1:15Executive visibility
PIQI Healthcare Data Quality
Roll the same quality evidence into facility trends and leadership visibility.
Watch the demo →Demo environment This recording uses synthetic fixture data and demonstrates current product behavior; it does not represent a live customer deployment.
A governed path from imperfect feeds to reusable knowledge.
Clinical data normalization becomes risky when a system silently invents meaning. Health-Connect separates predictable, testable repairs from decisions that require clinical or terminology expertise.
Integration engineers can dry-run a ruleset and inspect every before-and-after field. Records that cross the PIQI gate move to a review queue instead of disappearing. When a subject-matter expert approves a local-code mapping, that decision becomes a reusable FHIR ConceptMap for future messages.
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Transcript
Every EMR sends flawed data—missing genders, local codes, and OID identifiers. Health-Connect repairs these predictable defects right after parsing, with facility-scoped normalization rulesets.
In the workbench, the integration engineer tests a ruleset against a real resource before publishing anything. Nothing is written during a dry run.
Each rule hit shows the field before and after, and the PIQI quality score moves—so the quality lift is measured, not assumed.
Records the quality gate holds land in a clinical review queue. Nothing is silently dropped. A subject-matter expert releases what is safe and discards what is not, and every decision is audited.
When a mapping rule meets a local code with no ConceptMap entry, it flags it instead of guessing. The expert authors the mapping once, and every future message translates automatically.
The mapping is written to the facility ConceptMap in the FHIR server and the flag resolves—local lab code to LOINC, permanently.
Workbench-authored rules plus expert-curated mappings mean each facility's feed gets cleaner over time—and every downstream measure, agent, and dashboard benefits.
