How to Use AI to Improve Healthcare Access

Patients are changing fast. Healthcare must change faster. The old model is dead. Today’s patients are not waiting in line. They are searching, clicking, and asking AI for answers—before they ever see a doctor. One in four UK patients already use generative AI for health advice. Nearly a third would rather ask AI or social media than wait for a clinician. This is not a threat. It is a signal. Digital curiosity is the new front door to care. The best healthcare leaders see this as a chance to build something better. Not more apps. Not more portals. But a true bridge—where technology and empathy work together. Here’s the new playbook for Connected Care: 1/ Welcome the digital first step • Treat every online search, chatbot, or AI query as the start of the care journey. • Build systems that catch these signals and guide patients into real care, not dead ends. 2/ Make AI a bridge, not a barrier • Use AI to handle admin, triage, and routine questions. • Free up clinicians to spend less time on screens, more time in eye contact. • Let AI reduce friction, but never erode trust. 3/ Design for transparency and control • Give patients clear, simple access to their records, appointments, and care plans. • Let them see the whole journey, not just the next step. • Make them feel like part of the team, not just a case number. 4/ Connect the dots, break the silos • Stop building one-off tools that don’t talk to each other. • Create platforms where every digital touchpoint feeds into a single, human-centered care experience. 5/ Build trust at every step • Use technology to inform, not overwhelm. • Keep the human touch at the center, even as AI does more heavy lifting. This is not theory. This is the new roadmap for healthcare. When you treat AI-curiosity as the entry point—and connect it to a seamless, human care journey—you unlock the future. Your patients are already digital. Your care model must be, too. The future belongs to those who connect, not those who compete. Build the bridge. Welcome the search. Lead the change. Here's the link to the report: https://lnkd.in/eUfJ7aab Semble Christoph Lippuner Mikael Landau

As a surgeon, I've seen the potential of AI to diagnose diseases and streamline the entire patient journey, from the moment patients walk through the door to the day they're discharged. Imagine a patient arriving at the hospital with a suspected heart condition. Traditionally, this could involve multiple appointments, tests, and specialist consultations, causing delays and potential anxiety for the patient. With AI, this process can be expedited and personalized. Algorithms can quickly analyze medical records, lab results, and imaging scans to identify potential issues, flagging them for immediate attention. AI-powered chatbots can guide patients through the process, answering questions, scheduling appointments, and providing educational resources. For example, AI can help identify patients at high risk of readmission, allowing for proactive interventions and follow-up care that reduces hospital stays and improves outcomes. But AI's potential goes beyond efficiency. It can also enhance the patient experience by: ◾️Personalizing care plans: Tailoring treatment based on individual patient data. ◾️Providing 24/7 support: Offering virtual consultations and access to information anytime. ◾️Empowering patients: Giving them the tools and information they need to actively participate in their own care. I'm excited about AI's possibilities for improving healthcare delivery. By seamlessly integrating AI into the patient journey, we can create a more efficient, effective, and, ultimately, human-centered healthcare system. #AI #healthcare #innovation #patientjourney #efficiency #heart

