CFOs Turn to AI as Hospitals Face Rising Costs and Denials

The CFO Imperative: How Healthcare Finance Leaders Are Building AI-Ready Operations

Margins are thin. Costs keep climbing. The old playbook of across-the-board cuts won't fix structural problems in labor, supplies, and denials. CFOs are being asked to lead growth and resilience, not just close the books.

That shift is center stage at Innovaccer's virtual panel, "The CFO Imperative," moderated by CEO Abhinav Shashank. Financial leaders from Dignity Health (Carolynne Bordona), Atrium Health within Advocate Health (Jeff E. Francis), and Brooks Rehabilitation (Reid Oakes) will dig into how AI moves from buzzword to balance-sheet impact.

Why the urgency

• Labor remains expensive while supply prices stay elevated.
• Payer denials are more aggressive and time-consuming to overturn.
• Total hospital expenses rose about 5.5% in 2023, still outpacing revenue for many systems, per the American Hospital Association (AHA analysis).

In short: you need better foresight, fewer manual tasks, and tighter control of cash-flow levers.

From cost-cutters to growth partners

Finance leaders are being measured on strategic impact. That means linking clinical outcomes with financial performance, and building systems that predict issues before they hit the P&L. Surveys highlighted by Becker's Hospital Review show tech and automation rising to the top of CFO priorities.

Innovaccer's stance is clear: unify data across EHRs, claims, pharmacy, and supply chain, then apply AI to operational bottlenecks and revenue leakage. The company's "Sara for Healthcare" suite points AI at denial appeals, documentation gaps, and summarizing complex patient histories-work that drains budget and time. Fierce Healthcare has noted the side benefit: reduced administrative burden and less burnout (coverage).

Where AI creates near-term ROI

• Denial prevention: Score claims pre-submission, flag high-risk ones for review, and fix errors before they become write-offs.
• Clinical documentation improvement: Surface missing specificity and coding opportunities tied to reimbursement and risk adjustment.
• Appeals automation: Generate first drafts for denials with supporting evidence; route to experts for finalization.
• Throughput and access: Predict volumes, optimize schedules, and reduce no-shows to stabilize revenue and staffing.
• Supply chain: Forecast demand, right-size inventory, and avoid costly shortages or overstock.

The vendor landscape: EHR-embedded vs. data-agnostic

Epic and Oracle Health are pushing embedded AI-voice assistants, note automation, and predictive tools inside the EHR. Oracle has promoted its generative AI clinical assistant in recent announcements covered by outlets like SiliconANGLE. The financial upside is real: more physician time for patients and throughput, fewer clicks.

Data-agnostic platforms like Innovaccer compete by integrating sources across the enterprise, not just one EHR. The upside is a panoramic view; the tradeoff is heavier lift on data plumbing, governance, and change management.

Adoption hurdles to plan for

• Data quality and interoperability: Bad inputs kill trust and outcomes. Set owners, schemas, and SLAs early.
• Privacy and security: Bake in HIPAA controls, audit trails, access minimization, and vendor risk reviews.
• Model transparency and bias: Prefer explainable outputs for finance and compliance. Document model lineage and testing.
• Change management: Redesign workflows, not just tools. Train coders, rev cycle teams, and clinicians with role-specific playbooks.
• Unit economics: Tie each use case to a measurable outcome-clean claim rates, DNFB days, average denial overturn value, staff hours saved.

Your first 90 days: a practical plan

• Pick 2-3 high-yield use cases: pre-bill denial scoring, CDI nudges, and payer-specific appeal automation.
• Map data dependencies: EHR, clearinghouse, contract terms, charge master, and prior auth data.
• Define success metrics: preventable denial rate, net collection rate, days in A/R, coder productivity, appeal win rate.
• Run a limited pilot: One service line, one payer, clear before-and-after baselines.
• Stand up governance: CFO, CIO, CMO, compliance, and frontline leaders meet biweekly; publish a living issues log.
• Reinvest wins: Use recovered dollars and time to expand use cases and fund workforce development.

Metrics that matter

• Revenue cycle: First-pass yield, denial rate by category, average days to remit, DNFB days.
• Workforce: Overtime hours, agency spend, schedule accuracy vs. volumes, clinician admin time per patient.
• Supply chain: Stockouts avoided, inventory turns, price variance by category.
• Quality-finance link: Readmissions tied to penalty exposure, HCC accuracy, LOS vs. expected.

What to watch from the panel

Expect candid discussion from leaders at Dignity Health, Atrium Health, and Brooks Rehabilitation on where AI is delivering returns-and where it's falling short. Different settings, same pressures. The models work best when fed unified, clean data and embedded into daily workflows, not bolted on.

If you're planning your roadmap, borrow their sequence: stabilize revenue flow (denials, CDI), improve access and throughput, then expand to workforce and supply chain. Keep governance tight and metrics visible.

Bottom line

Healthcare finance is moving from retrospective control to proactive performance. AI won't fix fundamentals by itself, but paired with unified data and disciplined execution, it can reduce leakage, free up staff capacity, and support growth.

If you're upskilling finance and rev cycle teams on practical AI, explore curated resources that focus on tools and certifications relevant to the function (AI tools for finance).