Every revenue cycle vendor now has an AI story. Most of them are true in a narrow sense and misleading in a practical one. These twelve questions separate a model that will change your denial rate from one that will change your slide deck.
On the model
What data was it trained on, and does any of it come from organizations like ours? How is accuracy measured, per task and per payer, and can we see the confusion matrix? What is the confidence threshold for autonomous action, and who set it? How often is the model retrained, and what triggers it?
On supervision
Can every recommendation run in review mode before it runs autonomously? Can autonomy be granted per task, per payer, per confidence band, and revoked the same way? Does each recommendation show its reasoning and its evidence, in language a coder or an analyst can check? Who approved the last hundred autonomous actions, and can we audit them?
On failure
What happens when the model is wrong: who finds out, how fast, and what is the rollback? What is the error rate on the tasks you want to automate first, and what did those errors cost the last client? Will you price part of the engagement on the outcome, not the license? And finally: can we speak to a client who turned a feature off, and why?
A vendor with good answers to all twelve is rare. A vendor with bad answers to the last four should not be allowed near your claims.