How Is AI Used in Healthcare, and How Does It ​Differ From Automation

Key Takeaways:

• Automation handles repetitive, rules-based tasks, while AI learns from data, adapts, and makes decisions in complex scenarios.

• RPA bots mimic human clicks and keystrokes for high-volume, repetitive work, but they don’t “understand.” AI brings reasoning and adaptability, and the biggest gains come from combining both into intelligent automation.

• AI is already powering real-world healthcare RCM use cases like coding, denial appeals, and billing with measurable results.

• GenAI, AI Agents, and Agentic AI—they’re not the same. GenAI pulls useful info, AI agents combine intelligence + action, and agentic AI adds autonomy to plan and adapt toward long-term goals.

Several pressing forces are reshaping healthcare today, and you’re likely feeling their impact already.

Regulations keep shifting, with CMS rolling out new billing codes each year and HIPAA tightening compliance requirements. At the same time, margins continue to narrow. Processing a claim manually can cost $7 to $9.

Meanwhile, patients expect clear bills, quick answers, and personalized payment options, leaving little tolerance for outdated, slow processes.

It’s no wonder healthcare leaders are turning to technology for relief. But often AI and automation are lumped together as if they’re the same tool. Spoiler alert: They aren’t!

Add in the buzz around GenAI, AI agents, and agentic AI, and even seasoned physicians and RCM leaders find themselves asking: What’s the real difference? How do these technologies work in practice? And how do they actually help reduce costs, cut denials, and improve patient experience?

This article will break it down for you. You’ll learn how automation and AI differ, where they overlap, and how advanced AI (like GenAI, AI agents, and agentic AI) is already powering real-world healthcare RCM use cases.

What Is The Difference Between AI and Automation in Healthcare?

While AI and automation often work side by side, they are fundamentally different tools with different strengths.

What Is Automation in Healthcare​?

Automation is when a system is set up to perform a task automatically, without requiring human intervention each time. In healthcare, that means offloading repetitive, rules-driven work so staff can focus on higher-value tasks.

This could involve:

• Sending appointment reminders to patients
• Auto-submitting claims to insurance companies
• Updating patient records across systems without manual input

Of course, not all automation works the same way. Some are built on static rules (rule-based automation) that execute the same way every time. This type of automation runs on if-this-then-that rules, i.e., with a fixed logic.

RCM Use Case:

If payer = Blue Cross, always apply modifier X when procedure Y is coded.

On the other hand, intelligent automation blends automation with AI to make decisions on the fly. It’s when automation is enhanced with AI technologies like machine learning, natural language processing, or computer vision, so the system can make decisions, adapt, and handle complexity.

RCM Use Case:

1. Reads the denial letter using NLP (Natural Language Processing)
2. Identifies the denial reason category (eligibility, medical necessity, documentation)
3. Drafts an appeal letter with GenAI
4. Routes complex denials to the right team automatically

What Is RPA in Healthcare? 

Robotic Process Automation (RPA) is a more advanced form of rule-based automation that uses software bots to mimic human actions at the user interface level. This may include human clicks, typing, and screen activity to follow rules.

RCM Use Case:

Imagine you need to check claim statuses across 15 payer portals every day.

Traditionally, your billing staff would log into each site one by one, search claims, download statuses, and update your billing software.

With RPA, a bot can do this instead:
7:00 AM → Open payer portal #1, logs in, pulls claim status
7:05 AM → Move to payer portal #2
…and so on, until all statuses are pulled and copied into your system

The bot doesn’t “understand” the claims. It’s just following the clicks and keystrokes your staff would.

What Is AI in Healthcare?

While automation executes predefined tasks, artificial intelligence (AI) goes a step further by mimicking human intelligence. It learns from data, adapts to new information, and makes decisions in complex situations.

So, how is AI used in healthcare?

• Predictive analysis that involves identifying high-risk patients
• Supporting clinical decision-making, such as analyzing radiology images for signs of disease
• Streamlining revenue cycle workflows by suggesting accurate billing codes from clinical notes or drafting denial appeal letters that cite medical necessity

Types of AI

AI isn’t a single technology. It’s an umbrella term covering different techniques, each suited to specific problems in healthcare. Here are the main types you’ll come across:

1. Machine Learning (ML): The foundation of modern AI. Algorithms learn patterns from data instead of following only human-written rules. In healthcare, ML can predict which patients are at risk of readmission or forecast treatment outcomes.

Technology behind it:

• Linear & Logistic Regression: predicting continuous values or binary outcomes
• Decision Trees & Random Forests: splitting data into branches; forests combine many trees for accuracy
• Support Vector Machines (SVMs): finding optimal boundaries between data classes

RCM Use Case:

CombineHealth’s Mark (AI medical billing agent) learns from prior denial patterns to prevent future errors.

