A multi-facility healthcare network used QueryMind and DataSentinel to detect and explain revenue drift across billing systems—before it compounded into regulatory exposure.
Clinical (EHR), billing, lab, PACS, and external payer feeds often disagree on encounters and timestamps. Reconciling these reliably requires lineage, mapping, and conservative joins.
CPT/DRG use differs across facilities and coders. Small documentation differences can produce large revenue changes. Detection requires semantic matching across clinical notes and invoice records.
Different payer contracts apply different remittance rules; revenue shifts may be payer-driven rather than clinical. Our solution surfaces payer-driven vs clinical-driven causes.
Finance noticed recurring drops in revenue for several service lines. Surface-level metrics (totals) were available, but leadership needed causality: which service, which site, which documentation gap, and what payer behavior caused the change. Analysts were reluctant to run complex joins for fear of producing misleading results because some sources (e.g., lab, outpatient) were partial or delayed.
We classify the user's question as diagnostic (causal) rather than an aggregation. This changes downstream logic: the planner avoids naive aggregations and instead reasons about segments, baselines, and required evidence to support causal claims.
Map natural-language entities (e.g., "revenue", "elective procedures", "site X") to canonical tables, columns, and glossary terms. Use column lineage and historical validated queries to avoid ambiguous mappings.
The planner decomposes analysis into sub-queries, estimates cost, computes blast radius, and consults DataSentinel signals for data completeness and trust. High-risk plans (wide cross-facility joins, cross-PHI joins) are flagged and either rewritten into safe alternatives or require explicit approval.
Execute with row limits, cost ceilings and timeouts. Every execution records: intent, plan, data sources, result shape and trust scores — all available in a searchable execution log for audit and model feedback.
Return narrative explanations (what changed, what was excluded, alternative interpretations). Confirmed corrections (only from authorized reviewers) are used to update metadata and mappings — not to blindy retrain models — preserving correctness.
Multi-facility healthcare provider network serving 250,000+ patients annually across clinical, billing, lab, and payer systems with strict HIPAA compliance requirements.
EHR, billing, lab, and PACS systems disagreed on encounters and timestamps. Reconciling reliably required lineage, mapping, and conservative joins.
8% month-on-month decline in elective procedures revenue with inconsistent encounter counts and incomplete cohorts from delayed feeds.
CPT/DRG usage differed across facilities. Small documentation gaps produced large revenue changes requiring semantic matching.
No risky wide joins across sensitive PHI without controls. Required reproducible, auditable query plans for regulatory compliance.
Classify questions as diagnostic (causal) rather than aggregation, changing downstream logic to reason about segments and required evidence.
Map natural-language entities to canonical tables using column lineage and historical validated queries to avoid ambiguous mappings.
Decompose analysis into sub-queries, estimate cost, compute blast radius, and consult DataSentinel for completeness and trust.
Execute with row limits, cost ceilings and timeouts. Record intent, plan, data sources, and trust scores for audit trails.
Return narrative explanations with confirmed corrections used to update metadata—preserving correctness over blind retraining.
These numbers reflect actual impact from a 3-month deployment. Results depend on data maturity, scope, and remediation workflows. We always run discovery to produce realistic estimates before any PoC.
Let's discuss how Nabla-X can help you detect and explain hidden patterns in your healthcare data.