How to Build Patient Education Programs with AI: A Complete Guide for Healthcare Professionals

Table Of Contents

• Why Patient Education Matters More Than Ever
• Traditional Challenges in Patient Education
• How AI Transforms Patient Education Programs
• Building Your AI-Powered Patient Education Program
• Key Components of Effective AI Patient Education
• Implementation Strategies That Work
• Measuring Success and Improving Outcomes
• Getting Started with No-Code AI Tools

Patient education has always been a cornerstone of quality healthcare, yet most providers struggle with the same persistent challenges: limited time during appointments, diverse patient populations with varying health literacy levels, and the need for personalized information that each patient can actually understand and apply. These challenges have only intensified as healthcare becomes more complex and patient expectations for accessible, on-demand information continue to rise.

Artificial intelligence is fundamentally changing how healthcare organizations approach patient education. What once required extensive development teams, significant budgets, and months of programming can now be accomplished by individual healthcare professionals using intuitive, no-code platforms. The result is patient education that adapts to individual needs, remains available 24/7, and actually improves health outcomes in measurable ways.

This comprehensive guide will walk you through everything you need to know about building AI-powered patient education programs, from understanding the core benefits to implementing your first interactive patient advisor. Whether you’re a nurse educator, physician, health administrator, or patient advocate, you’ll discover practical strategies you can start using today—no technical background required.

Why Patient Education Matters More Than Ever

The statistics tell a compelling story. Studies consistently show that patients who receive comprehensive education about their conditions experience better health outcomes, fewer emergency visits, and higher treatment adherence rates. Yet the traditional model of patient education—pamphlets handed out during brief appointments or generic videos in waiting rooms—fails to meet the needs of modern healthcare consumers.

Today’s patients expect personalized, accessible information delivered when and where they need it. They want to understand not just what their diagnosis means, but how it specifically applies to their unique situation. A 65-year-old managing diabetes has different needs than a 30-year-old with the same condition. A patient preparing for surgery needs different information at different stages of their care journey. One-size-fits-all education materials simply don’t address this reality.

The healthcare landscape has shifted fundamentally. Patients are more engaged in their care decisions than ever before, often arriving at appointments with research from online sources of varying quality. Rather than fighting this trend, forward-thinking healthcare organizations are leveraging AI to provide reliable, personalized education that empowers patients while supporting clinical teams.

Beyond improved outcomes, effective patient education delivers tangible operational benefits. When patients understand their conditions and treatment plans, they call clinical staff less frequently with basic questions. They’re better prepared for procedures, reducing delays and cancellations. They manage chronic conditions more effectively, potentially reducing costly complications. The return on investment in quality patient education extends far beyond patient satisfaction scores.

Traditional Challenges in Patient Education

Before exploring AI solutions, it’s important to understand why traditional patient education approaches fall short. Healthcare providers face several persistent obstacles that limit the effectiveness of conventional education programs.

Time constraints represent perhaps the most significant barrier. Physicians spend an average of just 13-16 minutes with each patient. Within this limited window, they must conduct examinations, review test results, discuss treatment options, and address immediate concerns. Comprehensive patient education often becomes an afterthought, relegated to printed handouts that patients may or may not read or understand.

Health literacy varies dramatically across patient populations. Nearly nine out of ten adults struggle to understand and use health information effectively, according to the U.S. Department of Health and Human Services. Standard educational materials written at a high reading level or filled with medical jargon alienate the very people they’re meant to help. Yet creating multiple versions of every educational resource for different literacy levels requires resources most organizations don’t have.

Language and cultural barriers add another layer of complexity. Patients who speak English as a second language—or not at all—face obvious challenges accessing written materials. But even when translations exist, cultural contexts around health, illness, and treatment may require different educational approaches entirely. A one-size-fits-all educational video or pamphlet cannot address these nuanced differences.

Information retention poses yet another challenge. Patients typically remember only 20-40% of what healthcare providers tell them during appointments, and that percentage drops even further for patients experiencing stress or anxiety about their health. Without reinforcement and the ability to revisit information at their own pace, much of the education provided during clinical encounters is simply lost.

How AI Transforms Patient Education Programs

Artificial intelligence addresses each of these traditional challenges in powerful ways, fundamentally transforming how patient education can be delivered and experienced. The technology enables personalization at scale, something previously impossible with conventional approaches.

