The rounding error

I was looking at drug-interaction alerts from a pharmacy. Many required a pharmacist to stop, read and acknowledge them. Duplicate therapy, real interactions and real dosing problems were the reason the system existed. But some apparently ordinary prescriptions produced avoidable messages. I read the records to understand why.

The cause involved how a value was represented and rounded between systems. The arithmetic was technically repeatable but changed the meaning of the source information. That distinction mattered: deleting the alert category could hide useful warnings, while correcting the representation addressed the source of the noise. I investigated the data model because the clinical meaning and the arithmetic had to agree.

The cause involved how a value was represented and rounded between systems. The arithmetic was technically repeatable but changed the meaning of the source information. That distinction mattered: deleting the alert category could hide useful warnings, while correcting the representation addressed the source of the noise. I investigated the data model because the clinical meaning and the arithmetic had to agree.

I was looking at drug-interaction alerts from a pharmacy. Many required a pharmacist to stop, read and acknowledge them. Duplicate therapy, real interactions and real dosing problems were the reason the system existed. But some apparently ordinary prescriptions produced avoidable messages. I read the records to understand why.

The cause involved how a value was represented and rounded between systems. The arithmetic was technically repeatable but changed the meaning of the source information. That distinction mattered: deleting the alert category could hide useful warnings, while correcting the representation addressed the source of the noise. I investigated the data model because the clinical meaning and the arithmetic had to agree.

The cause involved how a value was represented and rounded between systems. The arithmetic was technically repeatable but changed the meaning of the source information. That distinction mattered: deleting the alert category could hide useful warnings, while correcting the representation addressed the source of the noise. I investigated the data model because the clinical meaning and the arithmetic had to agree.

I am a pharmacist by training. I am also, for the last seventeen years, the person in the room whose job is to make the system work for the people inside it. That combination is why I could investigate the data model and understand what would be lost if we simply removed a category instead of fixing its cause.