Quality care is the mission. Finance protects the margin.
Quality care is the mission. Finance protects the margin — while revenue-side functions like coding, adjudication, and denials get faster and more complex through AI and agents, finance teams are still working from fragmented systems and weeks-old data. This post argues that Databricks Genie closes that gap as a governed, ontology-grounded AI coworker that answers the three core finance questions — cost-to-reimbursement gaps, denials/underpayments, and trapped cash — with every figure traced to source and correct, not just accurate, in business context.
* Healthcare finance is tasked with protecting margin, but it's running on fragmented systems and weeks-old data, even as the revenue-side functions it depends on (coding, adjudication, denials, collections) get faster and more complex through AI and agents. The result is higher-risk decisions made from a partial, dated picture of the business. * Databricks Genie acts as a governed, "data-smart AI coworker" grounded in an ontology that captures what the numbers mean — which payer, contract, and service line — and keeps that meaning current as the business changes. The distinction it draws: an answer can be accurate (right figure) but not correct (that figure in full business context), and Genie is built for correct. * It targets the three questions every finance team asks, like where care costs outrun reimbursement, where earned revenue slips to denials and underpayments, and where cash is trapped in receivables, with every figure traced to source and a human making the final call. Answered together, the three compound into one reinforcing mechanism where each recovery sets up the next.