RPA, or robotic process automation, is software that handles repetitive, rules-based tasks on a computer, mimicking the clicks, keystrokes, and data entry a human worker would perform. In healthcare, it is primarily used to automate administrative work like billing, claims processing, appointment scheduling, and moving patient data between systems that don’t natively talk to each other. The global healthcare RPA market reached $2.27 billion in 2025 and is projected to nearly double to $4.52 billion by 2030, driven largely by the growing number of hospitals running electronic health records that generate enormous volumes of data-entry work.
How RPA Actually Works
Think of RPA as a macro on steroids. An Excel macro can repeat a series of steps inside a spreadsheet. An RPA bot does the same thing but across your entire computer: opening applications, logging into portals, copying data from one system, pasting it into another, clicking buttons, and filing records. It follows a strict set of “if-then” rules. If a claim is missing a diagnosis code, flag it. If a patient’s insurance is verified, move the record to the next step. The bot never improvises or makes judgment calls.
This makes RPA ideal for tasks that are high-volume, predictable, and boring. It also means the technology is relatively simple to set up compared to full artificial intelligence systems. Most RPA platforms are low-code or no-code, so health systems can configure bots without hiring a team of software engineers. The bots interact with existing software through the user interface, the same screens and fields a staff member would use, which means hospitals don’t need to rebuild their legacy systems to get started.
Where Healthcare Organizations Use It Most
The heaviest adoption is in revenue cycle management, the chain of steps from scheduling a patient to collecting final payment. A survey of health systems found the most common uses: claims status checking (40 percent of organizations), coding (28 percent), payment posting and reconciliation (28 percent), and prior authorization submissions (22 percent). These are tasks that involve pulling up a payer portal, entering patient details, waiting for a response, and recording the result, hundreds or thousands of times a day.
One pain treatment network started by automating claim submissions, then expanded to dozens of bots handling payment eligibility checks, write-offs, and data retrieval across its facilities. The pattern is common: organizations automate one bottleneck, see results, and scale from there.
Beyond billing, RPA handles patient-facing tasks as well. Bots can pre-register scheduled patients by pulling insurance details and verifying coverage before the appointment, helping health systems pre-register up to 95 percent of patients before they walk through the door. Automated appointment reminders reduce no-shows. Online self-scheduling, powered by bots that check provider availability in real time, removes the need for phone-based booking.
Connecting Systems That Don’t Talk to Each Other
One of healthcare’s persistent headaches is interoperability. A hospital might run one system for its electronic health records, another for its lab, and a third for billing, none of which share data automatically. Staff end up copying information by hand between screens, a slow process that introduces errors and contributes to burnout.
RPA bots bridge these gaps without requiring the underlying systems to be rebuilt. One documented case involved automating the pipeline between an EHR and a claims system, eliminating up to 250 hours per week of manual data-entry work. Another connected a laboratory information management system directly to payer portals so lab results could flow into billing without a human intermediary. These aren’t elegant integrations at the code level. They’re bots doing the same copying and pasting a person would, just faster and without mistakes.
Cost Savings and Efficiency Gains
The financial case for RPA in healthcare is well documented. Research from KPMG suggests automation can reduce revenue cycle costs by 25 to 40 percent for hospitals and health systems, with nearly double the productivity improvement compared to outsourcing those same tasks. One organization that used process mining to identify automation opportunities found it could cut claim processing costs by 74 percent.
McKinsey’s analysis of health insurance payers found that automation at scale can deliver average cost savings of up to 30 percent within five years. One payer increased its digital work intake by roughly 60 percent, reduced manual downstream activities significantly, and is approaching $30 million in annual administrative savings. That same organization cut adjudication problems related to provider data issues by about one-third.
At Baylor Scott & White Health, staff used to spend 5 to 7 minutes manually calculating cost estimates for patients, often with inaccurate results. After implementing RPA alongside AI tools, the system automated 70 percent of those estimates, saving time and reducing errors.
RPA vs. Artificial Intelligence
RPA and AI are often mentioned together, but they solve different problems. RPA handles structured, predictable work: enter this code, check this box, move this file. It follows rules and never deviates. About 60 percent of healthcare administrative tasks fit this description.
The remaining 40 percent involve unstructured data, context-dependent decisions, or complex relationships. Reading a physician’s handwritten notes, interpreting the meaning of a clinical narrative, or deciding whether an unusual claim should be approved: these require cognitive automation, which uses techniques like natural language processing, machine learning, and text analytics to mimic human judgment.
In practice, the two technologies increasingly work together. RPA might pull a document from an inbox and feed it to an AI model that reads and classifies it, then the RPA bot takes the AI’s output and enters it into the correct system. McKinsey estimates that 80 to 90 percent of claims adjudication is already automated through rules-based systems, but adding machine learning could push automatic adjudication rates past 95 percent. Similarly, prior authorization, currently only about 25 percent automated in the U.S., could reach 50 to 75 percent automation within five years as cognitive tools mature.
Privacy and Compliance Requirements
Any software that touches patient information in the U.S. falls under HIPAA’s Security Rule. RPA bots are no exception. Because bots access electronic health records, insurance portals, and billing systems, they interact with protected health information constantly.
Organizations deploying RPA need to ensure several safeguards are in place. Access controls must limit each bot to only the data it needs for its specific task, just as a human employee would have role-based permissions. Audit controls must log every action the bot takes so the organization can review what was accessed and when. Authentication mechanisms verify the bot’s identity before granting system access. Integrity controls prevent unauthorized changes to data, and transmission security protects information as it moves between systems over a network.
When a third-party vendor builds or hosts the RPA solution, the healthcare organization must have a business associate agreement in place, a written contract ensuring the vendor meets the same privacy and security standards required of the organization itself. Risk assessments should be performed before deployment, evaluating what could go wrong if a bot malfunctions, accesses the wrong record, or is compromised by a security breach.
What Drives Adoption
The biggest accelerant is the sheer volume of electronic records now flowing through healthcare. As more providers adopt EHR systems, the amount of data that needs to be entered, verified, transferred, and reconciled grows exponentially. Staff shortages compound the problem. Automating routine tasks lets existing employees focus on work that requires human judgment, like resolving complex billing disputes or communicating directly with patients.
The trends shaping the next few years include broader integration of RPA with EHR systems, expansion of automated scheduling and billing workflows, and a growing emphasis on intelligent automation that pairs rule-based bots with AI capabilities. Medical record review, currently less than 10 percent automated, could reach 50 percent automation as these hybrid approaches mature, freeing clinical staff from one of the most time-consuming parts of their workflow.