Healthcare data projects tend to fail in the seams. The EHR exports one shape of patient data. The payer system expects another. Claims, labs, eligibility, pharmacy, provider directories, and consent records all move on different schedules. Then someone asks for an AI model. Before that happens, the data layer needs boring engineering discipline: mappings, lineage, access rules, terminology normalization, audit logs, and a service provider who has seen healthcare data break in production. This i