How health systems leverage AI to boost SEO & engage patients
Our SEO team is on the leading edge of healthcare AI and digital marketing. Together with Stewart, they’ve compiled insights and recommendations on how health systems and healthcare organizations can leverage AI to supercharge SEO and patient engagement.
AI is here and it’s transforming how health systems and healthcare organizations connect with patients, deliver care and grow their digital presence. But, with regulatory hurdles, data silos and changing search algorithms, it can be a real challenge to stay ahead and remain competitive.
Is your healthcare organization keeping pace?
Healthcare organizations that strategically combine AI tools with human expertise are improving search engine optimization (SEO) and patient engagement while setting a new standard for trust, efficiency and patient experience.
Let’s explore the biggest SEO and engagement challenges health systems face today and how AI can help overcome them.
9 challenges healthcare organizations face in SEO & patient engagement
SEO challenges
- Trust and credibility
Deliver consistent, credible and compliant content at scale to build lasting trust and credibility among patients, caregivers and referring physicians. - Regulatory constraints and medical accuracy
Adhere to strict regulations, such as HIPAA, for online content and patient interactions. All content must be medically accurate, well-sourced and reviewed by experts. - Personalization at scale for diverse patient segments
Deliver the right message at the right time, across diverse populations. - Technical SEO and site architecture complexity
Understand and adapt to changing search algorithms. Site architectures must be designed to serve the target audience and web crawlers, ensuring optimal search visibility and discoverability.
Patient & provider engagement challenges
- Data security and privacy
Protect sensitive patient data with robust security protocols to maintain patient trust and credibility while also enabling engagement. - User experience on digital platforms and patient portals
Developing user-friendly, intuitive portals and platforms that support conversion and satisfaction. - Siloed patient data across platforms
Unify insights across platforms to improve personalization, insights and patient experiences. - Workforce burnout and shortage
Free staff from repetitive administrative tasks with AI to support authentic connection and patient engagement. - Technology integration
Ensure AI tools and telehealth solutions work seamlessly within existing workflows.
AI tools & techniques that health systems use
AI is embedded across healthcare marketing, from drafting compliant patient communications to predicting which topics will attract search traffic.
Here’s a look at the key tools and how they’re already being applied:
Natural language generation (NLG) and large language models (LLMs)
These tools manage mountains of unstructured data found in clinical notes, patient records and research:
- Generative AI tools (e.g., ChatGPT, Claude, Perplexity and healthcare-focused LLMs) to draft administrative and patient communication, summarize complex clinical records and results and automate routine communications.
- Nuance Dragon Ambient eXperience (DAX) to automatically convert doctor-patient conversations into detailed medical notes.
- Elicit, PubMed.AI, PathAI, Delphi-2M and BioGPT to aid in medical research, accelerate discovery and improve diagnostics and treatment strategies.
Natural language generation of health content helps teams create compliant, SEO-optimized patient materials more efficiently.
Predictive analytics for user intent and topic modeling
Predictive analytics tools help identify user intent and content opportunities to improve SEO, patient engagement and patient retention. Examples range from enterprise platforms like Microsoft Azure Machine Learning (ML), Google Cloud AI, IBM SPSS and SAS Viya, to specialized solutions like H2O.ai, Invoca, Adobe Analytics and Qlik Sense.
Your choice of AI tools depends on your team’s technical skills, available data and use cases.
AI chatbots
Conversational AI chatbots and agents support patient engagement, triage, scheduling and chronic condition management. They can also help address issues like workforce shortages by allowing healthcare professionals to focus on patient care. Examples include Ada Health, Sensely, Babylon Health and WellSpan Health Assistant.
A 2025 review in Frontiers for Public Health found hybrid AI chatbots improved patient engagement by 30 percent.
Personalization engines
Personalization engines deliver AI‑driven content personalization to optimize patient experiences, improve engagement and reduce costs. Popular solutions include Epic MyChart, Lirio, Salesforce HealthCloud and DearDoc.
Automated SEO functions in action
Streamline these functions to boost online visibility and discoverability:
- Audits: Quickly spot broken links, duplicate content and slow pages
Tool options: Ahrefs, SEMrush, Screaming Frog and JetOctopus - Local SEO: Automatically keep business info accurate across all directories
Tool options: Ahrefs, SEMrush and BrightLocal - Content ideation: Find trending topics and frequently asked questions (FAQs) your audience is searching for
Tool options: SEMRush and Frase - Keyword research: Identify high-value search terms and organize by intent.
- Tool options: SEMrush, Ahrefs, Google Keyword Planner and AnswerThePublic
- Schema implementation: Automatically add structured data like FAQs or location info
Tool options: Schema.org and Google Search Console - Internal linking: Get smart suggestions for connecting related content
Tool options: SEMrush, Google Search Console, Screaming Frog and Air Ops
Best practices & ethical considerations
Ensuring medical accuracy and oversight
Oversight by a qualified healthcare professional is critical for ‘your money your life’ (YMYL) content. YMYL means any content that could significantly impact a person’s health, financial stability, safety or overall well-being.
