- Client case study · Cloud warehouse / governance
- Client anonymised with their approval
Healthcare analytics, securely scoped.
A multi-location provider network needed centralized operational insight without weakening privacy controls or exposing patient-level records to unauthorized audiences.
The engagement at a glance
What Quorlytic built.
Quorlytic established a governed serverless BigQuery warehouse for a multi-location healthcare provider network, replaced spreadsheet-based reporting with Looker semantic access, and scoped role-specific analytics so each audience sees only the views it is approved for — with audit, retention and revocation designed in.
Reporting before
Manual spreadsheets and isolated databases slowed cross-site reviews.
Architecture
Serverless BigQuery foundation and governed semantic access.
Access at scale
Department-aware controls for a population of 200+ users.
Architecture
The architecture, and the decisions that hold it up.
Security cross-cutting layer: Source access approval, protected transport, identity-based permissions, least-privilege data views, audit logging, retention and controlled external sharing.
Architecture guardrails
Minimize patient data
Use only necessary attributes and aggregate wherever the analytical decision permits.
Separate roles
Differentiate analysts, clinicians, administrators, executives and partners.
Review obligations
Security and legal owners validate privacy, jurisdiction and retention requirements.
Delivery
A three-phase implementation.
Delivery ran in three phases, each closing on an exit gate before the next phase began.
- Phase 01
Foundation + core ingestion
- Source inventory, privacy classification, stakeholder decisions and acceptance matrix.
- Cloud project, private connectivity, service identities and baseline audit setup.
- Ingest nine agreed operational tables, normalize keys and record source lineage.
- Reconcile extracts, validate ownership and approve the foundational data set.
Exit gate: Validated nine-table foundation, initial runbook and approved access baseline.
- Phase 02
Dashboards + permission design
- Design semantic model, define operational measures and dashboard specifications.
- Build executive and departmental dashboards with audited data definitions.
- Implement group-based RBAC, restricted views and test user-role mappings.
- Perform persona-based UAT and remediate critical access / metric defects.
Exit gate: Signed dashboard UAT, permission test evidence and role ownership.
- Phase 03
Advanced analytics + handover
- Extend clinical performance measures and confirm approved drill-through paths.
- Provision limited external vendor or auditor access to approved aggregates.
- Harden alerts, document recovery, test access revocation and perform go-live checks.
- Train owners, finalize SOPs and transfer operational accountability.
Exit gate: Secure production release, monitoring and named operating owners.
Analytical products
Turn warehouse data into trusted action.
Dashboards designed around operational decisions, with evidence confirmed before production access was expanded.
Leadership scorecard
Facility trends, scheduling utilization, service access and operational throughput. All metrics carry documented definitions and cohort rules.
Department workspace
Authorized service-line reporting, referral queues, appointment patterns and drill-through only where permitted.
Clinical monitoring
Pre-agreed operational and care-process indicators, with denominator rules and review by clinical owners.
Production readiness evidence
Reliable by design — not just at launch.
Governance, QA and incident ownership formed part of the deliverable, rather than being added after dashboards went live.
Data fidelity
Table counts, key distributions, incremental replay, transformation checks and source-owner reconciliation.
Privacy / permissions
Role-denied tests, masked-sensitive views, service identity review, audit trail and access revocation drill.
Dashboards / usability
Approved metric descriptions, persona acceptance, filter behavior, refresh expectations and accessible user guidance.
Operations / recovery
Failure notifications, incident ownership, runbook execution, restoration test and scheduled access review.
Risks and responses
Risks managed during delivery.
Excessive permissions
Least privilege, recurring reviews and role-denial regression checks.
Partner disclosure
Aggregate-only publishing, isolated datasets and expiring grants.
Reporting drift
Signed source definitions, lineage and versioned semantic models.
Questions
Questions about this engagement.
What did Quorlytic build for the healthcare provider network?
A governed serverless BigQuery warehouse fed by secure, validated synchronization from healthcare CRM and operational systems, with Looker semantic access that gives each audience — leadership, departments, operations, administrators and approved partners — only the views it is permitted to see.
How is patient data protected?
Patient data is minimized and aggregated wherever the decision permits, roles are separated, and access runs through identity-based permissions, least-privilege views, audit logging, retention rules and revocable partner grants. Security and legal owners validate the obligations for the deployment jurisdiction.
Which technologies were used?
A serverless BigQuery data platform fed by secure, incremental synchronization from healthcare CRM and operational systems, and Looker semantic access with group-based RBAC and restricted views.
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