2 huge announcements! Watch video
Automation softwareAutomation thoughts

AI Automation in Healthcare

By
on

According to Global Newswire, the global healthcare automation market was valued at USD 35.92 billion in 2023 and is projected to reach USD 80.28 billion by 2032. This data shows that AI is already playing a huge role in the healthcare space.

AI in healthcare is already applied in areas from automating routine administrative tasks to supporting complex clinical decisions and generally making healthcare processes faster, more accurate, and more scalable.

In this article, we’ll discuss everything you need to know about AI automation in healthcare

What is Healthcare Automation?

Healthcare automation refers to the use of technology ( AI and automation) to perform repetitive or complex healthcare tasks with minimal human intervention. These tasks can range from administrative processes to direct patient care. The goal is simply to free healthcare workers from tedious tasks so they can concentrate on clinical and operational excellence.

How is Automation Used in Medicine?

  • Healthcare Insurance: AI automation is also used in automating processes in healthcare insurance. For example, Alan, a leading healthcare insurance company uses Activepieces to automate user interview scheduling. This saved 90% of time and helped them conduct 2-3 interviews weekly. You can read the full case study here: https://alan.com/en/blog/discover-alan/a/automate-user-interview-scheduling-using-ai
  • Streamlining Clinical Workflows: In medical practice, automation is used to simplify and accelerate many clinical processes. For example, automated systems assist in automating patient intake data or managing appointment scheduling. This helps physicians and nurses to spend less time on logistics and more time on patients. Routine tasks like answering phone calls or transcribing notes can be handled by automated solutions, freeing clinicians to focus on complex care.
  • Enhancing Decision-Making in Medicine: AI automation acts as a second pair of eyes for healthcare professionals. In diagnostics, AI algorithms can scan medical images (X-rays, MRIs, CT scans) to detect abnormalities or early signs of disease with speed and accuracy that complement human expertise. In treatment planning, AI tools analyze patient data and medical literature to suggest personalized treatment options or flag potential drug interactions. While human doctors always make the final call, these automated insights help ensure nothing is overlooked and can improve diagnostic speed and accuracy.

Key Tools For AI in Healthcare

1. AI Automation Tools:

AI automation tools enable the intelligent automation of repetitive healthcare and healthcare insurance tasks in a HIPAA-compliant way. An example is Activepieces. Activepieces also integrates well with other healthcare software to trigger more complex workflows. Some tasks that Activepieces automates in health include

Appointment Scheduling and Reminders

  • Automate patient appointment bookings through online forms.
  • Send automated appointment reminders via SMS or email to reduce no-shows.

Electronic Health Records (EHR) Management

  • Automatically collect and store patient information from registration forms into the EHR system.
  • Integrate third-party tools (e.g., voice recognition, fingerprint scanners) to capture and update patient data.

Lab Test Orders and Results

  • Deliver lab results to patient portals and notify healthcare providers upon completion

Billing and Insurance Claims

  • Generate bills based on patient prescriptions and send them automatically to patients or insurance companies.
  • Automate the initiation, data extraction, and processing of insurance claims, including pre-filling forms and integrating with external data sources

Clinical Decision Support

  • Set up alerts for overdue or missed screenings, tests, or follow-up appointments for both patients and providers

Patient Communication and Education

  • Automate the sending of educational materials, follow-up reminders, and notifications for medication adherence

Claims Data Capture and Initiation

  • Use automated online forms for policyholders to initiate claims (e.g., health, car, property).
  • Automatically extract and populate claim details from external databases, reducing manual entry errors

Claims Processing and Decision Support

  • Analyze available policyholder data to help with claim decisions.
  • Route claims for approval, flag incomplete submissions, and notify relevant parties of claim status updates

Compliance and Security

  • Ensure all automated workflows encrypt sensitive data and comply with HIPAA and other relevant regulations.
  • Use on-premise deployment to maintain data sovereignty and meet strict security standards

Large Language Models (LLMs)

An example is OpenAI’s ChatGPT or medical-adapted models. They are used to summarize medical records, answer patient queries, or even draft clinical documentation.

