CEOs, CFOs, COOs, RCM leaders, payer/provider operators, and healthcare AI founders
AI Solutions Design and RCM Orchestration
AI solution design for healthcare revenue cycle, prior authorization and claims intelligence, with defined responsibilities across payer and provider workflows.
Read the operating briefFrom workflow signal to reviewed action
- Intake the job
Select a bounded RCM or access task and name its owner.
- Assemble context
Gather payer rules, records, evidence packets, exceptions, and approvals.
- Bound agent work
Specify where AI may draft, score, route, or monitor.
- Review and act
Keep consequential decisions with accountable humans before system action.
- Measure operating value
Track cycle time, clean claims, appeals, access speed, and staff capacity.
The buyer problem
Design the workflow before adding automation.
I design revenue-cycle automation around the point at which a task becomes an accepted decision. Source evidence, payer rules, authorized action and exception ownership determine whether the system resolves work or merely moves it. I would compare a bounded automation with process repair and additional review capacity before selecting the technology.
The organization is surrounded by AI point solutions, but the work still lives across payer rules, portals, EHR data, CRM, RCM queues, documentation, humans, and exceptions. Without a design layer, automation can create more fragmentation.
What gets built
The deliverables and the handoff.
The mandate produces a workflow and control design, an implementation sequence and a financial evaluation. The cost model includes integration, routine review, difficult exceptions and corrections; time released, accelerated collections and incremental revenue remain separate value categories.
- 01
Solution architecture across signal intake, context assembly, agentic work blocks, human review, system action, and proof loops.
- 02
RCM and denial-prevention design patterns for eligibility, documentation, coding review, authorization packets, appeals, and underpayment triage.
- 03
Prior authorization orchestration model for APIs, portals, payer rules, evidence packets, status monitoring, and escalation.
- 04
Governance model that defines where AI can recommend, draft, route, monitor, or score, and where human approval is required.
- 05
Measurement plan covering cycle time, clean-claim rate, denial prevention, appeal yield, A/R days, access speed, and staff capacity.
Proof patterns
What leadership should be able to observe.
- 01
Claims and referral leakage surfaced through claims forensics.
- 02
AI-assisted RevOps patterns across lifecycle architecture, scoring, attribution, and revenue-quality reporting.
- 03
Payer/VBC and provider-network operating fluency across reimbursement, workflow, access, and value proof.
Decision questions
What the executive room must answer.
- 01
Which revenue-cycle or access workflow is costly enough and bounded enough to design first?
- 02
What evidence sources, rules, exceptions, and approvals must be assembled before action?
- 03
Where can AI safely draft, score, route, or monitor without replacing human judgment?
- 04
Which proof metric would make the CFO, COO, or founder believe the system is working?
Trust boundary
Conditions for responsible delivery.
- Do not start with a model demo before mapping work ownership and evidence sources.
- Do not let agents make autonomous clinical, referral, payer, or patient-impact decisions without human review.
- Do not measure AI value in prompts, documents generated, or dashboard views alone.
Connected context
Follow the system beyond this mandate.
Discuss the mandate