AI-powered patient education tools can adapt content based on individual patient characteristics, learning preferences, and comprehension levels. Instead of receiving the same generic information about diabetes management, a patient interacts with an AI advisor that adjusts explanations based on their specific type of diabetes, current medications, lifestyle factors, and understanding of key concepts. The system can detect when a patient seems confused by a concept and automatically provide simpler explanations or additional context.

The always-available nature of AI education tools solves the time constraint problem from a different angle. Patients can access personalized education whenever questions arise—at 2 AM when they’re worried about a symptom, while preparing meals and wondering about dietary restrictions, or in the quiet moments before a procedure when anxiety peaks. This on-demand access extends the educational reach of clinical teams without requiring additional staff hours.

Natural language processing allows AI systems to understand and respond to patient questions in conversational terms, not medical jargon. A patient can ask “Why does my chest feel tight when I climb stairs?” rather than needing to know technical terminology. The AI interprets the question, provides relevant education about their specific condition, and can even flag concerning symptoms for clinical review when appropriate.

Perhaps most importantly, AI enables consistent, evidence-based education that reflects current clinical guidelines. When protocols change or new research emerges, updates can be made once and immediately reflected across all patient interactions. This ensures every patient receives accurate, up-to-date information aligned with best practices, regardless of which staff member they encounter or when they seek information.

Real-World Applications Across Healthcare Settings

The versatility of AI patient education becomes clear when examining specific use cases across different healthcare contexts. Chronic disease management programs use AI chatbots to provide daily tips, medication reminders, and answers to common questions about managing conditions like diabetes, heart disease, or asthma. Patients develop better self-management skills while clinical teams focus on complex cases requiring human expertise.

In surgical settings, AI-powered pre-operative education tools guide patients through what to expect before, during, and after procedures. Interactive quizzes verify understanding of important preparation steps, reducing same-day cancellations due to patients not following pre-surgical instructions. Post-operative education adapts based on the specific procedure and individual recovery progress, helping patients recognize normal healing versus concerning symptoms.

Mental health applications represent another powerful use case. AI-powered educational tools can provide psychoeducation about conditions like depression and anxiety, teach coping strategies, and help patients understand their treatment options. The 24/7 availability proves particularly valuable in mental health contexts, where questions and concerns often arise outside traditional office hours. While these tools complement rather than replace human therapy, they extend therapeutic benefits beyond scheduled sessions.

Maternal and pediatric care settings use AI education platforms to support new parents with questions about infant care, childhood development milestones, and when to seek medical attention. The ability to ask questions privately and receive immediate, reliable answers reduces unnecessary emergency visits while ensuring parents don’t ignore truly concerning symptoms.

Building Your AI-Powered Patient Education Program

Creating an effective AI patient education program requires thoughtful planning and a systematic approach, but it doesn’t require coding skills or massive budgets. The process becomes manageable when broken into clear steps that build upon each other.

Step 1: Define Your Educational Objectives and Audience

Start by identifying the specific patient education needs you want to address. Rather than trying to build a comprehensive program covering everything at once, focus on high-impact areas where improved education can make a measurable difference. Consider which patient populations have the most questions, where misunderstandings commonly occur, or which conditions lead to the most preventable complications.

Document the questions patients ask most frequently. Review call logs, patient portal messages, and staff observations to identify patterns. What information do patients consistently need? When do they most often reach out for clarification? Understanding these patterns helps you design an AI education program that addresses real needs rather than assumed ones.

Characterize your target audience in detail. Consider factors like average age, health literacy levels, technological comfort, primary languages spoken, and cultural considerations. This information shapes how you’ll structure and present educational content through your AI platform. An AI education tool for elderly patients managing multiple chronic conditions will look quite different from one designed for young adults learning about preventive care.

Step 2: Gather and Structure Your Educational Content

Effective AI patient education starts with strong foundational content. Collect your existing patient education materials, clinical protocols, and frequently asked questions. Review this content for accuracy, currency, and alignment with evidence-based practices. Identify gaps where additional information needs to be developed.

Organize content in modular, topic-based structures rather than lengthy documents. AI platforms work best with information broken into discrete concepts that can be combined and customized based on patient needs. For example, rather than a single 20-page diabetes guide, create separate modules covering blood sugar monitoring, medication management, dietary considerations, exercise recommendations, and complication prevention.

