An unprecedented pandemic, historic labor shortages, long-awaited revenue cycle innovations, and continued challenges with revenue leakage make now the opportune time to transition the revenue cycle into a new age. Integrating artificial intelligence (AI), machine learning (ML), and other intelligent automation (IA) programs into the revenue cycle can help alleviate today’s labor shortfalls, improve efficiency, increase net patient service revenue (NPSR), and lift employee job satisfaction.

In fact, 68% of health system executives believe that further investment is needed in intelligent automation (IA) programs to advance their overall enterprise goals, but 70% simultaneously reported that they have yet to start a strategic IA program.[1] So, why the delay? One reason may be the lack of clear consensus among health executives regarding where and how IA can drive value.[2] Another reason is that misconceptions abound, with many vendors selling IA as a “solution purchase,” as opposed to what is truly needed to be successful— a holistic transformational program requiring detailed attention to all aspects of implementation and execution. To compound the lack of clarity, revenue cycle processes are not easily changed and often lack strategic direction. This change resistance has caused many leaders to be interested in IA only for processes that can be 100% automated. But these processes are nearly impossible to find, thus further slowing IA solution deployment.

Where revenue cycle leaders should be setting their sights is on an integrated IA program. A successful IA program will first require integration of a digital workforce, not full replacement of employees. The key is the ability to unravel decades of entrenched revenue cycle processes to identify where organizations can start incorporating a digital workforce to operate alongside traditional employees. This integration approach will lower the barriers to entry while still bringing the benefits to the revenue cycle and freeing current employees from repeatable, uninspired tasks.

Where AI, ML, and IA Can Bring the Most Value

Beyond the movement to better electronic health record (EHR) and patient accounting systems, revenue cycle operations has not seen much innovation in the past 10–20 years, and the current environment still often requires employees to perform tasks for which they are overqualified. Too frequently, organizations ask revenue cycle employees to act as a “manual interface” between various systems and web portals, migrating data from one source of truth to another. These activities are often dull, do not intellectually stimulate staff, and as a result, can be ripe for human error. An IA-enabled digital workforce allows employees to focus on higher order engagement, which will also lead to increased job satisfaction—a strategic imperative in a severe labor shortage environment.

Additionally, organizations need analysis of revenue cycle data to identify actionable insights, such as payer patterns, revenue risks, and root cause errors. This type of insight is not easily identified, and in some cases is impossible, with manual human review. ML was developed to perform these exact tasks. If deployed appropriately, it can provide the insights financial leaders have long wished for. Revenue cycle activities that use data for repetitive, rules-based, high-volume, or error-prone work can be more effectively executed by nonhuman “workers” suited for such tasks—such as IA-enabled digital workers.

Characteristics of Automated Processes

Revenue Cycle - Digital Workforce

If we ignore traditional revenue cycle roles and look at the functions within the revenue cycle, we can begin to parse out the activities where data is being collected, aggregated, and migrated to different systems or compiled for employees to translate them into insights. These activities are better addressed by digital workers that won’t make data input errors, will work around the clock, and never tire of doing the same thing.

Using Digital Workers to Manage Data

Interact with web portal data 
  • Confirm and update insurance details
  • Check medical necessity and authorization rules
  • Check status of authorization numbers
  • Check status of unpaid claims
  • Appeal rule-based denials (e.g., no authorization, missing records, ineligibility, coordination of benefits, etc.)

Deliver data insights
  • Rapidly mine revenue cycle data to create “best next action” suggestions, deliver insights on payer behavior, and source root causes of common errors
  • Leverage system audit trails to deliver real-time quality assurance (QA) reviews
  • Aggregate data from multiple system sources
  • Communicate daily provider-specific charge performance

Enable patient data self-service
  • Electronically query patients for missing or inaccurate registration information
  • Deliver accurate patient financial estimates without human interaction

How Employees and the Digital Workforce Integrate for the Greatest Impact

We should resist thinking about digital workers immediately or easily replacing employees. This can cause delays to adoption of digital workers as leaders wait for the perfect use case to deploy IA solutions. While these digital capabilities are continuously improving, there are still limitations and gaps to what IA solutions can and cannot do well.

This is where employees and digital workers come together to greatly enhance current capabilities. For example, a digital worker may be able to perform thousands of eligibility and benefit checks in one day, but it will have a hard time deciphering benefit limitation for a patient’s specific visit and an even harder time explaining those limitations to the patient. The employee is better suited to empathetically counsel the patient through their options. This is where we need efficient digital-to-employee integration.

