How Automation and AI are Quietly Rebuilding the Hospital Back Office

The following is a guest article by Paige Schwein, Vice President, Technology Services at Healthrise

Ask most people where AI is changing healthcare, and they’ll describe a surgical robot or an algorithm that reads scans faster than a radiologist. But after spending more than a decade in revenue cycle operations, the real revolution unfolding quietly in the administrative back office.

Health systems and hospitals that embed intelligent automation into their day-to-day operations can reduce the administrative burden created by endless claim queues, denial worklists, and prior authorization backlogs.

There is a compelling business case for this pivot. Administrative work consumes 15% to 30% of U.S. healthcare spending, and most hospitals operate on margins between 1% and 3%, leaving virtually no room for avoidable waste. First-pass denial rates still top 10% nationally, even at organizations that have spent heavily on billing software and staff.

The 2025 CAQH Index estimates that automation saved the industry $258 billion in administrative costs in 2024 and puts the savings still on the table at another $21 billion a year.

That makes the hospital back office one of the clearest opportunities for AI adoption: it is data-rich, highly repetitive, and directly connected to a health system’s financial performance. The next phase of healthcare AI isn’t about adding more tools; it’s about embedding intelligence into the work hospitals already do. Most AI efforts began as isolated pilots targeting a single payer or denial category. Now, the opportunity is to move beyond those pilots and integrate automation directly into daily worklists, embedding flagged claims into existing queues so staff don’t have to check separate dashboards.

What AI Tools Actually Do

For years, denial management meant someone exporting claims data into a spreadsheet and piecing together patterns long after the revenue had been written off. Predictive models with embedded large language models (LLMs) now read through that data and surface the trends a person might miss. Health systems using them have cut incoming denials by an average of 25%.

The same historical data (billing history, seasonal swings in patient volume, and the habits of individual payers) also lets revenue cycle leaders forecast cash flow and identify where to focus resources before a shortfall arrives.

Coding and audit teams work differently now as well. Random sampling used to decide which accounts got reviewed, so the costliest errors often went unexamined. Automated selection sends reviewers to the highest-risk accounts first. Systems using standardized audit-coach-measure workflows have seen underperforming team members progress to consistent top performers in fewer than 90 days.

Together, these tools shift revenue cycle work from reacting to problems after they happen to identifying where attention is needed before those problems become costly. The technology handles the volume and pattern recognition, while revenue cycle teams can spend more time interpreting what the data means, deciding what action to take, and improving the processes behind it.

Confronting Bureaucratic Hurdles

New federal mandates require health plans to enforce strict prior authorization turnarounds — 72 hours for urgent cases and seven days for standard requests — before moving to standardized electronic submissions by 2027.

Physicians and their staff already spend about 13 hours a week on prior authorization. Keeping pace with those deadlines without adding headcount or exhausting the team you have requires automation that connects clinical documentation to the billing system, backed by a denial prevention program that catches problems before a claim goes out.

Before Adding One More Tool

A typical revenue cycle department already juggles an EHR, a clearinghouse, a claims scrubber, a denial tracker, and multiple point solutions. Another AI tool can easily become one more place for work to hide.

Before introducing new technology, healthcare leaders should confirm it can embed into existing workflows, assign clear ownership for flagged issues, and avoid duplicating current functionality. To track progress and prove it works, it’s important to establish concrete baseline metrics before the tool goes live, such as first-pass denial rates, days in A/R, and authorization turnaround times.

The goal shouldn’t be another dashboard. It should be a better workflow.

Healthcare Still Needs a Human Touch

Physicians have good reason to be wary. Six in ten say they worry payers will use AI to deny more claims, and nothing in the technology itself prevents that. A model that flags a questionable claim or an audit risk has done half the job. The other half belongs to a trained specialist who reviews the flag, understands the context around it, and has the authority to correct what’s wrong.

Governance decides whether these tools make the work easier for the people doing it or harder, and that requires clear ownership, visible oversight and a defined process for acting on what the software finds.

When those pieces are missing, automation just makes a broken process run faster. When they’re in place, the billing office finally catches up to its own workload. For a hospital operating on a 2% margin, the claims it stops losing can matter as much as anything new in the operating room.

About Paige Schwein

Paige Schwein is Vice President of Technology Services at Healthrise, where she leads the technology behind the company’s revenue cycle work for health systems. She has spent more than a decade in revenue cycle operations and builds teams that use data to improve efficiency and financial performance.

