How Kior Healthcare unified lab, ERP, and clinical data to automate reporting and enable population health insights in days.
Customer overview
Industry and customer challenges
The diagnostics and healthcare services space faces a familiar challenge: data scattered across multiple clinical and operational systems. As patient volumes grow and new service lines are added, each workflow introduces its own data formats, report types, and manual steps, making consistency and clinical visibility increasingly difficult. And Kior Healthcare was experiencing the same issues:
1. Fragmented clinical and lab data
Patient records, lab results, imaging reports, ERP entries, and booking data all lived in separate systems with no unified source of truth.
2. Manual reporting workflows
Teams spent hours stitching together spreadsheets, PDFs, scanned files, and clinical notes to create daily and weekly reports.
3. No data engineering capacity
Kior lacked the internal engineering bandwidth to build automated ingestion pipelines or maintain governed data flows.
4. Limited clinician visibility
Clinicians could not easily access unified patient timelines, abnormal result patterns, or pending-report insights.
5. Operational overhead increased with scale
As test volumes and service locations grew, manual data handling and reporting became harder to sustain.
How LakeStack helped Kior healthcare
LakeStack was fully implemented within days, establishing an end-to-end data foundation across ingestion, unstructured file processing, modeling, governance, and clinical analytics. All delivered without requiring internal engineering effort.
LakeStack harmonized LIS outputs, ERP data, bookings, patient records, imaging reports, PDFs, and notes into a single governed model.
Lab values, medical reports, scanned forms, attachments, and clinician notes were processed automatically, eliminating manual handling.
Dashboards provided real-time visibility into test volumes, turnaround times, delays, revenue trends, technician workloads, and pending reports.
Clinicians could run NLQ queries like: “Show patients with abnormal results today.” or “List all pending reports older than 24 hours.”
LakeStack powered early AI use cases such as: patient reminders, overdue test follow-ups, population-health pattern detection.
PHI masking, role-based access, and audit logs ensured compliant handling of patient data across locations.
Success metrics
- $250K saved in manual engineering cost.
- 80% reduction in manual data prep and file processing.
- 70% faster visibility for clinician and operational insights.
- 60% faster follow-up and reminder workflows.
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