Prior authorization is not a healthcare technology problem. It is an ownership problem — the cleanest available case study of The Ownership Gap™ inside a regulated, cross-organizational workflow.
Every stage has a contributor. No stage has an outcome owner accountable to the patient. That is why prior authorization is simultaneously the most-automated and least-improved healthcare workflow.
Healthcare AI compounds outcomes only when it sits inside an outcome-based operating model. The Spirit Works AI work is built on exactly that principle.
Originally inspired by Donna's LinkedIn article. This is the canonical, expanded version on ownershipgapofficial.lovable.app. Read the original on LinkedIn ↗
“Every stage of prior authorization has a contributor. No stage has an owner accountable to the patient outcome. That is why the workflow has been automated for decades and improved for none of them.”
Why Prior Authorization Is The Canonical Case
Prior authorization crosses payer, provider, prescribing physician, clinical operations, revenue cycle, and patient. Every one of those functions has responsibility for a portion of the workflow. None of them is accountable for the patient outcome that the workflow is supposed to produce.
The result is a workflow that is simultaneously the most automated and least improved in healthcare. Approval time, denial rate, appeal load, and administrative burden have all been targeted for decades. None of them have moved meaningfully — because the workflow was engineered around administrative accountability, not patient outcome ownership.
The Ownership Gap™ In Healthcare Form
Approval time is owned partially by payer, partially by provider, partially by the physician's office, partially by revenue cycle. Denial rate is a payer metric that provider operations bear the cost of. Appeal load is a provider metric that payer operations produce the volume of. Administrative burden lands on the workforce that has the least authority to change the workflow.
Every metric has a contributor. No metric has an owner. That is The Ownership Gap™, expressed inside the most consequential workflow in American healthcare.
Payer, provider, physician, clinical ops, revenue cycle, patient — six accountable parties, zero outcome owners. The patient experiences the seam. The workforce absorbs the friction. The system pays the cost.
Why Healthcare AI Keeps Underperforming
AI is being deployed across every stage of prior authorization — intake, eligibility, documentation, submission, adjudication, appeal. Every deployment moves the local metric. Very few deployments move the patient outcome.
The reason is structural. AI compresses the cycle time of the stage it sits inside. It does not create the outcome owner the workflow never had. Without that owner, faster prior authorization is still fragmented prior authorization — just faster.
What Outcome Ownership Looks Like Across Payer And Provider Lines
Outcome ownership in prior authorization requires assigning a single accountable owner for the patient outcome — approval, treatment access, care continuity — with authority to govern the workflow across payer and provider lines.
That is not a role most healthcare organizations currently have. It is the role the AI-era healthcare operating model actually requires. It is also the role that closes The Ownership Gap™ inside prior authorization specifically and healthcare operations broadly.
The Spirit Works AI Thesis
The Spirit Works AI work is built on exactly this principle. AI-powered workflow intelligence inside an outcome-based operating model — designed to close the ownership gap in prior authorization, not to accelerate the existing fragmentation.
That is the operating model healthcare AI actually needs. And it is the operating model the next decade of healthcare transformation will be built on.
- Prior authorization is an ownership problem, not a technology problem.
- Every stage has a contributor. No stage has a patient-outcome owner.
- AI compresses cycle time — it does not create outcome ownership.
- Outcome ownership across payer and provider lines is the unlock.
- Spirit Works AI is built on AI-powered workflow intelligence inside an outcome-based operating model.
Why This Matters
Healthcare AI leadership requires more than model expertise — it requires operating-model expertise across payer, provider, workforce, and regulatory lines. Donna's current work at Spirit Works AI, combined with her enterprise operations background at Aetna, Humana, and Blue Cross Blue Shield, is exactly that profile.
Interested in how Operational Intelligence™, Product Marketing, and AI can drive measurable business outcomes?
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