Conifer integrates Google Cloud AI into revenue cycle workflows
Conifer Health Solutions, a Tenet Healthcare subsidiary, is building an AI platform on Google Cloud to plug directly into its revenue cycle management (RCM) operations. The goal: apply AI across patient access, insurance and eligibility, revenue integrity, and accounts receivable to improve speed, accuracy, and financial outcomes.
According to Conifer COO Deepali Narula, combining the company's RCM expertise with Google Cloud's AI aims to improve financial performance for clients and support a better patient experience.
What this means for management
For CFOs, COOs, and revenue leaders, this points to a more data-driven RCM stack where AI helps prioritize work, standardize decisions, and reduce manual churn. The near-term upside sits in fewer denials, faster cash, and lower cost to collect.
Where AI is likely to add value in RCM
- Patient access: Eligibility checks, benefits verification, and cost estimates surfaced earlier and with fewer errors.
- Insurance and eligibility: Automated verification and exception routing to cut downstream rework.
- Revenue integrity: Coding support, audit flags, and documentation checks to protect revenue.
- Accounts receivable: Denial prediction, worklist prioritization, and payment likelihood scoring to speed collections.
Market signals you should note
Conifer recently extended its multi-year RCM partnership with Dartmouth Health as the exclusive end-to-end provider. At a Google Cloud healthcare roundtable, leaders emphasized ROI and responsible deployment, and shared that 46% of organizations plan to allocate more than half of their future AI budget to AI agents, according to Aashima Gupta, Google Cloud's global director of healthcare strategy and solutions.
Workforce readiness is also getting attention. Adtalem Global Education launched an AI credential program with Google Cloud to give students and clinicians hands-on experience with the tools shaping clinical and operational work.
Action plan for RCM and finance leaders
- Define 2-3 focused use cases (e.g., denial prevention in top five DRGs, eligibility accuracy for high-volume payers).
- Start with a contained pilot tied to clear outcomes and a 90-day timeline.
- Instrument workflows end to end so AI outputs flow into existing systems and worklists.
- Stand up an AI review lane: human-in-the-loop for exceptions, audits for bias and drift, and rollback criteria.
- Create a payer-by-payer rules library and feed it with feedback loops from denials and appeals.
- Set a change management plan: training, job aids, and daily management huddles for new workflows.
- Lock down data governance: PHI handling, access controls, and vendor risk reviews.
- Refresh your business case quarterly with real results, not model accuracy alone.
Core metrics to track
- First-pass claim rate
- Denial rate (overall and by root cause)
- Cash acceleration (days in A/R, A/R > 90 days)
- Cost to collect (per dollar and per claim)
- Clean registration rate and eligibility verification accuracy
- Coder and collector productivity (units per FTE)
- Patient self-service adoption and NPS for billing
Risk and governance checklist
- Audit trails on every AI-assisted decision.
- PHI safeguards and data minimization by default.
- Clinical vs. financial boundaries: keep AI for RCM separate from clinical decision-making.
- Vendor SLAs on quality, uptime, and remediation.
- Model monitoring for drift, bias, and error hotspots.
Resources
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