This client operated 8 hospitals and 45 clinics with patient data scattered across 12 different EHR and billing systems accumulated over 20 years of acquisitions. Clinicians wasted 30-40 minutes per shift searching for patient information. Duplicate records led to medication errors (two serious incidents in the year before we started) and billing losses the CFO estimated at $3-4M annually. Previous integration attempts had failed twice—once with an enterprise vendor, once with an internal team.
We spent the first 8 weeks just mapping data sources and interviewing clinicians. The previous projects had failed partly because they didn't understand the actual workflows. We proposed a "hub" architecture with a FHIR-compliant data layer that could coexist with legacy systems during a phased transition—no big-bang migration. The client was skeptical (they'd been burned before), so we agreed to a 3-month pilot with one hospital before committing to the full rollout.
We built a probabilistic patient-matching engine that achieved 94% accuracy on clean data and 87% on the messiest legacy records. The new unified dashboard gives clinicians a complete patient view in under 3 seconds. We implemented incremental sync from legacy systems, allowing them to be retired gradually. The pilot at the first hospital went well enough that the client expanded to all 12 facilities, though the timeline slipped from 14 to 18 months.
Duplicate patient records decreased 73% (we projected 89%, but some legacy data quality issues couldn't be resolved automatically). Average time to access patient history dropped from 8 minutes to 15 seconds. No medication reconciliation incidents in the 10 months since launch. The platform now serves 1.8M patient records. Full ROI data isn't in yet, but the CFO estimates $2M+ annual savings.
Built an ML-powered routing system that reduced fleet fuel costs by 18%. Took longer than planned because integrating with their dispatch system was harder than scoped.
Migrated a legacy insurance platform to AWS. Zero customer-facing downtime, though we had three internal incidents during the parallel-running period.
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