(We’ll discuss more on generative AI in healthcare  in the next section.)

2. Clinical AI software

Aidoc, Arterys, and Zebra Medical Vision scan medical images for specific conditions (like detecting brain hemorrhages or lung nodules).

We also have virtual nurse assistants. Examples include Ada Health or Babylon Health.  These use AI to ask patients about symptoms and provide preliminary advice or triage recommendations.

3. Voice recognition and NLP tools

Examples include Nuance Dragon Medical One, an AI tool that automatically transcribes doctors’ spoken notes into written records and can even pick out key medical concepts. Such tools reduce the documentation burden on clinicians.

It’s important to note that AI tools in healthcare are developed to improve efficiency, not to replace humans. For healthcare leaders, this means AI is best used as an augmenting tool. Doctors and staff still oversee care and make the critical judgments, but with AI automation, they are supported by timely insights.

What is Generative AI in Healthcare?

Generative AI refers to AI systems that can produce human-like content, text, images, and even data. In healthcare, generative AI’s most notable form is large language models (LLMs) that can understand and generate text. These models have learned from large amounts of medical literature and data, enabling them to draft documents, answer questions, or even converse with patients and clinicians in natural language.

Application of Generative AI in Healthcare

Generative AI is already being applied in several practical ways in healthcare:

  • Clinical Documentation:

One of the hottest and first uses of generative AI is in automating clinical note-taking and report generation. For example, Microsoft’s DAX (Dragon Ambient eXperience) uses ambient listening and generative AI to draft clinical notes for physician review, saving time on paperwork. Hospitals report that this reduces documentation time significantly, helping combat physician burnout by letting providers spend more time with patients and less at the keyboard.

  • Patient Communication:

. Patients might ask, “What does my blood test result mean?” and a generative AI could explain it in layman’s terms, or a patient could interact with a virtual agent to get guidance on managing a chronic condition. These AI agents can also draft personalized follow-up emails or educational materials for patients, tailored to their reading level and needs.

  • Synthetic Data Generation:

Another emerging use is generating synthetic healthcare data for research and training. Generative AI can create realistic but fake patient records that preserve statistical patterns of real data without exposing actual patient information. This is helpful for developing AI models (where more data is needed) while maintaining privacy. Similarly, generative models can produce simulated medical images (e.g., MRI scans with certain pathologies) to augment datasets for training diagnostic algorithms.

The healthcare sector has quickly recognized the potential of generative AI. A recent late-2024 survey found that 85% of healthcare leaders (across providers, healthtech companies, and insurers) were either exploring or already adopting generative AI solutions.

While generative AI is powerful, healthcare organizations are implementing it carefully. Issues of privacy, security, and bias are top of mind. This is why AI automation tools in healthcare must be used in compliance with HIPAA and other regulations, and outputs need validation. Sensitive patient data must be encrypted at all times

AI Automation for Better Healthcare

AI automation in healthcare helps healthcare enterprises, healthtech innovators, and insurance firms deliver better results at lower costs. By automating the mundane and data-heavy aspects of healthcare, organizations enable their human professionals to do what they do best: provide compassionate, high-quality patient care and make well-informed decisions. The examples above demonstrate that

As of now (mid-2025), we see a strong momentum: nearly every large health system and insurer is investing in AI automation in some form, whether it’s deploying chatbots, piloting an AI diagnostic tool, or automating their billing cycle.

If you’re looking to include AI automation in your healthcare organization, now is the perfect time to take the first step. Activepieces  offers an intuitive, flexible platform tailored for seamless AI automations in healthcare, whether you’re automating patient communication, claims management, or integrating with leading healthtech solutions.

You can start with a free account on Activepieces here