Healthcare Intelligent Automation: Use Cases & Examples [2023]

ByPaul Christiano Last Update on

Healthcare Intelligent Automation: Use Cases & Examples [2023]

Intelligent automation is transforming healthcare by combining cutting-edge technologies like robotic process automation (RPA), artificial intelligence (AI), machine learning (ML) and natural language processing (NLP). According to Accenture, scaling intelligent automation in healthcare could save over $150 billion in annual spend in the US alone. This game-changing shift promises to reduce costs, boost efficiency, improve patient experiences and empower medical professionals.

In this comprehensive guide, we’ll explore real-world examples and use cases of healthcare intelligent automation in action. Let’s dive in!

What is Intelligent Automation?

First, what exactly is intelligent automation? Intelligent automation refers to using automation software enhanced by AI capabilities like ML and NLP. This gives systems the ability to handle unstructured data, learn over time, and mimic human judgment and decision making.

Key technologies powering intelligent automation include:

  • Robotic process automation (RPA) – Software bots that automate repetitive, rules-based tasks.
  • Artificial intelligence (AI) – Enables systems to sense, comprehend, act and learn.
  • Machine learning (ML) – Algorithms that analyze data to learn and improve autonomously.
  • Natural language processing (NLP) – Understands and interprets human language.
  • Computer vision – Automates tasks involving analyzing visual data and imagery.

Combining these leading-edge technologies allows developing intelligent bots capable of end-to-end process automation. These bots can integrate seamlessly across healthcare systems, leverage unstructured data, and handle dynamic decision making.

Why Intelligent Automation in Healthcare?

Healthcare has emerged as a major adopter of intelligent automation, with projected growth of over 20% CAGR in the sector according to Grand View Research. What’s driving this demand?

Intelligent automation enables healthcare organizations to:

  • Reduce administrative and repetitive manual work
  • Accelerate processes and increase efficiency
  • Lower operational costs
  • Improve patient experiences and satisfaction
  • Unlock staff productivity to focus on critical tasks
  • Strengthen compliance and data security

In a survey by Deloitte, 61% of healthcare executives cited improving operations and reducing costs as a top goal of their automation programs. Intelligent automation serves as a powerful catalyst for healthcare’s digital transformation.

Use Cases and Examples

Now, let’s explore some of the top use cases and real-world examples of healthcare intelligent automation in action:

Customer Service and Scheduling

Automating routine customer service interactions through AI-powered chatbots can significantly improve patient experiences. Virtual assistants can handle appointment bookings, insurance questions, reminders, rescheduling and more. This provides 24/7 self-service options.

For example, Cooper University Health Care saw call volume drop 28% after implementing a chatbot fielded over 1 million patient queries in its first year. Meanwhile, bots sending appointment reminders and follow-ups have reduced no-shows by up to 30% according to Business Wire.

Regulatory Compliance

From HIPAA to FDA regulations, healthcare deals with immense compliance requirements. Intelligent bots can continuously monitor systems and workflows to automatically detect and flag any risks or violations.

Blue Cross Blue Shield improved regulatory transparency by using RPA to automatically log every action during claims processing. This also reduced compliance risks without human intervention.

Insurance Claims Processing

Insurance claims processing involves extensive amounts of manual data entry, validation checks, and form submissions. RPA bots excel at rapidly automating these repetitive, rules-based steps. Meanwhile, AI and NLP extract unstructured data from documents and detect discrepancies.

Healthcare payment platform InstaMed uses intelligent automation to process over 40 million payments annually. This delivered 92% straight-through processing rates according to a case study.

Additional Use Cases:

  • Revenue cycle management
  • Clinical documentation
  • Patient appointment reminders
  • Hospital bed management
  • Medical coding
  • Prior authorizations


Image source: research.expertbeacon.com

Healthcare Analytics

Automation and AI can transform data analytics in healthcare. Bots can aggregate disparate data into structured formats. AI models uncover insights from unstructured data. Together, they enable real-time analytics and dashboards.

For example, AI analyzes patient medical records at Memorial Sloan Kettering Cancer Center to recommend personalized therapies. Meanwhile, automation enables live dashboards tracking key operational metrics across Cincinnati Children’s Hospital according to a case study.

Intelligent Automation Platforms for Healthcare

Leading technology vendors offer intelligent automation platforms tailored for healthcare‘s needs:

Here‘s a brief overview comparing top platforms:

  • UiPath – End-to-end platform with advanced AI capabilities. HIPAA-compliant bots.
  • Automation Anywhere – User-friendly with integrated process mining.
  • Blue Prism – Strong compliance and security features. High scalability.
  • WorkFusion – Heavy focus on AI and ML with NLP integration.
  • Kryon – Pre-built solutions for common healthcare tasks.

When evaluating platforms, healthcare organizations should consider security, compliance, AI capabilities, pre-built templates, ease of use, total cost of ownership and vendor experience in the healthcare sector.

Case Studies

Now let’s look at two case studies highlighting successful intelligent automation adoption in healthcare:

ApprioHealth Automates Insurance Claims Processing

Problem: ApprioHealth provides technology solutions for maximizing healthcare insurance reimbursements. Manual processing of Medicaid applications and claims across disparate systems was inefficient.

Solution: RPA bots were implemented to automate repetitive data entry and processing steps. Computer vision automation was then layered to enable processing unstructured data from any system interface.

Results:

  • 7x increase in automation rates
  • 90% reduction in missing information
  • Improved compliance and data accuracy

AMN Healthcare Transforms Timesheet Processing

Problem: AMN Healthcare supplies medical staffing to healthcare facilities. Submitting, tracking and processing staff timesheets was manual, tedious and error-prone.

Solution: Nurses now photograph and submit timesheets via mobile app. Intelligent automation bots process the timesheet photos, reducing manual intervention by 68%.

Results:

  • 68% faster timesheet processing
  • 50% less missing timesheets
  • 83% lower labor costs

The Future of Healthcare Automation

Adoption of healthcare intelligent automation is still in early phases, with vast possibilities ahead. Per Global Market Insights, the healthcare AI market alone is projected to grow over 20% annually reaching $50 billion by 2027.

We will see AI and bots becoming integral components across medical systems and processes. According to Accenture, scaling automation in healthcare could save over $150 billion in annual costs in the US. With intelligent automation, healthcare workers can dedicate more time to improving patient outcomes and experiences.

In summary, automating repetitive tasks allows healthcare professionals to focus on high-value work. Intelligent automation also makes care more affordable and accessible. Leading healthcare providers are increasingly embracing automation and AI to drive the next phase of innovation. The future is bright for intelligent automation in healthcare!

I am Paul Christiano, a fervent explorer at the intersection of artificial intelligence, machine learning, and their broader implications for society. Renowned as a leading figure in AI safety research, my passion lies in ensuring that the exponential powers of AI are harnessed for the greater good. Throughout my career, I've grappled with the challenges of aligning machine learning systems with human ethics and values. My work is driven by a belief that as AI becomes an even more integral part of our world, it's imperative to build systems that are transparent, trustworthy, and beneficial. I'm honored to be a part of the global effort to guide AI towards a future that prioritizes safety and the betterment of humanity.

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