Translate medical jargon into plain language without sacrificing accuracy. Work with patient advocates or health literacy specialists to ensure explanations truly make sense to people without medical backgrounds. Test your language with actual patients and revise based on their feedback. Remember that what seems simple to healthcare professionals may still be confusing to patients encountering concepts for the first time.

Document the relationships between different topics and concepts. How do basic concepts build toward more advanced understanding? What prerequisite knowledge does a patient need before a particular topic makes sense? These connections help your AI system guide patients through education in a logical, progressive way that builds understanding rather than overwhelming them with disconnected facts.

Step 3: Choose the Right AI Platform for Your Needs

The platform you select should align with your organization’s technical capabilities, budget, and specific use cases. No-code AI platforms like Estha have democratized access to sophisticated AI capabilities, enabling healthcare professionals to build custom patient education applications without programming knowledge.

When evaluating platforms, consider the types of AI applications they support. Do you primarily need a chatbot that answers questions, an interactive assessment tool that personalizes recommendations, or perhaps a virtual health coach that guides patients through care protocols? Some platforms excel at specific application types while others offer flexibility across multiple formats.

HIPAA compliance and data security must be non-negotiable requirements for any platform handling patient information. Ensure the platform provider can demonstrate appropriate security measures, provide Business Associate Agreements, and offer transparency about how data is stored and protected. Even if you’re not collecting protected health information initially, choose a platform that could support that capability as your program evolves.

Evaluate the platform’s ease of use and learning curve. The best technology is useless if your team can’t effectively use it. Look for intuitive interfaces, quality documentation, and strong support resources. Platforms with active user communities often provide additional value through shared templates, best practices, and troubleshooting assistance.

Step 4: Design Your AI Application with Patient Experience in Mind

User experience design directly impacts whether patients will actually use your AI education tool. Start with a clear, welcoming interface that immediately communicates what the tool does and how it can help. Patients should understand within seconds how to get started and what to expect from the interaction.

Create conversational flows that feel natural and supportive, not robotic or clinical. Use a warm, empathetic tone that acknowledges the stress and uncertainty many patients experience regarding their health. Build in empathy statements that validate patient concerns before providing educational information. For example, “It’s completely normal to feel worried about an upcoming procedure” before launching into pre-operative education.

Design for accessibility from the start. Ensure your AI application works well on mobile devices, as many patients will access it from smartphones. Consider visual accessibility with appropriate text sizes, color contrasts, and options for audio output for patients with vision impairments. If serving multilingual populations, plan for translation from the beginning rather than as an afterthought.

Build in checkpoints that verify understanding without feeling like a test. Interactive elements like “Does this make sense so far?” or “Would you like a simpler explanation?” give patients control over their learning pace while helping your AI system adapt to their comprehension level. These checkpoints also provide valuable data about which concepts patients find most challenging.

Step 5: Test, Refine, and Launch Strategically

Before rolling out your AI patient education program broadly, conduct thorough testing with representative patient groups. Observe how real patients interact with your application. Where do they get confused? What questions does the AI struggle to answer adequately? What works better than expected? This user testing reveals issues that internal testing by healthcare staff might miss.

Start with a pilot program limited to a specific patient population or clinical area. This controlled launch allows you to work out problems, gather feedback, and demonstrate value before expanding. Document success metrics from your pilot to build the case for broader implementation and ongoing resource allocation.

Create clear protocols for clinical escalation. Your AI application should recognize when a question falls outside its scope or when a patient describes symptoms requiring immediate clinical attention. Build in clear pathways for patients to reach human providers when needed, and ensure your clinical team knows how to monitor and respond to these escalations.

Develop a communication plan that helps patients discover and start using your new AI education resource. Simply building the tool isn’t enough; patients need to know it exists and understand how it can help them. Consider multiple touchpoints including appointment reminders, patient portal announcements, waiting room signage, and direct recommendations from clinical staff during encounters.

Key Components of Effective AI Patient Education

While every AI patient education program will be unique to its context, several core components consistently appear in successful implementations. Understanding these elements helps you design more effective applications regardless of your specific use case.

Personalization and Adaptive Learning

The most powerful AI education tools adapt to individual patients based on multiple factors. Patient profiles capture relevant characteristics like age, diagnosis, medications, and known comorbidities that shape educational needs. An AI system can reference this information to provide contextually relevant education without requiring patients to repeat their history.

