Help & Guides

Healthcare data quality

Bad data in healthcare isn't an IT problem — it's a patient safety problem. Duplicate records, missing allergies, incorrect medications, and stale demographics all create clinical risk.

Common challenges

What teams struggle with

Accumulated data debt

Years of manual entry, system migrations, and missing validation create messy datasets.

No data ownership

Nobody is responsible for data quality — it's everyone's problem and nobody's job.

Coding inconsistencies

Diagnosis codes, medication codes, and provider identifiers used inconsistently.

Quality vs usability tradeoff

Strict validation blocks workflows; loose validation allows bad data.

How we help

Practical solutions that ship

Data quality assessment

Quantify the problem — duplicate rates, missing fields, coding accuracy.

Cleansing and deduplication

IHI-based matching, merge workflows, and field standardisation.

Validation at entry

Real-time checks during registration, prescribing, and data entry.

Ongoing governance

Data quality metrics, ownership, and continuous improvement processes.

Frequently asked

Questions about healthcare data quality

How bad is our data?

We run a data quality assessment that quantifies duplicates, completeness, and accuracy — so you know the scope before committing to cleanup.

Need help with this?

Tell us where you're stuck. We'll give you an honest assessment — no sales pitch, just healthcare technology expertise.