Hospitals are increasingly examining how AI can identify payment delays, claim denials and reimbursement gaps before they affect revenue. Recent 2026 research suggests that healthcare finance teams are moving beyond reporting past losses towards using predictive intelligence to prioritise claims, detect billing errors and improve financial decisions.
A patient undergoes treatment at a hospital, the medical team completes its documentation and the billing department submits an insurance claim. Weeks later, the insurer rejects the payment because of an incorrect code, missing information or an authorisation issue.
The hospital has already provided the treatment. Now, its finance team must investigate the rejection, correct the information and begin another round of communication with the insurer.
For healthcare providers, such situations can delay payments, increase administrative work and sometimes result in revenue that cannot be recovered.
This is where artificial intelligence is beginning to change how hospitals manage their finances.
Instead of identifying problems only after a claim has been rejected, AI systems can examine historical claims, billing records, payer requirements and patient information to identify potential errors earlier.
The technology can also help finance teams detect underpayments, identify changes in insurance company behaviour and prioritise accounts that need immediate attention.
The development is part of a wider shift in healthcare revenue cycle management, commonly known as RCM, which covers the financial processes from patient registration and treatment documentation to billing and payment collection.
Recent research indicates that hospitals are increasingly considering AI for these processes, although actual implementation remains uneven.
Experian Health’s October 2026 State of Claims survey, involving 204 healthcare professionals, found that 75% reported an increase in claim denials. Forty per cent said at least one in ten claims was being denied.
The survey also found that 89% considered reducing denials an organisational priority, while 84% believed AI could improve claims management in some capacity.
The findings reflect the experiences of surveyed US healthcare professionals rather than the entire industry. Nevertheless, they illustrate the financial pressures encouraging hospitals to explore predictive technologies.
Can AI Stop a Claim From Being Rejected?
Traditionally, many healthcare billing processes have focused on correcting errors after they occur.
When a claim is denied, employees review the reason, examine supporting documents and determine whether to resubmit or appeal.
AI introduces the possibility of examining potential problems before submission.
By analysing previous denials, payer requirements and claim information, predictive systems can identify patterns associated with payment rejection.
For example, a hospital submitting hundreds of outpatient claims could use AI to flag those with missing authorisations, inconsistent coding or information that previously resulted in denials from a particular insurer.
Employees could then review those claims before sending them for reimbursement.
This does not guarantee that a claim will be approved. However, it can help hospitals direct attention towards potential errors before they become more expensive to resolve.
Mindy Fortson, Chief Operating Officer at Experian Health, described the importance of identifying problems earlier.
“Every denial means more staff time spent reworking claims, longer reimbursement cycles and revenue that may never be recovered,” she said.
According to Fortson, accurate information and appropriate technology can help organisations identify issues before claims are submitted.
A 2026 Inovalon survey involving more than 400 revenue-cycle leaders also highlighted the importance of earlier intervention.
Its research identified front-end administrative activities, including insurance eligibility verification and prior authorisation, as significant sources of avoidable reimbursement disruption.
Another Inovalon study found that only 27% of hospital respondents reported implementing AI in their revenue-cycle software.
Adoption was considerably lower among ambulatory and post-acute care organisations, at 8% and 4%, respectively.
The findings suggest that although interest in AI is increasing, many healthcare organisations have not yet integrated it into everyday financial operations.
They also highlight the distinction between having financial information and being able to act on it.
A monthly report showing that claim denials increased provides an account of past performance.
A system identifying particular claims at risk of rejection provides information that employees can use before a payment problem occurs.
From Billing Records to Revenue Intelligence
Hospitals generate substantial financial information throughout the patient journey.
This includes registration details, insurance coverage, treatment documentation, billing codes, contractual reimbursement rates and payment records.
However, much of this information is distributed across different systems and departments.
AI is increasingly being explored as a way to connect these records and identify patterns that may otherwise remain unnoticed.
Consider a hospital receiving payments from several insurance companies.
One insurer begins reimbursing a particular type of treatment at a lower rate than the amount specified in its agreement with the hospital.
Individual transactions may not attract immediate attention. But when thousands of payment records are examined together, an analytics system could identify a recurring discrepancy.
Finance employees can then investigate whether the issue involves a contractual interpretation, an incorrect payment or another billing problem.
Brian Kenah, Chief Technology Officer at EnableComp, discussed this challenge in an interview with Healthcare Finance News.
“Humans have been the glue between systems,” he said.
Kenah explained that AI could help extract information from contracts, fee schedules and payment documents that previously required substantial manual review.
This expands the application of revenue intelligence beyond predicting denials.
AI can also help identify underpayments, monitor reimbursement trends, prioritise accounts approaching filing deadlines and support payment reconciliation.
The pressure to improve these processes is reflected in recent industry research.
Guidehouse and the Healthcare Financial Management Association surveyed 191 healthcare provider executives in late 2025 for their 2026 Revenue Cycle Management Trends report.
Eighty-eight per cent ranked payer-related challenges among their three biggest revenue-cycle concerns.
Among health-system respondents, 81% reported increasing denials, 74% reported more prior-authorisation delays and 73% observed an increase in unclear denial explanations or underpayments.
The research suggests that hospitals are managing financial challenges that extend beyond individual billing mistakes.
Changes in payer policies, reimbursement practices and administrative requirements can affect multiple claims simultaneously.
Shannon Cameron, Chief Operating Officer of Revenue Cycle and Anesthesia Financial Solutions at Harvard Medical Faculty Physicians, described the importance of detecting such patterns earlier.