2. Deep Learning: A subset of ML that uses neural networks for advanced pattern recognition. Deep learning is especially useful in imaging, genomics, and drug discovery.

Technology behind it:

• Artificial Neural Networks (ANNs): general deep models
• Convolutional Neural Networks (CNNs): excels at image analysis
• Recurrent Neural Networks (RNNs) and successors (LSTM, GRU): handles sequences like time series or clinical notes

ML vs. Deep Learning:

ML = learns from data (could be simple models like decision trees).
Deep Learning = learns from massive, complex datasets using neural nets (better for images, speech, text).

3. Natural Language Processing (NLP): Enables computers to understand and extract meaning from human language. Advanced transformer-based models like BERT, RoBERTa, and RadBERT have been applied in radiology to improve comprehension of reports, predict hospitalizations, and enable more accurate text classification.

NLP is an application of ML/Deep Learning to language.

Technology behind it:

• Traditional: Rule-based models, TF-IDF, bag-of-words‍
• Modern: Word embeddings (Word2Vec, GloVe), Transformer models (BERT, RoBERTa)

4. Computer Vision (CV): Interprets visual data like medical images. In healthcare, CV powers diagnostic tools that read X-rays, CT scans, and pathology slides. CV is an application of ML/Deep Learning to images.

Technology behind it:

Built largely on CNNs and image-specific architectures:

• Object detection models (e.g., YOLO, Faster R-CNN)
• Segmentation models (e.g., U-Net, Mask R-CNN)

Clinical use case:

AI reading X-rays or CT scans for tumors.

5. Large Language Models (LLMs): LLMs are a new, powerful class of NLP models built with deep learning and trained on massive amounts of text. Instead of just scanning for keywords, LLMs learn the patterns of language itself: grammar, context, semantics, and even tone.

And because they’ve seen so much text, they can generalize across tasks: answer questions, summarize, translate, generate new sentences, etc

Technology behind it:

• Transformer architecture: Allows models to capture long-range dependencies in text.
• Self-attention mechanism: Lets the model weigh the importance of each word relative to others in a sequence
• Pre-training: Learn general language patterns from massive datasets (billions of words).
• Fine-tuning: Adapt to specific domains (like healthcare or law).

Examples of LLMs:

• GPT series (OpenAI) → text generation, chat-based reasoning
• BERT (Google) → language understanding, classification, embeddings
• RoBERTa → Optimised BERT variant for better performance
• PaLM (Google/DeepMind) → large-scale model with reasoning and code abilities
• LLaMA (Meta) → efficient, open-source LLM for research and enterprise use

6. Generative AI (GenAI): At its core, GenAI models are trained to model probability distributions of data.

That means they can:

• Generate new content that looks like the training data (the “creative” part).
• Transform existing content into new forms (summarization, translation, style transfer).
• Fill in the gaps or predict missing information (autocompletion, inpainting).

RCM Use Case:

CombineHealth’s Amy (AI medical coding agent) reads the provider’s clinical notes and generates the correct ICD-10 code.

RPA vs. AI in Healthcare: When To Use What?

RCM workflows in healthcare are too complex for a one-size-fits-all approach. Some tasks are best handled by RPA, others demand AI, and the most effective solutions often combine both.

Here’s when to use what:

RPA: for Repetitive, Rule-Based Work

RPA shines where the rules are clear and the volume is high.

Think about those repetitive billing tasks your staff spends hours on: pulling claim statuses from payer portals, or posting payments from ERAs/EOBs. None of these requires judgment. They just need to be done consistently, every single day.

That’s exactly where RPA delivers. It works like a digital clerk—logging in, clicking through screens, and moving data—so your team can focus on higher-value tasks.

AI: Best for Decision-Heavy, Data-Driven Tasks

AI is built for interpretation and adaptability.

RCM leaders face dynamic challenges: denial management, coding accuracy, fraud detection, and patient collections. These require analyzing large amounts of structured and unstructured data—payer policies, clinical notes, patient payment histories.

That’s where AI shines. It doesn’t just follow rules; it learns from past claims, adapts to new patterns, and helps predict the best next step. Imagine a denial predictor that flags risky claims before submission, or an NLP model that reads provider notes to suggest the right CPT codes.

When To Combine RPA and AI

The TL;DR answer for making quick decision:

• If your task is repetitive, rules-driven, and cross-system → Use RPA
• If it requires judgment, adaptability, or large-scale data analysis → Use AI
• If it requires both → Use RPA + AI for end-to-end efficiency

The longer answer?

