Clean, Standardized, Interoperable Data: The AI Advantage Healthcare Can Trust
Derek Plansky, senior vice president of strategic governance at Health Gorilla (a QHIN), has a simple message for clinical and IT leaders: AI works only as well as your data. In healthcare, that bar is high-and unforgiving.
Data quality issues have hampered analytics for years. Now, with AI in clinical workflows, bad data doesn't just slow teams down-it can mislead care and damage trust. A recent Experian survey put confidence in healthcare data quality at 7.08/10, with duplicative work, incorrect patient details and missed appointments topping the list of concerns.
What poor data quality costs you
- Clinical risk: Incomplete, biased or outdated records skew algorithms and produce unsafe recommendations. That erodes clinician trust and can lead to missed or wrong diagnoses.
- Operational drag: Fragmented records, identity mismatches and inconsistent coding increase errors, retests and manual rework across the enterprise.
- Compliance exposure: Meeting expectations tied to TEFCA, HIPAA and FDA oversight becomes harder when data is inconsistent or unverifiable. Transparency and auditability take a hit. Learn about TEFCA.
- Financial leakage: Denied claims from coding inaccuracies, costly clean-up cycles and delayed AI deployments add up-especially as more workflows depend on data readiness.
What clean, standardized, interoperable data enables
Standardized, normalized and deduplicated data lets AI deliver reliable insights at the point of care. Clinicians get signals they can act on with confidence.
- Safer, faster decisions: Consistent inputs improve CDS, reduce unnecessary testing and align care plans.
- Predictive care: Longitudinal, interoperable records make risk stratification and early intervention practical, not theoretical.
- Scalable programs: Population health, public health monitoring and research require normalized datasets to find patterns across millions of encounters-so pilots don't stall.
- Ecosystem coordination: Clean data stitched across EHRs, labs and payers supports smoother transitions, fewer gaps and measurable cost savings.
Governance: the guardrails that make AI safe and sustainable
Governance isn't busywork. It's how you ensure every AI use case is fed consistent, compliant, context-rich data. Plansky's stance: interoperability only delivers value when every participant plays by the same rules.
- Clear ownership: Assign data product owners and stewards with decision rights over definitions, changes and quality thresholds.
- Data contracts: Define schemas, code sets, provenance, and service levels at the interface-not just in a policy document.
- Quality gates: Validate in real time (syntax, vocabulary, identity, units, dates) and in batch (completeness, outliers, drift).
- Access and audit: Enforce least privilege, consent, and trace lineage from source to model output for every record used.
- Model oversight: Establish preflight checks for training and inference datasets, bias monitoring and periodic revalidation.
What "clean data" should mean: adopt standardized frameworks
"Clean" needs to be defined, testable and enforced. Standardized frameworks set the rules for formatting, validation and maintenance so every system speaks the same language.
- Structure and vocabularies: HL7 FHIR, USCDI, LOINC, SNOMED CT, RxNorm, ICD-10-CM and CPT for consistent representation.
- Normalization: Units, reference ranges, code mapping coverage and value set conformance are non-negotiable.
- Deduplication and survivorship: Define how duplicates are detected and which source wins attribute-by-attribute.
- Identity resolution: Patient matching via MPI plus referential methods; consistent use of NPI and organizational identifiers.
- Timeliness and provenance: Ingest, refresh and latency SLAs; include source, acquisition time and transformation steps.
- Interoperability policy: Align to QHIN/TEFCA data use, consent and exchange requirements to keep sharing predictable and enforceable.
The practical playbook for health systems and payers
- 1) Baseline your data: Profile duplicate rates, code mapping coverage, unit consistency, identity match performance and missingness by domain.
- 2) Define "clean" by domain: For problems, meds, labs, imaging and claims, publish rule sets and acceptable thresholds.
- 3) Stand up a governance council: Clinical, quality, privacy, security and data engineering leaders meet monthly to approve standards and changes.
- 4) Implement quality gates: Put validators at ingestion and exchange; fail fast with clear error messages and remediation paths.
- 5) Instrument everything: Dashboards for data quality KPIs; alerts for drift or code mismatches; audit trails end-to-end.
- 6) Create model readiness checks: No dataset enters training or inference without documenting lineage, conformity, bias tests and constraints.
- 7) Close the loop with clinicians: Embed feedback in the EHR for wrong meds, allergies, or problem codes; route issues to stewards.
- 8) Vendor accountability: Bake data contracts and SLAs into agreements; require terminology updates and conformance reports.
KPIs that keep you honest
- Duplicate patient rate (% and trend)
- LOINC mapping coverage for labs (%)
- SNOMED coverage for problem lists (%)
- Claim denial rate attributable to coding
- Allergy and medication reconciliation completeness
- Time to resolve data quality incidents
- Model audit findings tied to data issues
Where QHINs fit
QHINs exist to make cross-network exchange predictable with shared rules, identity, security and auditing. That common framework reduces variability so data arriving from outside your walls is usable on day one. Plansky's perspective: interoperability delivers its full value only when governance and data standards are enforced consistently across EHRs, labs and payers.
Bottom line
AI will not fix bad data. Clean, standardized and interoperable data-managed under clear governance-lets AI earn clinician trust, reduce waste and improve outcomes. Set the rules, measure relentlessly, and make conformance the default across your ecosystem.
Helpful resources: TEFCA overview.
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