Learning preference adaptation recognizes that different patients learn in different ways. Some people prefer detailed explanations; others want concise summaries. Some benefit from visual diagrams; others prefer narrative descriptions. AI systems can detect and adapt to these preferences based on how patients interact with different content formats.

Progressive disclosure prevents information overload by introducing concepts in manageable chunks. Instead of presenting all information about a condition at once, the AI introduces foundational concepts first, then builds to more complex topics as the patient demonstrates understanding. This scaffolded approach mirrors effective teaching strategies used in traditional education.

Conversational Intelligence and Natural Language Understanding

Modern AI platforms excel at understanding natural language, allowing patients to ask questions as they would to a human educator. Intent recognition helps the system understand what patients really want to know, even when questions are vaguely worded or technically imprecise. A patient asking “Will this hurt?” before a procedure receives different information than one asking “How long until I can drive?” despite both being valid concerns about the same procedure.

Context awareness enables AI systems to maintain coherent conversations across multiple exchanges. The system remembers what was discussed earlier in the conversation and references that context in subsequent responses. This creates more natural interactions and prevents the frustration of repeating information or receiving contradictory answers.

Sentiment analysis allows sophisticated AI applications to detect when patients seem anxious, confused, or distressed based on their word choices and interaction patterns. The system can adjust its tone, offer reassurance, or suggest connecting with a human provider when emotional support might be needed beyond pure information delivery.

Multi-Modal Content Delivery

Effective patient education leverages multiple content formats to accommodate different learning styles and accessibility needs. Text-based explanations form the foundation, but truly comprehensive AI education programs incorporate visual elements like diagrams, illustrations, and infographics that help patients understand anatomical concepts or procedural steps.

Video content brings procedures and techniques to life in ways static images cannot. An AI system might link to short video demonstrations of proper wound care techniques, inhaler use, or exercise modifications for physical therapy patients. The AI can suggest specific videos based on patient questions and verify understanding afterward.

Audio options support patients with vision impairments or those who prefer listening to reading. Some AI platforms can generate natural-sounding audio of written content, while others support voice-based interactions where patients ask questions verbally and receive spoken responses. This flexibility ensures education accessibility across different patient capabilities.

Interactive elements like quizzes, decision aids, and scenario-based learning increase engagement while helping patients apply knowledge to their specific situations. Rather than passively receiving information, patients actively work through scenarios that build confidence in managing their health conditions.

Implementation Strategies That Work

Even the most thoughtfully designed AI patient education program will fail without effective implementation strategies. Success depends not just on the technology itself, but on how you integrate it into existing clinical workflows and patient experiences.

Integration with Existing Systems and Workflows

Seamless integration with electronic health records and patient portals dramatically increases utilization. When patients can access AI education tools directly from the same portal where they view test results and message providers, adoption rises significantly. Single sign-on capabilities eliminate friction and make the education resource feel like a natural extension of existing patient tools.

Clinical workflow integration ensures your AI education program supports rather than burdens healthcare providers. Consider how the technology can enhance current processes. Perhaps the AI education tool automatically sends pre-appointment education based on the visit reason, reducing time spent on basic explanations during encounters. Or maybe it provides post-visit summaries that reinforce key points discussed with the provider.

Documentation capabilities help clinical teams track patient engagement with educational content. When providers can see that a patient completed pre-operative education modules and passed comprehension checks, they can focus appointment time on specific questions rather than covering basic information. This visibility also helps identify patients who may need additional support or alternative educational approaches.

Staff Training and Change Management

Healthcare staff must understand and embrace your AI education program for it to succeed. Comprehensive training should cover not just how to use the technology, but why it benefits both patients and staff. Share evidence about improved outcomes and reduced workload from similar programs. Address concerns about AI replacing human jobs by emphasizing how the technology handles routine education, freeing staff for more complex patient needs.

Create champions within different departments who become go-to resources for questions and troubleshooting. These champions can provide peer-to-peer support that often feels more accessible than formal IT help. They also gather frontline feedback about what’s working and what needs adjustment as the program rolls out.

Develop clear protocols for when and how staff should recommend the AI education resource to patients. Some organizations include it in discharge instructions for specific conditions, while others train staff to suggest it when patients ask common questions. Consistency in recommendations increases patient awareness and utilization.