The real value comes from using both together, which is precisely what we call intelligent automation. The rule of thumb:

• Use RPA to do
• Use AI to decide
• Combine them when your workflow requires both

Here’s an example to make it clearer: claim denial management.

AI reviews denial letters, categorizes them, and predicts the likelihood of a successful appeal.

RPA takes that intelligence, pulls the necessary data from payer portals, fills out the appeal form, and submits it automatically.

GenAI, AI Agents, and Agentic AI: Are They All the Same?

No, they’re not all the same. All these are AI technologies that are built on top of each other.

• AI is a broad field.
• GenAI pulls up-to-date info, and helps non-experts perform technical tasks with assistance features. It’s typically powered by large language models (LLMs) or diffusion models, and it responds to prompts, but doesn’t act on its own.
• AI agents combine intelligence and action to complete tasks, often combining an LLM or other AI model with integrations to external tools and data sources.
• Agentic AI adds autonomy, planning, and adaptability to pursue long-term goals. Instead of waiting for human prompts or scripted workflows, these systems can pursue high-level goals autonomously.

The table below highlights their key differences in detail:

What Are AI's Ethical Concerns in Healthcare?

AI has enormous potential to improve healthcare outcomes and streamline operations, but its use also raises serious ethical concerns. This includes:

Transparency, Fairness, and Accountability

AI models are often criticized as “black boxes.” If a system denies a claim or flags a diagnosis, providers need to know why. 

Lack of explainability undermines trust and may introduce bias. Governance frameworks stress that AI in healthcare must be transparent, auditable, and fair, ensuring patients and providers understand how decisions are made.

Data Privacy and Security

AI systems require massive amounts of patient data. That raises questions about HIPAA compliance, anonymization, and cybersecurity. Protecting sensitive information is non-negotiable—any breach or misuse could erode both patient trust and institutional credibility.

Bias and Misinformation

Healthcare data may reflect systemic biases, incomplete patient populations, or errors in documentation. 

When trained on biased or flawed data, AI systems risk amplifying these issues. LLMs in particular can “hallucinate” or generate plausible-sounding but incorrect information. In healthcare, even small mistakes can have serious consequences.

Legal and Governance Frameworks

Who is responsible when an AI makes a mistake? Legal liability and AI governance are still evolving. Clear frameworks are needed to define accountability, resolve disputes, and set standards for safe, fair use across clinical and administrative workflows.

Education and Empowerment

Healthcare professionals need the training to understand, supervise, and question AI outputs. Without proper data literacy and empowerment, providers risk becoming passive users instead of active overseers—an ethical concern in itself.

Put AI to Work Across Your RCM Processes Today

As we’ve seen, not all “AI” is the same. There’s a clear difference between automation, rule-based systems, and advanced AI models like LLMs and Generative AI. Each plays a role: automation ensures consistency, AI adds intelligence, and together they can transform workflows that were once repetitive, error-prone, or overly dependent on manual effort.

Of course, these gains come with responsibilities. Healthcare organizations can’t simply “add AI” and hope for the best. They need solutions that are explainable, compliant, and designed for real-world RCM complexities.

That’s where platforms like CombineHealth’s AI Agents come in. Purpose-built for healthcare revenue cycle management, CombineHealth doesn’t just layer AI on top of existing processes—it embeds intelligence across every step of the cycle:

• Jessica transcribes provider-patient encounters into structured EHR-ready notes.
• Amy codes and audits with line-by-line rationale.
• Mark manages billing end-to-end, seamlessly navigating payer portals.
• Adam follows up on A/R, making payer calls and retrieving status updates.
• Rachel drafts payer-specific appeal letters backed by coding and policy evidence.

Together, they deliver the best of automation and AI: fast, accurate execution combined with contextual decision-making. The result is fewer denials, faster reimbursements, and a smarter, more resilient RCM process.

Ready to invest in AI that gets healthcare RCM and abides by the rules? Book a demo with us!

FAQs

How does AI help in healthcare?

AI supports diagnosis, coding, billing, and patient care by learning from data, spotting patterns, and making decisions. It improves accuracy, reduces manual work, and enables faster, more personalized care.

How does AI reduce costs in healthcare?

AI cuts costs by preventing claim denials, automating repetitive tasks, reducing errors, and speeding up reimbursements. This lowers labor expenses and shortens A/R cycles, improving margins.

How could doctors use AI?

Doctors use AI to analyze scans, predict patient risks, transcribe notes, and suggest accurate codes. This reduces paperwork, supports clinical decisions, and frees time for patient care.

What are the three AI technology categories in healthcare?

The three core categories are: machine learning (pattern recognition and prediction), natural language processing (understanding medical text and speech), and computer vision (analyzing medical images).