Faced with increasing patient volume, complexity of care, and financial and workforce pressures, healthcare organizations are looking for new ways to deliver better outcomes more efficiently. Intelligent automation (AI) has emerged as a powerful solution to achieve operational excellence and improve decision making.
This is the first installment of our blog series on Intelligent Automation in Healthcare, where we will explore what AI really means, how it differs from previous automation solutions, and why it is becoming essential to healthcare transformation.
In this series, we will review 4 key areas of AI and what it means for healthcare organizations:
- This article, Part 1discusses what AI really means and why it’s essential to healthcare transformation
- Part 2 looks at how increasing patient volume and complexity of care are overwhelming healthcare systems and how AI can help
- Part 3 considers the economic and operational pressures facing healthcare organizations and how AI supports cost control and revenue growth.
- Part 4 explores how AI helps healthcare organizations meet regulatory mandates, manage risk, and create future-ready systems.
How intelligent automation is streamlining healthcare workflows
The term automation has been around for decades, but its meaning has evolved significantly in recent years. In the 1990s and early 2000s, legacy automation focused on rules-based workflows, such as claims routing or automating basic tasks. These solutions were rigid, code-intensive, and often required traditional process modeling and orchestration using BPMN (Business Process Model and Notation). While they previously offered some efficiency benefits, they currently lack the adaptability and intelligence necessary to modern and scalable systems.
That’s where AI comes in. Intelligent automation offers a more agile way to enable real-time decision making and dynamic workflows by leveraging different automation technologies together. It is not achieved through a single technology working independently, but through a combination of knowledge and technologies acting together.
Key components of intelligent automation
It is common for companies to approach automation through a technology lens such as robotic process automation (RPA) and then report that they are “fully automated.” These organizations sought a specific solution and, in most cases, are taking the technology beyond its intended purpose.
While partial automation can be achieved through traditional digital process automation (DPA) platforms or process orchestration, AI occurs at the intersection of process mining, DPA, RPAand artificial intelligence (AI). We are now moving towards an era of agent frameswhere automation is not limited to executing repetitive manual tasks or following predefined workflows, but also provides recommendations and decisions based on the situational context and actions that can be taken.
Key components of hyperautomation include:
- RPA and APA: Robotic process automation and the new Agentic process automation to not only automate repetitive tasks but also perform complex automations with contextual awareness.
- GenAI-enabled process discovery: Wearing Generative AI to transform legacy applications and business processes
- AI agent: Autonomous decision making within business workflows
- Case management: Manage complex business processes with defined stages and steps to advance a case through it.
- Intelligent Document Processing (IDP): Digitize unstructured data from claims, enrollment forms and faxes to enable seamless DPA across all platforms.
- Low Code/No Code: Solution development with minimal coding effort
These technologies work together to reduce costs, improve process quality, and increase speed and agility, while improving patient, provider, and employee experiences.
More information: Wellbeing and efficiency engineering in the era of generative AI
The need for intelligent automation is growing
Digital transformation requires agility and flexible frameworks that legacy systems do not support, which is accelerating the adoption of AI in healthcare.
The momentum is undeniable:
- In its 2025 health predictions report, Forrester estimates that half of the top 10 US health insurers will deploy AI-powered tools to support contact center employees and care advocates by 2025.
- In its report FutureScape Worldwide Healthcare Industry 2025 Predictions, IDC predict That by 2027, AI is projected to help the healthcare sector save up to $382 billion by improving efficiency in clinical, operational and administrative workflows.
These trends reflect a growing recognition that automation is not just a cost-savings tool, but a strategic enabler for improved care delivery, operational resilience, and competitive advantage.
You can also enjoy: Reimagining Find Care: How AI is transforming the digital healthcare experience [Webinar]
How intelligent automation can transform healthcare organizations
AI is already transforming healthcare in a wide range of functions. Here are just a few examples of how you can improve operations for patients, providers and payers:
Conversational AI (using GenAI)
- Prompt-based search for context across multiple documents
- Case summary and content comparison.
Document triage
- Channel entry, classification, extraction, validation and data entry.
- Integration of specialized AI and generative AI for faster and more accurate processing
Patient and Provider Operations
- Patient enrollment, insurance and benefit verification, prior authorization, scheduling
- Supplier Validation and NPI Lookup
- Supplier life cycle management.
Pharmacy and supply chain
- Order fulfillment, inventory management, fulfillment and patient profiling.
- Manage supply chain disruptions with AI
Consultation and attention
- Case management, utilization management, clinical analytics and value-based care
- Pre-Authentication Modernization
Claims processing
- End-to-end automation, from claims ingestion to quality assurance and auditing.
- Member and Provider Outreach, Medicare and COB Coordination
Registration
- End-to-end automation of the registration process for requests received from different channels.
These use cases go beyond simply automating tasks by reimagining entire workflows to deliver better results, reduce costs, and improve efficiency.
Read more: Revolutionizing clinical trial data management with AI-powered collaboration
Empower smart healthcare solutions through automation
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AND don’t miss part 2 of this series, where we will explore how increasing patient volume and complexity of care are overwhelming healthcare systems, and how AI can help. See you later!
