Trustworthy AI Starts With Grain, Context And Permissions

​Sharan Senthilvasan is director, Data Platform Engineering at ModMed.

As the director of data platforms for a $5.6 billion SaaS company selling EHR solutions to doctors and patients, I’ve seen firsthand how important—and how challenging—it is to build a data foundation for AI-enabled workflows.​

Like most companies, we have data spread across multiple systems. A data platform brings those sources together and transforms the information for reporting and other uses. But just putting data in one place doesn’t make it trustworthy. The platform needs rules that ensure the same question produces the same answer every time, regardless of who asks it or how the original data was structured.​

Unify Meaning First

The first instinct companies often have is to pull data from different systems and put it all in one place: a data lake. That gives you unified storage, but you still need shared definitions, documented calculations, ownership, access rules and consistent ways to transform the data.​

One billing system might store a claim as a single record, while another breaks it into service lines, so one table has one record per claim and another can have 10. Joining the tables without accounting for that difference can give incorrect financial numbers even if every record is correct.​

A trustworthy platform defines how tables relate and makes those rules reusable. Technical teams need consistent design patterns, while nontechnical users should be able to easily understand a table through a catalog or interface.​

The platform must also protect the data, and when information is subject to HIPAA, those requirements need to be built into the architecture, whether it’s a person or an AI model using it. A platform can provide one environment containing protected information and another in which identifying details are masked. But changing a patient’s name is not enough if claims, insurance records and diagnoses aren’t connected correctly.​