Data Quality Programme for a Healthcare Network — Enabling AI Diagnostics on Clean Data

"We were blaming the AI models for underperforming when the real problem was the data we were feeding them. Celumai fixed the root cause instead of patching the symptoms."

— Chief Medical Information Officer, Multi-Hospital Healthcare Network

Eight years of patient records across 12 hospitals contained inconsistent field formats, duplicate patient identities, and missing values on 34% of clinically significant fields. Three AI diagnostic projects had been paused because model accuracy was unacceptably low — traced back to training data quality.

We deployed Informatica Data Quality to profile, cleanse, and standardise patient records. Match rules identified 28,000 duplicate patient identities that were merged under a single patient master. Standardisation rules were applied to diagnosis codes, medication names, and clinical measurements. Ongoing DQ rules now prevent new data quality issues at the point of entry.

AI diagnostic model accuracy improved from 67% to 91% after retraining on cleaned data. Two previously paused AI projects were resumed. 28,000 duplicate patient records eliminated, reducing clinical risk.

Is this familiar?

Informatica challenges in Healthcare — what we see most often

Businesses in Healthcare running Informatica often face the same underlying issues: data quality problems that compound over time, automation configurations that worked at launch but have not kept pace with the business, and adoption gaps that mean the CRM captures only a fraction of what it should.

The challenge is rarely the platform itself. Informatica is capable of handling this complexity. The challenge is the implementation — the data model decisions, the integration architecture, and the process design that determine whether the platform works for the business or against it.

Celumai specialises in Informatica implementations and rescues for Healthcare businesses. If the issues described in this case study are familiar, the causes and solutions are likely similar.