Patient Onboarding and Engagement

First impressions matter tremendously in determining whether patients will adopt and continue using your AI education tool. Thoughtful onboarding introduces the resource in welcoming, non-technical terms. Avoid describing it as “artificial intelligence” or “chatbot,” which may sound impersonal or confusing. Instead, present it as “your 24/7 health education guide” or “personalized health information assistant.”

Provide multiple entry points that meet patients where they are. Some patients will discover the tool through patient portal prompts, others through recommendations from providers, and still others through targeted outreach for specific conditions. Each pathway should include brief orientation to core features and benefits.

Consider gamification elements that encourage ongoing engagement. Progress tracking, achievement badges for completing education modules, or health challenges can motivate continued use, particularly for chronic disease management programs. However, ensure these elements enhance rather than overshadow the core educational purpose.

Regular communication keeps the education resource top-of-mind. Periodic emails or text messages highlighting new content, sharing success stories from other patients, or providing seasonal health tips remind patients the resource exists and can help them beyond their initial use case.

Measuring Success and Improving Outcomes

Demonstrating the value of your AI patient education program requires establishing clear metrics and consistently monitoring performance. The specific measures you track should align with your original objectives while providing actionable insights for continuous improvement.

Usage and Engagement Metrics

Quantitative usage data provides foundational insights into program adoption. Track the number of unique patients using the AI education tool, frequency of use, session duration, and which topics generate the most interest. Declining usage over time might indicate patients aren’t finding ongoing value, while steady or increasing use suggests the tool has become a trusted resource.

Engagement depth metrics reveal how thoroughly patients interact with educational content. Do they simply ask one question and leave, or do they engage in extended educational sessions? Are they completing suggested modules or abandoning them partway through? High abandonment rates for specific content indicate that material may be too complex, poorly structured, or not meeting patient needs.

Question analysis identifies common patient concerns and knowledge gaps. What topics generate the most questions? Are there patterns in the questions asked by patients with specific conditions? This intelligence helps you refine and expand educational content to address the areas where patients need most support. It may also reveal educational gaps that should be addressed during clinical encounters.

Clinical Outcome Measures

The ultimate test of patient education effectiveness is its impact on health outcomes. Treatment adherence rates often improve when patients better understand their conditions and why specific treatments matter. Track medication compliance, appointment attendance, and follow-through with recommended lifestyle changes among patients who use your AI education program compared to those who don’t.

Healthcare utilization patterns can shift positively with better patient education. Monitor changes in emergency department visits for non-emergent issues, urgent care usage, and after-hours calls to clinical staff. Well-educated patients better understand when to seek immediate care versus when to monitor symptoms, leading to more appropriate healthcare utilization.

Procedure-related metrics demonstrate education impact in surgical and procedural contexts. Track rates of same-day cancellations due to inadequate patient preparation, complications related to patient non-compliance with post-procedure instructions, and patient-reported confidence levels going into procedures. Improvements in these areas often correlate with better pre-procedure education.

Patient-reported outcomes including satisfaction scores, quality of life measures, and self-efficacy ratings provide direct feedback about patient experience. While these measures can be influenced by many factors, trends over time offer valuable insights into whether your education program is meeting patient needs.

Continuous Improvement Processes

Regular content reviews ensure educational material remains accurate and aligned with evolving clinical guidelines. Establish a schedule for reviewing and updating content, particularly for rapidly changing clinical areas. Assign subject matter experts to specific content domains and task them with quarterly or semi-annual reviews.

Patient feedback mechanisms should be built directly into your AI application. After educational interactions, prompt patients to rate helpfulness and provide comments. This real-time feedback identifies confusing content, technical issues, or missing topics that need to be addressed. Act on feedback promptly to demonstrate responsiveness and build trust.

A/B testing of different content approaches, conversation flows, or educational sequences helps optimize effectiveness. Try different ways of explaining complex concepts and measure which approaches generate better comprehension. Test whether shorter, more frequent educational modules work better than comprehensive single-session education. Data-driven refinement leads to continuously improving patient experiences.

Getting Started with No-Code AI Tools

The prospect of building an AI patient education program might still feel daunting, but modern no-code platforms have removed the technical barriers that once made this technology accessible only to large organizations with substantial development resources. You can create your first AI education application in hours, not months.