Even with careful prompting or searches, AI can produce something called hallucinations, or confident but incorrect or misleading information. In healthcare, the risks of errors like these are exponentially higher. To protect patients, content must be monitored for hallucinations prior to publishing. Also, predictive analytics should be used to anticipate where these types of errors are most likely to occur.
Proper oversight ensures that all content remains medically accurate, trustworthy and safe for those making critical health decisions.
Addressing bias, privacy and HIPAA concerns
AI models are only as good as their training data—garbage in, garbage out. Meaning they can inherit and amplify bias, leading to unfair or discriminatory results.
These technologies also rely on vast amounts of PHI, making data anonymization (removing all identifiable details), encryption (securing data storage and transmission), human oversight and regulatory governance critical for protecting content integrity and preserving patient privacy.
Hybrid approach: human + AI collaboration
As Forrester analyst J.P. Gownder notes, “human oversight remains critical for setting goals, ensuring quality and making judgment calls.”
Human editors remain critical, reviewing AI-generated content for SEO and medical accuracy before publication, improving efficiency while maintaining patient trust.
Measuring ROI and iterative improvement
The value of AI in healthcare marketing lies in measurable outcomes. Vital KPIs include:
- Marketing impact
SEO rankings, organic traffic and cost per new patient acquisition - Engagement
Portal logins, chatbot use and appointment conversions - Efficiency
Saved time on repetitive tasks - Trust and quality
Patient satisfaction and clinical accuracy checks
Start small, measure results, refine and then expand once outcomes are validated. Think of AI adoption like a clinical trial—get your evidence first.
Implementation roadmap for health systems and healthcare organizations
Audit current content, site and engagement metrics
Assess SEO, content quality, patient engagement and workflow bottlenecks to set your baseline.
Pilot small AI use cases
Start with small or low-risk applications, such as:
- AI-assisted keyword research or content ideation for one service line
- Try an AI chatbot for scheduling or patient support
Scale with governance, workflows and review process
Prioritizing human oversight to catch gaps in reasoning and prevent AI hallucinations is essential.
Establish a clear, human-first review process to ensure content accuracy, compliance and quality. Also, train staff to integrate AI into daily workflows while maintaining accountability.
Monitor, evaluate and refine AI models and content
Track things like engagement, efficiency, patient satisfaction and SEO results. Continuously monitor, refine and optimize as needed.
| AI is no longer optional. Health systems and healthcare organizations that strategically apply AI for medical content optimization will enhance patient engagement, streamline operations and increase their online visibility and discoverability—keeping them relevant and competitive. |
Frequently asked questions
Can AI really write accurate health content?
While AI can write health content, it may not always be medically accurate. It should be treated as a first draft or tool, not a final, authoritative source. Key risks for AI-generated content include:
- Hallucinations
Confident but entirely false (or nonsensical) information. This can be extremely dangerous for health information. - Data bias
AI is only as good as the data it’s trained on, including any biases within that data (e.g., race, gender, socioeconomic status, etc.). - Outdated information
Medical information is always evolving and improving, but AI models and search results can be affected by temporal bias, meaning they may prioritize older or outdated information. Using AI trained on stale data (or relying on results influenced by bias) can produce inaccurate, obsolete or even harmful content. It’s critical to review and update AI-generated content to ensure it reflects current, relevant, and accurate content using best practices. - Lack of nuance and empathy
Human input is essential for breathing life and lived experiences into your content.
Is AI compliant with HIPAA in content and chatbots?
No. Most commercial chatbots lack the technical, administrative and contractual safeguards for handling protected health information (PHI). Unlike platforms like Google Analytics or Search Console, AI tools (e.g., Perplexity, Claude, ChatGPT) do not provide analytics or visibility into usage patterns, so it’s essential to monitor log files and track AI interactions carefully to maintain oversight and compliance.
How do we avoid AI content being penalized by Google?
To avoid penalties, healthcare organizations must prioritize high-quality, timely and relevant content that demonstrates E-E-A-T (experience, expertise, authoritativeness and trustworthiness), Google’s standard for useful information.
In other words, never publish raw AI-generated content. Repeated use of AI content on top of AI-generated data can contribute “model-collapse,” meaning the quality and originality of content deteriorates over time.
Always, always, always review AI-generated content for accuracy and enhance it with personal experiences, credible sources and unique content that adds value.
What are early wins for health systems using AI?
Health systems and healthcare organizations that leverage AI often begin by streamlining clinical documentation and closing care gaps. These applications provide near immediate and measurable benefits, including:
- Reducing administrative burden
- Improving care efficiency and accuracy
- Enhancing patient care and experience
How do we measure success?
Success for AI in healthcare marketing is measured by:
- Patient outcomes and quality of care
- Operational efficiency and cost reduction
- Trustworthiness and ethical application
- User satisfaction and adoption
- New patient acquisition
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