“Too many organizations still discover revenue problems after the money has already been lost,” she said.
Cameron explained that her organisation had shifted towards using data to identify changes in payer behaviour, coding gaps and denial patterns while there was still an opportunity to respond.
The emerging approach involves using revenue information not only for financial reporting but also for operational decisions.
AI Is Changing Which Financial Problems Get Attention First
Revenue-cycle automation is not entirely new.
Healthcare providers have long used software for eligibility verification, billing checks, payment processing and administrative reporting.
What AI increasingly adds is the ability to interpret information, identify exceptions and recommend which cases require attention.
Rather than reviewing thousands of claims through the same process, a finance team could receive a prioritised list of accounts based on potential financial impact, deadlines or previous reimbursement patterns.
A denied claim approaching an appeal deadline, for instance, may require more immediate attention than a routine payment already progressing through the system.
Similarly, an AI tool could identify documentation gaps before billing, reducing the need for subsequent corrections.
This approach does not eliminate employees from the revenue cycle. Instead, it can change the type of work they perform.
Staff may spend less time locating information and more time investigating unusual cases, resolving disputes and validating recommendations.
McKinsey’s 2025 Revenue Cycle Management Buyer Survey, published in April 2026, examined responses from 215 US healthcare delivery leaders.
It found that 51% identified AI and advanced technologies as priority investment areas, compared with 33% in its previous survey.
Denial management and appeals were the most frequently prioritised functions for advanced technology, cited by 57% of respondents.
Documentation and coding accuracy followed at 56%.
The findings indicate growing interest in technology that addresses specific financial challenges rather than simply automating administrative work.
However, expectations about returns have also become more cautious.
Only 8% of McKinsey’s respondents anticipated very high returns from automation within five years, while 42% expected high returns.
Financial constraints, difficulties integrating systems, employee training and organisational change remained barriers.
This is relevant because many claims about AI in healthcare finance focus on the possibility of largely autonomous revenue cycles.
In practice, implementation appears to be progressing through narrower applications.
Guidehouse and HFMA found that 59% of surveyed executives had not yet implemented AI or automation in revenue-cycle operations, although 42% were exploring such technologies.
At Nebraska Methodist Health System, CFO and Vice President Jeff Francis has described the use of coding optimisation before claims are billed as one application intended to improve payment timelines.
Such examples indicate that healthcare providers may not need to automate every financial process to benefit from better information.
Identifying a documentation problem, detecting an incorrect reimbursement or directing employees towards the right claim can each contribute to operational improvements.
Hospitals Still Need to Prove AI Is Delivering Results
Despite growing interest, an important question remains: can healthcare organisations demonstrate that AI is improving financial performance?
Deloitte’s 2026 Healthcare CFO Survey examined 64 US finance leaders, divided equally between health systems and health plans.
Forty-four per cent represented organisations classified as AI scalers, meaning they had deployed generative AI across a broader range of activities.
Yet only 18% of these organisations reported mature financial attribution capabilities, including consistently measuring AI’s effect on revenue growth or cost savings.
The findings suggest that organisations adopting AI extensively may still struggle to establish precisely how much financial value the technology creates.
This distinction matters in revenue-cycle management.
A system might identify hundreds of potentially problematic claims. However, the number of alerts generated does not establish whether the hospital has recovered more money or reduced administrative expenses.
Finance leaders need to assess whether the technology improves clean-claim rates, reduces avoidable denials, shortens reimbursement timelines or lowers the cost of collecting payments.
Accuracy and governance also remain important.
An incorrect prediction could lead employees to spend time reviewing claims that would otherwise have been approved.
Outdated payer information could produce unsuitable recommendations, while incomplete patient records may affect the reliability of automated decisions.
Hospitals must also consider patient data protection, compliance requirements and the need for human review when using sensitive financial and medical information.
Another challenge is the growing use of AI by insurance companies themselves.
HCA Healthcare CFO Mike Marks acknowledged this imbalance while discussing claims technology in 2026.
“We’re behind the payers, in my view,” he said.
The observation highlights how both sides of the reimbursement process are introducing more advanced technology.
Insurers may use automation to examine claims and payment requirements, while hospitals apply AI to improve documentation, identify denials and challenge reimbursement decisions.
Greater automation could improve processing efficiency, but it does not necessarily resolve disagreements between providers and payers.
The financial benefits will depend on how effectively technology is implemented and whether organisations can use its findings to make appropriate decisions.
For now, much of the available evidence concerns industry adoption, expectations and reported operational challenges rather than independently measured financial outcomes.
The emerging transformation is therefore not simply about processing bills faster.
Healthcare providers are beginning to use financial records to anticipate problems, investigate payment patterns and decide where employees should intervene.
A claim that might previously have been rejected can be reviewed earlier. An unexpected change in reimbursement can be detected across multiple transactions. A finance team can identify which accounts need attention instead of responding only after revenue has been delayed.
AI is gradually shifting healthcare revenue management from reporting financial problems towards identifying opportunities for action. Whether that translates into stronger hospital finances will depend on the accuracy of those insights, the decisions they support and the measurable results organisations can demonstrate.
The feature uses five 2026 research datasets from Experian Health, Inovalon, Guidehouse-HFMA, McKinsey and Deloitte, with four attributed industry quotations. The evidence primarily covers US healthcare providers, so its findings have not been generalised to India’s different hospital financing and insurance systems.
Disclaimer: All data points and statistics are attributed to published research studies and verified market research. All quotes are either sourced directly or attributed to public statements.
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