No-code platforms like Estha provide intuitive visual interfaces where you build AI applications by dragging and dropping components, connecting them logically, and configuring them with your content. This approach requires no programming knowledge. If you can create a presentation or organize information in a spreadsheet, you have the skills needed to build functional AI patient education tools.

The development process follows a straightforward pattern. You start by defining the structure of your AI application—is it a question-answering chatbot, an interactive assessment tool, or perhaps a guided educational journey? Then you add your content, organizing it into topics and creating response patterns for common questions. The platform’s AI engine handles the complex technical aspects of understanding patient questions and matching them with appropriate educational content.

Testing and iteration happen quickly with no-code tools. You can immediately preview how your application will behave, test it with sample questions, and refine responses based on results. Unlike traditional software development where changes require coding and redeployment, no-code platforms let you update content and functionality in real-time. This agility is particularly valuable in healthcare contexts where information needs to be updated rapidly.

Practical First Steps for Healthcare Professionals

Begin with a narrowly focused use case rather than trying to build a comprehensive education program all at once. Choose a single condition, procedure, or patient population where you already have strong educational content and clear patient needs. This focused approach allows you to learn the platform, develop your skills, and demonstrate value before scaling up.

Gather a small, multidisciplinary team including clinical subject matter experts, patient advocates, and someone comfortable with technology (even if not a programmer). The clinical experts ensure accuracy and relevance, patient advocates keep the focus on accessibility and clarity, and the technology-oriented team member can navigate the platform and troubleshoot issues.

Allocate realistic time for your initial project. While no-code platforms enable rapid development, creating truly effective patient education still requires thoughtful content development, testing, and refinement. Plan for 4-6 weeks to move from concept to pilot launch for a focused application. Subsequent projects will go faster as you develop familiarity with the platform and reusable content components.

Start building by exploring templates and examples provided by your chosen platform. Most no-code AI platforms offer starting points for common use cases like healthcare chatbots or patient assessment tools. These templates provide structure you can customize rather than starting from a blank slate, significantly accelerating development.

Leveraging Platform Resources and Support

Take advantage of educational resources provided by the platform. Quality no-code platforms offer tutorials, documentation, and often live training sessions that teach best practices for different types of applications. Investing time in these learning resources upfront prevents frustration and helps you build more effective tools.

Connect with user communities where other healthcare professionals share experiences, templates, and advice. These communities often provide practical insights you won’t find in official documentation, including workarounds for common challenges and creative approaches to specific use cases. Learning from others’ successes and mistakes accelerates your own development.

Don’t hesitate to reach out to platform support when you encounter obstacles. Reputable platforms provide responsive support to help users succeed. Whether you have technical questions about platform features or need advice on structuring your patient education content, support teams want to help you build effective applications.

Consider starting with Estha’s comprehensive ecosystem, which includes not just the no-code AI platform itself, but also EsthaLEARN for ongoing education, EsthaLAUNCH for guidance on scaling your programs, and EsthaeSHARE for distributing your AI applications to patients. This complete ecosystem supports healthcare professionals through the entire journey from learning to launching to measuring impact.

Building effective patient education programs with AI is no longer a futuristic concept or the exclusive domain of large healthcare systems with massive technology budgets. Today’s no-code AI platforms have democratized access to sophisticated tools that individual healthcare professionals can use to create personalized, accessible, and impactful patient education experiences.

The transformation this technology enables goes beyond mere efficiency gains. When patients receive education that adapts to their individual needs, answers their specific questions, and remains available whenever uncertainty arises, they become true partners in their healthcare. They make more informed decisions, adhere to treatment plans more consistently, and experience better health outcomes. Meanwhile, clinical teams spend less time answering routine questions and more time on complex care that truly requires human expertise.

The journey to implementing AI patient education doesn’t require you to become a programmer or data scientist. It requires clinical expertise, empathy for patient needs, and willingness to explore new tools that can amplify your impact. Start small, focus on solving specific patient education challenges, and let success in one area build momentum for expanding your AI education programs.

The patients you serve are ready for more accessible, personalized health education. The technology to deliver it is available now, without coding barriers or prohibitive costs. The question isn’t whether AI will transform patient education—it already is. The question is whether you’ll be among the healthcare professionals leading this transformation or catching up later.