, matching, and advanced techniques to ensure validity. ⸻ 🔄 Collaboration Across the Research Cycle From idea to impact, these fields work hand-in-hand: • Design: Epidemiologists define the framework; biostatisticians handle power calculations and randomization. • Data Collection: Field implementation meets data integrity. • Analysis & Interpretation: Quantitative models meet contextual insight. ⸻ 🌍 Landmark Public Health Successes • Framingham Heart Study — Identified key cardiovascular risk factors. • Polio Vaccine Trials (1954) — Pioneered RCTs and statistical validation. • COVID-19 Response — Modeling and analytics shaped global strategies. ⸻ 🔬 Modern Integration & Innovation Today’s research integrates both fields in exciting ways: • Causal Inference — From associations to causation using tools like mediation analysis and instrumental variables. • Big Data & Machine Learning — Leveraging EMRs and AI for scalable insights. • Precision Medicine & Spatial Epidemiology — Mapping risk at individual and population levels. ⸻ 🚧 Challenges & 🚀 Opportunities Challenges: • Managing high-dimensional data. • Bridging training gaps between disciplines. Opportunities: • Interdisciplinary education. • Harnessing AI to merge epidemiological context with statistical power. ⸻ 🧠 At the intersection of inquiry and evidence lies the true strength of public health. As biostatistics and epidemiology evolve together, they continue to shape a healthier, data-driven world. 🌐 #PublicHealth #Biostatistics #Epidemiology #Research #DataScience #CausalInference #HealthPolicy #PrecisionMedicine #AIinHealthcare #LinkedInScience

What Healthcare Can Learn from Art At first glance, healthcare and art may seem worlds apart - one driven by data and precision, the other by creativity and expression. But in reality, both share a profound mission: to understand, heal, and connect with the human experience. 1. Seeing Beyond the Surface Artists train their eyes to see what others overlook - subtle details, hidden emotions, deeper meanings. In healthcare, the same principle applies. A great physician or radiologist doesn’t just see an image or a lab result; they perceive the patient behind it. True medical excellence requires both analytical skills and deep human empathy. 2. The Power of Perspective Art teaches us that there is never just one way to see a subject. Picasso’s Cubism challenged traditional perspectives; in medicine, AI and digital health are revolutionizing how we diagnose and treat disease. By embracing new perspectives, healthcare can evolve beyond conventional approaches and make innovation truly transformative. 3. Embracing Uncertainty Every artist faces a blank canvas, full of uncertainty. Medicine, too, is full of unknowns. No patient fits perfectly into textbook cases, and treatments don’t always follow expected paths. Like artists, healthcare professionals must embrace ambiguity, adapt, and remain open to discovery. 4. Creating Meaningful Experiences Great art moves us - it tells a story, evokes emotions, and leaves a lasting impact. Shouldn’t healthcare do the same? Beyond technology and treatments, healthcare should focus on creating meaningful experiences for patients, making them feel heard, valued, and cared for. 5. Collaboration Across Disciplines The most groundbreaking art movements, like the Renaissance, emerged when different disciplines - science, philosophy, and art - collided. The same applies to healthcare. When physicians, engineers, data scientists, and artists collaborate, innovation flourishes, leading to patient-centered solutions that blend technology with humanity. The Art of Healing At its core, healthcare is not just science - it is an art. The best clinicians, like the best artists, have vision, compassion, and the ability to transform lives. As we navigate the future of medicine, let’s take inspiration from art: to see differently, think creatively, and heal with both knowledge and heart. What lessons from art have inspired your work in healthcare? Let’s discuss! #Healthcare #Innovation #ArtAndMedicine

China is leading the way in the use of advanced technologies within the healthcare sphere, and as a result,'smart hospitals’ have been created. These facilities use artificial intelligence (AI), big data, and the Internet of Things (IoT) to improve patient care, increase efficiency, and enhance the quality of healthcare. Recent Updates on Smart Hospitals in China “Trinity” Strategy Diagrams: In China’s bold strategy to go digital in hospitals, “smart medicine," “smart administration," and “smart services” are its primary goals. The use of new technologies in the construction of medical institutions is hoped to elevate the quality and accessibility of health services in China. This offering focuses on China-wide healthcare problems. PMC China Medical University Hospital: China Medical University Hospital has an edge against other hospitals because it has always embraced smart healthcare improvements. CMUH has released a comprehensive ‘AI-assisted physician’ suite that would aid physicians in establishing a diagnosis, devising a treatment, and managing a patient. AI services for bone age diagnosis, abnormal chromosome detection, ECG detection, chest radiography, and screening for diabetic eye disease are integrated into this system. PR NEWSWIRE BOE Hefei Digital Hospital: BOE Hefei Digital Hospital is among the few Smart Hospitals in China set up by BOE in collaboration with American Dignity Health. The Rehabilitation Centre of the Hospital employs cutting-edge digital tools that are accessible for improving the quality of health and and engaging patients’ safety, and experience. WSP Jiahui International Hospital (JIH): November 2017. Marked Shanghai’s first foreign-funded smart hospital, a strong step forward. Patented building services technologies are integrated within the JIH with the intention of constantly improving patient experience and operational efficiency. JOHNSON CONTROLS The implementation of AI robots in hospitals is another facet of growth that China seeks to pursue. The researchers from Tsinghua University are developing an AI hospital that has the capacity to cater to up to 3000 patients in a single day, and it employs robot doctors to treat these patients. These AI doctors have already been able to answer more than 93% of questions pertaining to the U.S. market licensing test which shows their potential in treating patients. BILD Smart hospitals benefits: Improved Patient Engagement: AI and IoT integration allows real-time tracking and develops customized treatments, thus enhancing health outcomes. Streamlined hospital operations: Automation in administration is complemented by improved operational management to shorten unnecessary hospital delays and ensure prompt service. Better Decisions: With all the information about the patient from a singular doctor, they will be able to make better and well-informed decisions, leading to more effective diagnoses and treatment procedures. #smarthospital

🧠 Is AI in medical school creating more problems than solutions? I had the opportunity to talk to a few medical students the other day. I posed some clinical questions and they all used apps on their smartphones to provide answers. I was a bit struck by it - it’s as if they had to first “check” their answer before they replied. With the rapid rise of AI tools in healthcare, medical schools are integrating these technologies to teach the next generation. But could this approach be inadvertently counterproductive? AI can certainly assist in analyzing data, generating insights, and even predicting patient outcomes. But medical school isn’t just about acquiring knowledge—it’s about developing critical thinking, honing diagnostic skills, and building the empathy necessary for effective patient care. When students rely too heavily on AI, they may lose the opportunity to develop their clinical reasoning and problem-solving abilities independently. Moreover, AI isn’t always correct. It can introduce biases or miss nuances that are obvious to a trained, observant human. Over-reliance on technology may lead to a generation of clinicians who lack confidence in their own judgment, especially when AI tools fail or aren’t available. The balance is essential: AI can enhance education, but not replace the hands-on learning and personal growth that make a great doctor. As medical educators, we need to teach students to critically assess AI recommendations, understand its limitations, and prioritize human-centered care. What are your thoughts? Should AI be a tool or a central component in medical education? #MedicalEducation #ArtificialIntelligence #FutureDoctors #HealthcareAI