Healthcare does not have a knowledge problem. It has a coordination problem. A simple example. A patient with type 2 diabetes sees their GP. HbA1c is poorly controlled. Guidelines recommend annual foot screening. The GP knows this. The patient has been told before. A referral is generated. It goes into a queue. The appointment is weeks away. The patient misses it. No structured follow-up happens. Six months later, the patient presents with an infected ulcer. No one made a clinical error. The system just had too many steps between risk and response. This is the failure mode AI should be solving. That is not AI replacing a clinician. That is AI removing latency between known risk and action. This should be the design principle for AI in healthcare. Not: Can AI make the diagnosis? But: Where is the unnecessary distance between information and action, and can AI close it? From working inside a large public healthcare system, I consistently see four high-friction zones: 1. Administrative compression Ambient documentation, coding support, automated prior authorisation. When clinicians reclaim even 20 percent of their time, effective capacity expands without additional headcount. 2. Smarter triage and referral routing AI that packages cases clearly and routes complexity deliberately. Specialist time is scarce capital. 3. Longitudinal clinical memory Preventing duplicate labs and imaging across fragmented systems before orders are placed. 4. Between-visit risk detection Proactive monitoring that flags deterioration before it becomes an admission. The mechanism is simple. Reduce transaction friction and cost falls. Reduce information loss and quality rises. Expand effective capacity and access improves. The wrong move is automating judgement. The right move is automating latency. For those who do not know me, I am Dr Ang Yee Gary. I work at the intersection of health services research, clinical AI advisory, and frontline primary care. My focus is system redesign, outcomes, and cost-effectiveness. I still see patients, which keeps strategy grounded in operational reality. I am actively looking to connect with startups building in this space, particularly those focused on: • Point-of-care diagnostic integration • Referral optimisation and triage engines • Administrative automation with measurable ROI • Chronic disease risk infrastructure • Interoperability layers that reduce duplication If you are building AI that removes steps rather than adding dashboards, I would be keen to speak. Healthcare does not need more tools. It needs fewer unnecessary steps between the patient and the professional. #HealthcareInnovation #ClinicalAI #HealthSystems #DigitalHealth #ValueBasedCare #HealthcareInvestment

Health AI, not Healthcare AI > Why the current focus of AI in healthcare is missing the larger opportunity! In healthcare AI, the spotlight currently is on the brief 4-12 hours the average person spends in a care setting each year. However, the true essence of health encompasses the remaining 364 days annually where there are no healthcare interactions. Beyond the confines of care settings are significant challenges: - 60% of patients fail to complete their recommended care gaps - 70% procrastinate on crucial healthcare - 50% of adults skip their yearly physical exams - 75% of patients are ill-prepared for doctor visits - 40% do not fully adhere to routine lab tests - 30% neglect to pick up prescribed medications - 70% do not follow through on care plans or discharge instructions No amount of AI optimization of the doctor workflow is likely to yield significant outcomes without addressing these issues. While optimizing the limited time during care visits through AI is beneficial, the true value lies in leveraging AI to optimize health in the vast expanse of days where the person is not interacting with healthcare. AI can play a significant role in transforming health by: - Understanding and interpreting healthcare records for patients - Identifying care opportunities and engaging patients proactively - Matching patients with high-quality, cost-effective care options - Streamlining appointment scheduling and automating form completion - Generating patient summaries and suggesting relevant questions for upcoming visits - Anticipating necessary lab tests for future appointments - Ensuring comprehension of care plans and discharge instructions - Supporting treatment plan adherence and behavior modification - Identifying cost-saving measures like prescription transfers and optimal provider selection - Facilitating behavior changes to align with care plans - Assisting individuals in achieving their personal health objectives What do you think? Should the focus of AI be on the fleeting 4-12 hours in care settings or the substantial 364 days outside? #healthcare #artificialintelligence #thinkdifferent

