The healthcare industry is undergoing a generational shift, driven by the need for efficiency, accuracy, and patient-centric care. At the heart of this transformation is intelligent automation (IA), a powerful convergence of technologies reshaping how healthcare organizations operate. Whether it’s streamlining administrative tasks or enhancing clinical outcomes, IA is paving the way for an autonomous future.

Difference Between RPA and Intelligent Automation

Robotic Process Automation (RPA) and Intelligent Automation (IA) are often used interchangeably, but they serve distinct purposes. RPA involves software bots that automate repetitive, rule-based tasks by mimicking human interactions with digital systems. In healthcare, RPA excels at tasks like claims processing, patient registration, and appointment scheduling, reducing manual effort and errors. For example, RPA can verify insurance eligibility in seconds, compared to minutes for a human.

Intelligent Automation, however, goes beyond RPA by integrating artificial intelligence (AI), machine learning (ML), Generative AI, NLP and analytics. While RPA follows predefined rules, IA enables systems to learn, adapt, and make decisions. For instance, IA can analyze unstructured data, such as physician notes, to optimize billing codes or predict patient no-shows. This cognitive capability makes IA ideal for complex processes requiring reasoning, such as revenue cycle management (RCM) or clinical decision support.

Mphasis Use Cases in Revenue Cycle and Virtual Agents

Mphasis leverages IA to address critical challenges in revenue cycle management and patient engagement. In RCM, Our solutions such as Mphasis Javelina® and Mphasis DeepInsights™ employ Intelligent Automation to streamline processes like claims processing, eligibility verification, and payment posting. These tools can also extract data from disparate systems, such as electronic health records and automate coding and billing, cutting processing times and improving cash flow. This can be critical for patients and healthcare providers, as a significant percentage of claims are denied due to errors like missing documentation.

Mphasis also excels in deploying virtual agents powered by IA. These AI-driven chatbots and voice bots handle patient inquiries, appointment scheduling, and follow-up reminders, enhancing patient experience while reducing administrative burdens. By analyzing patient data in real-time, virtual agents provide personalized communication, such as tailored reminders for medication adherence, reducing no-show rates. Mphasis’ virtual agents operate across multiple channels, ensuring seamless support and freeing staff to focus on clinical tasks. These use cases demonstrate how IA can optimize both financial and patient-facing operations.

Leveraging our deep technical expertise and experience with IA, Mphasis was able to help a health plan provider achieve 90% improvement in process efficiency while automating 70% of the testing process by using automation tools and processes. Learn more.

Clinical Trial Automation

Clinical trials are data-intensive and time-consuming, but IA is revolutionizing this domain. Automating trial processes, such as patient recruitment and data analysis, accelerates drug development and reduces costs. IA systems can analyze vast datasets to identify eligible patients by cross-referencing EHRs, medical histories, and inclusion criteria, a task that manually takes up to 35 hours per application. Digital workers can process thousands of applications in hours, improving trial efficiency.

Moreover, IA enhances data management by automating data entry, monitoring, and reporting, ensuring compliance with regulatory standards. For instance, AI-powered tools can detect anomalies in trial data, flagging issues for review. Companies like SS&C Blue Prism have implemented IA to streamline trial workflows, enabling pharmaceutical firms to bring drugs to market faster. As IA evolves, predictive analytics will further optimize trial design, forecasting outcomes and identifying risks early.

ROI and Scale Metrics

The return on investment (ROI) for IA in healthcare is compelling. Studies suggest automation can save $200–360 billion annually in the U.S. healthcare system by reducing administrative costs and errors. Specific metrics include a 25% increase in successful claim resolutions, 80% reduction in helpdesk overload, and 20% improvement in referral conversion rates. Implementations typically achieve ROI within six months to a year, driven by labor cost savings and improved revenue collection.

Scalability is another advantage. IA platforms, such as those from AutomationEdge, are designed to handle surging demand, integrating with existing systems without disruption. Metrics like process cycle efficiency (PCE) highlight scalability; one study reported PCE rising from 69.07% to 95.54% after IA implementation. Scalable IA solutions ensure healthcare organizations can adapt to growing patient volumes and regulatory changes.

How to Get Started

Embarking on an IA journey requires a strategic approach:
1. Assess Processes: Identify repetitive, high-volume tasks in RCM, patient scheduling, or clinical trials suitable for automation. Prioritize processes with clear ROI potential.
2. Choose the Right Platform: Select an IA provider like Mphasis or Automation Anywhere, ensuring compatibility with existing EHRs and compliance with HIPAA.
3. Start Small: Pilot IA in a single area, such as claims processing, to measure impact and refine workflows before scaling.
4. Train Staff: Educate employees on IA’s benefits to alleviate job displacement fears, emphasizing its role in enhancing clinical focus.
5. Monitor and Optimize: Use analytics to track KPIs like error rates and processing times, continuously improving IA performance.

By leveraging IA, healthcare organizations can achieve operational excellence, improve patient outcomes, and prepare for an autonomous future. The time to act is now—embrace intelligent automation to transform healthcare delivery.