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.
Related guides
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