AI and automation offer us an incredible opportunity: the chance to free up time, energy, and attention for the human connections that matter most in healthcare. When we're intentional about implementation, we can create systems that are both more efficient and more deeply human - where technology handles the transactional so people can focus on the relational. Here are ten principles for using AI and automation to strengthen human connection: 1. Start with Human Needs, Not Technical Capabilities Before asking what you can automate, ask what people actually need. Observe where friction exists. Listen to where patients and staff struggle. Let those insights guide your technology decisions. 2. Automate the Transactional to Protect the Relational Routine scheduling, wayfinding, and basic information transfer are ideal for automation. This frees up your team for moments that truly need human attention - difficult conversations, emotional support, and relationship building. 3. Test with Real People in Real Conditions What works in an outpatient setting might not work in an inpatient procedural space. Prototype different approaches and observe how people respond in the specific contexts where they'll use these tools. 4. Design for Everyone, Especially the Most Vulnerable When your automation works for people with varying comfort with technology, different language needs, and different digital access levels, you've created something that expands access rather than creating new barriers. 5. Make Human Interaction Always Available Give people easy, judgment-free ways to connect with a human whenever they need to. When automation is truly helpful, most people will use it. When they need a person, that option should be readily available. 6. Measure Whether You're Creating Capacity for Connection The best automation frees staff from routine tasks so they can spend more time on complex care conversations, emotional support, and personalized attention. If your team isn't gaining that capacity, refine your approach. 7. Be Clear About What's Automated and What's Human People appreciate knowing when they're interacting with AI versus a person. Transparency builds trust and sets appropriate expectations. 8. Design Seamless Handoffs Between Technology and Humans When someone moves from an automated system to human interaction, the transition should feel smooth. Information should carry forward, staff should have context, and patients shouldn't repeat themselves. 9. Learn and Adapt Continuously Pay attention to what's actually happening as people use your systems. Where does automation help? Where does it frustrate? Use these insights to keep improving. 10. Let Your Values Guide What Stays Human Your organizational values should illuminate where human presence is essential. If you value dignity and compassion, those values can guide which moments need human interaction and which can be effectively supported by technology.

Artificial Intelligence (AI) has the potential to revolutionize healthcare, particularly for underserved populations. By leveraging AI, we can address critical gaps in healthcare delivery, improve outcomes, and ensure equitable access to quality care. AI enhances diagnosis and treatment by analyzing medical data to provide accurate and timely diagnoses, even in remote areas, assisting in early detection of diseases for better treatment outcomes. Predictive analytics enables preventive care by identifying health risks from patient data, allowing for early interventions to manage chronic diseases. AI-powered telemedicine platforms and remote monitoring tools facilitate virtual consultations and continuous care, making healthcare accessible to those in rural or underserved regions. Personalized medicine tailors treatment plans based on individual data, improving health outcomes for populations that might otherwise receive generalized care. AI also optimizes healthcare resource allocation, ensuring efficient use of limited supplies and personnel in underserved areas. Additionally, AI-driven chatbots and virtual assistants provide health education, breaking down complex medical information into understandable language, empowering patients to make informed health decisions. By integrating AI into healthcare, we can bridge the gap in care delivery for underserved populations, providing them with the tools and resources needed for better health outcomes. #AI #Healthcare #UnderservedCommunities #HealthEquity #InnovationInHealthcare Sources: - [World Health Organization](https://lnkd.in/eMuVREjh) - [NIH - National Library of Medicine](https://lnkd.in/eJA2achj) - [McKinsey & Company](https://lnkd.in/evvGSRh7)

AI in Healthcare Sepsis infection is one of the largest causes of deaths in hospitals, estimated 11 m deaths/year. AI can help. After a patient checks into the emergency ward of a hospital, AI can look into 150 patient variables like lab results, vital signs, current medications, medical history, demographics to predict risk profile for possible sepsis. Staying vigilant has brought down sepsis incidence in hospitals ! I just gave you one example of how AI can help in healthcare. Few more … DIAGNOSIS – GE is using gen AI for multi modal integration from sources like imaging, genomics, pathology to help a clinician in diagnosis. Another ex is ischemic stroke where the image has to be read by a radiologist quickly to identify the clot in the brain. This can be done by AI when radiologists are busy or limited in number. This speed in diagnosis can save lives. REMOTE PATIENT CARE – We are know that there is a demand & supply mismatch in doctors and nurses. Monitoring devices with AI can send a notification to the healthcare professionals to visit the patient as and when needed saving time. Such efficient remote care limits the number of days patient has to spend in the hospital thereby reducing cost of stay which is very helpful for patients and insurance companies. AI-trained Chatbots have shown the potential to answer patient questions when doctors are not available. DRUG DISCOVERY – With millions of people waiting for the approval of new medicines, bringing a drug to market still takes on average more than 10 years and costs over 1.9 billion Euros on average. Merck has launched a drug discovery software that identifies compounds from over 60 billion possibilities based on key properties like non toxicity, solubility and stability in the body. Insilico Medicine, a biotech company out of Hong Kong is the first company where an AI discovered drug has entered phase II clinical trials in US and China. CLINICAL TRIALS - AI can help in trials through patient recruitment (through analysing patient health records and identifying most suitable candidates thereby reducing recruitment time), patient monitoring (by identifying adverse events or complications real time), protocol design, trial site selection, predict enrolment rates, data analysis (AI can often spot patterns and correlations that might be missed by humans) and cost efficiency by automating a lot of the admin paperwork involved in trials. MANUFACTURING– AI can predict machine failure and schedule equipment maintenance before breakdown occurs. It can inspect products and detect defects more accurately than humans, it also ensures timely delivery of raw materials through analysis and prediction of typical delays due to logistics, weather, shortages etc. Way ahead - I have only skimmed the surface & covered a few areas above. There is no doubt that AI can transform healthcare in many way however the challenges of data privacy and related ethics, prohibitive costs and unclear regulations remain.

