Applying AI to healthcare operations — prior authorization, administrative workflows, workflow intelligence, and human + AI operating models — grounded in payer, provider, and med-tech experience.
Administrative complexity — not clinical judgment — is where the largest AI leverage exists today. Prior authorization, benefits verification, document intake, coding support, status communication, and denial rework are all workflows where AI plus redesigned human roles can meaningfully reduce cycle time, cost, and clinician burden.
The failure mode in healthcare AI is bolting a model onto a broken workflow. The alternative is designing the workflow with AI in it from the start — with clear human decision points, escalation paths, governance controls, and measurement of the operational and clinical outcomes the workflow exists to produce.
Donna's healthcare perspective is built from payer, provider, and med-tech experience with Blue Cross Blue Shield, Aetna/CVS Health, Humana, and Medtronic. Her perspective on prior authorization and accountable automation is explored in the LinkedIn article above.
Healthcare AI is the application of machine learning, natural language processing, and workflow intelligence to clinical, administrative, and operational processes in healthcare — with the goal of improving patient outcomes, reducing administrative burden, and lowering total cost of care.
AI reduces healthcare administrative work by automating document intake, coding, prior authorization drafting, benefits verification, and status updates — and by giving humans better decisions at the top of their workflow instead of asking them to hunt for information across systems.
Healthcare workflow automation is the redesign of clinical and administrative workflows so that AI, RPA, and integrated systems handle repeatable tasks, while clinicians and reviewers spend their time on judgment, exceptions, and patient interaction.
Start with the outcome — throughput, denial rate, turnaround time, member experience — and design the human + AI workflow backward from it. Define ownership, governance, and measurement before selecting tools. Treat AI as an operating-model change, not a point solution.
AI readiness in healthcare means data access and quality, workflow clarity, governance, clinical and compliance guardrails, and ownership for the operational and clinical outcomes AI is expected to influence. Regulatory and PHI requirements make governance non-optional.