Employees need to know when to step in, and digital workers need to be carefully programmed to communicate the appropriate handoff messages. Not every handoff to an employee requires the same degree of attention and response. Considerations will need to be made to avoid the revenue cycle equivalent of clinical “alarm fatigue.” For example, if a digital worker completes its daily review of the previous day’s charging performance by departmental providers, findings can easily range from simple missed charges to severe compliance mistakes. Such wide ranges need to be both programmable in the digital worker and thoroughly supported with processes to enable an appropriate employee response.

The bottom line: an effective IA program is not installing a simple, off-the-shelf software solution. The IA technology is just one component of integrating IA into the current revenue cycle. Implementing an effective IA program is business transformation. Successful transformational journeys require detailed attention to policies, procedures, business processes, employee training, change management, and communication.

Where to Redeploy Employees for Higher Value Roles

As employees are transitioned away from repeatable, mundane tasks that their digital co-workers will take over, the number of employees working in legacy revenue cycle roles can naturally be reduced. Some may think the immediate result would be forced layoffs. However, a multi-sector study of companies that launched similar IA programs only showed an 11% job loss impact, as the remaining 89% of employees were redeployed, reskilled, and upskilled.[3] Additionally, these newly reskilled employees could help solve the workforce challenges currently experienced in the market and drive greater revenue realization.

Employee redeployment should drive return on investment by better leveraging roles that require empathy, good judgement, and creativity. Many of these roles involve working with patients, clinical staff, and providers. For example, financial counselors, revenue integrity specialists, and trainers are highly valuable but often understaffed roles. These types of roles typically allow employees to feel closer to the health system’s mission and vision, laying the foundation for higher job satisfaction and lower attrition rates—all while delivering greater value to the organization and its consumers.

Building an Optimized Revenue Cycle Empowered by IA

An IA program is more than the procurement of a defined solution or simple software addition. Successful execution requires organizations to transform how they think about the revenue cycle and rethink the workforce paradigm. Doing so will help to accelerate the integration of digital workers alongside employees to leverage the unique capabilities of each and enable an optimal workforce and improved performance outcomes.

What’s next for revenue cycle operations is transformation—leveraging IA to capture reimbursement left on the table, creating a more efficient process, and repositioning employees for more engaging work. A recent U.S. study on denials found that out of $3 trillion in total claims, $262 billion were denied, and more than half of those claims were never even appealed.[4] IA solutions can materially mitigate and reverse this leakage in ways a traditional workforce cannot, through root cause identification and process execution on a much larger, more efficient scale than the traditional workforce, directly resulting in improvements to efficiency and, ultimately, net patient service revenue (NPSR). Revenue cycle operations can no longer delay implementing an IA strategy.

To get started on this journey, revenue cycle leaders should develop their strategy, assess the impact, design the program, source the technology, resource the program, and systematically drive their IA platform to enable revenue cycle capabilities that address current and future revenue cycle challenges, which will better fuel their organization to continue providing the services their communities need.

© 2023 The Chartis Group, LLC. All rights reserved. This content draws on the research and experience of Chartis consultants and other sources. It is for general information purposes only and should not be used as a substitute for consultation with professional advisors.

Next Intelligence

Key Takeaways for Revenue Cycle Leaders
  • IA implementation is far more complex than simply selecting technology.
  • Building a new workforce model and an operational program around the technology solution(s) is imperative.
  • IA should be a component of every modern revenue cycle executive's operational portfolio.
Our State of the Healthcare Workforce collection offers data and insights that bring into focus the immediate solutions and long-term requirements for turning the healthcare workforce into one that thrives.
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1 The Chartis Group, Survey of Health System Executives, released August 2021 as part of the HIMSS Research Trust,

Healthcare Information and Management Systems Society, Inc. (HIMSS) is a global advisor and thought leader supporting the transformation of the health ecosystem through information and technology.

The HIMSS Trust partnership is a consortium of leaders from across the healthcare and technology space who are collecting, analyzing, and reporting on in-depth, data-driven market intelligence. Insights gathered by the Trust will unveil trends and challenges to help the industry prepare and predict for the next 3 to 5 years.

2 The Chartis Group, Survey of Health System Executives, released August 2021

Everest Group Research, Enterprise RPA Adoption Pinnacle Model Assessment, 2018,

HFMA, Success in Proactive Denials Management and Prevention, Glen Reiner, May 2021,

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