#AI adoption in #healthcare doesn’t fail because of technology. It fails because we skip the habits. I’ve watched brilliant clinicians burn out while “cutting-edge” tools gathered dust. I’ve also seen simple, intentional AI deployments quietly save time, restore focus, and improve care. The difference isn’t budget. It’s behavior. After years in the ER and years working in healthcare AI, these are the 7 habits I see in teams that actually make AI work 👇 1️⃣ Be proactive Don’t wait for perfect tools. Learn the basics. Pilot safely. Own the responsibility. 2️⃣ Begin with the end in mind Outcomes first. Mortality. Access. Equity. If the AI doesn’t move those, it’s noise. 3️⃣ Put first things first Automate admin before touching diagnostics. Fix volume problems before chasing complexity. 4️⃣ Think win-win AI + physician beats either alone. Precision improves when trust exists. 5️⃣ Seek first to understand Explainability isn’t optional. If you can’t explain the “why,” you shouldn’t deploy the “what.” 6️⃣ Synergize Medicine is multimodal. Text, images, signals, humans AI works best when combined, not isolated. 7️⃣ Sharpen the saw Models drift. Humans fatigue. Continuous learning protects both patients and clinicians. This framework isn’t about replacing doctors. It’s about protecting humanity while scaling intelligence. Where do you see healthcare AI succeeding or breaking right now? #HealthcareAI #DigitalHealth #HumanCenteredAI #DrGPT

Two years ago, generative AI burst onto the scene, and I've been fascinated by its potential in healthcare ever since. Could this technology finally solve some of medicine's biggest problems? Or will it simply become another overhyped innovation, full of promise but lacking real-world impact? I had the opportunity to discuss these questions with Munjal Shah, co-founder and CEO of Hippocratic AI on Fixing Healthcare with Dr. Robert Pearl and Jeremy Corr. Important takeaways: 🔹 AI can finally make value-based care a reality. Shah's company is using AI-powered virtual agents to reach more patients, lower costs and prevent unnecessary hospitalizations—something traditional models have struggled to scale. 🔹 The economics of AI are changing everything. At just $9 per hour (and likely to drop further), this technology is making proactive, preventative care feasible at scale. 🔹 This isn't about replacing clinicians, says Shah. The key is empowering patients and healthcare professionals. Unlike other AI companies focused on diagnostics or clinical decision support, Hippocratic AI is tackling a different problem: expanding access to care, ensuring patients take their medications, and filling critical gaps in chronic disease management. The potential here is enormous. Healthcare has always operated under a scarcity mindset: too few doctors, too few nurses, too little time. But what happens when AI makes it possible to check in on every at-risk patient every day? I recognize AI is a charged topic in medicine today. I encourage you to listen to this episode and learn firsthand about the role AI (and companies like Hippocratic AI) could play for patients, clinicians and the entire healthcare system. 🎧 Listen: https://lnkd.in/dEhVU37v What do you think: Are we finally at the tipping point where AI can meaningfully improve patient care? Let's discuss. ⬇️