The following is a guest article by Paige Schwein, Vice President, Technology Services at Healthrise

Ask most people where AI is changing healthcare, and they’ll describe a surgical robot or an algorithm that reads scans faster than a radiologist. But after spending more than a decade in revenue cycle operations, the real revolution unfolding quietly in the administrative back office.

Health systems and hospitals that embed intelligent automation into their day-to-day operations can reduce the administrative burden created by endless claim queues, denial worklists, and prior authorization backlogs.

There is a compelling business case for this pivot. Administrative work consumes 15% to 30% of U.S. healthcare spending, and most hospitals operate on margins between 1% and 3%, leaving virtually no room for avoidable waste. First-pass denial rates still top 10% nationally, even at organizations that have spent heavily on billing software and staff.

The 2025 CAQH Index estimates that automation saved the industry $258 billion in administrative costs in 2024 and puts the savings still on the table at another $21 billion a year.

That makes the hospital back office one of the clearest opportunities for AI adoption: it is data-rich, highly repetitive, and directly connected to a health system’s financial performance. The next phase of healthcare AI isn’t about adding more tools; it’s about embedding intelligence into the work hospitals already do. Most AI efforts began as isolated pilots targeting a single payer or denial category. Now, the opportunity is to move beyond those pilots and integrate automation directly into daily worklists, embedding flagged claims into existing queues so staff don’t have to check separate dashboards.

What AI Tools Actually Do

For years, denial management meant someone exporting claims data into a spreadsheet and piecing together patterns long after the revenue had been written off. Predictive models with embedded large language models (LLMs) now read through that data and surface the trends a person might miss. Health systems using them have cut incoming denials by an average of 25%.

The same historical data (billing history, seasonal swings in patient volume, and the habits of individual payers) also lets revenue cycle leaders forecast cash flow and identify where to focus resources before a shortfall arrives.

Coding and audit teams work differently now as well. Random sampling used to decide which accounts got reviewed, so the costliest errors often went unexamined. Automated selection sends reviewers to the highest-risk accounts first. Systems using standardized audit-coach-measure workflows have seen underperforming team members progress to consistent top performers in fewer than 90 days.

Together, these tools shift revenue cycle work from reacting to problems after they happen to identifying where attention is needed before those problems become costly. The technology handles the volume and pattern recognition, while revenue cycle teams can spend more time interpreting what the data means, deciding what action to take, and improving the processes behind it.

Confronting Bureaucratic Hurdles

New federal mandates require health plans to enforce strict prior authorization turnarounds — 72 hours for urgent cases and seven days for standard requests — before moving to standardized electronic submissions by 2027.

Physicians and their staff already spend about 13 hours a week on prior authorization. Keeping pace with those deadlines without adding headcount or exhausting the team you have requires automation that connects clinical documentation to the billing system, backed by a denial prevention program that catches problems before a claim goes out.

Before Adding One More Tool

A typical revenue cycle department already juggles an EHR, a clearinghouse, a claims scrubber, a denial tracker, and multiple point solutions. Another AI tool can easily become one more place for work to hide.

Before introducing new technology, healthcare leaders should confirm it can embed into existing workflows, assign clear ownership for flagged issues, and avoid duplicating current functionality. To track progress and prove it works, it’s important to establish concrete baseline metrics before the tool goes live, such as first-pass denial rates, days in A/R, and authorization turnaround times.

The goal shouldn’t be another dashboard. It should be a better workflow.

Healthcare Still Needs a Human Touch

Physicians have good reason to be wary. Six in ten say they worry payers will use AI to deny more claims, and nothing in the technology itself prevents that. A model that flags a questionable claim or an audit risk has done half the job. The other half belongs to a trained specialist who reviews the flag, understands the context around it, and has the authority to correct what’s wrong.

Governance decides whether these tools make the work easier for the people doing it or harder, and that requires clear ownership, visible oversight and a defined process for acting on what the software finds.

When those pieces are missing, automation just makes a broken process run faster. When they’re in place, the billing office finally catches up to its own workload. For a hospital operating on a 2% margin, the claims it stops losing can matter as much as anything new in the operating room.

About Paige Schwein

Paige Schwein is Vice President of Technology Services at Healthrise, where she leads the technology behind the company’s revenue cycle work for health systems. She has spent more than a decade in revenue cycle operations and builds teams that use data to improve efficiency and financial performance.