ACI Infotech
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ACI Infotech

Enterprise data and AI, engineered and run in production.

ACI Infotech is an enterprise data and AI engineering firm headquartered in Somerset, New Jersey, with delivery hubs worldwide. We build the data foundation, put AI on top of it, and run both in production for enterprises in financial services, healthcare, retail, manufacturing, and energy.

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ACI Infotech
  • Founded 2006
  • 1,200+ engineers
  • 500+ enterprise projects
  • 11 global delivery hubs
  • ISO 27001:2022
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All playbooks

/ Healthcare playbook

HIPAA + GDPR + More

Multi-Jurisdiction Healthcare Data

Comprehensive playbook for managing patient data across multiple countries with different compliance requirements.

  • 12x deployed
  • Healthcare
  • 15-24 months typical
  • 20-35 consultants

/ Typical outcomes

100%

Patient identity unified

58%

Duplicate reduction

Zero

Compliance violations

100%

Audit coverage

/ Overview

What this playbook is for

Healthcare data is uniquely challenging. Patient privacy isn't just good practice; it's federal law with criminal penalties. And when you operate across borders, you face a patchwork of regulations: HIPAA in the US, GDPR in Europe, PIPEDA in Canada, and dozens of local laws. One misstep means fines, lawsuits, and destroyed trust. Meanwhile, clinical care depends on unified patient data, but your systems have created duplicate records, inconsistent identities, and dangerous gaps. This playbook, refined through 12 deployments across healthcare systems, healthtech companies, clinical research organizations, and pharmaceutical companies, provides the compliance-first architecture for multi-jurisdiction healthcare data.

/ Challenge pattern

When this playbook applies

This playbook fits organizations facing these common challenges:

  • 01Patient data distributed across multiple countries, each with different privacy laws and handling requirements
  • 02HIPAA in the US, GDPR in Europe, plus dozens of local healthcare privacy regulations that conflict and overlap
  • 03No unified patient identity. The same patient appears as 3-5 different records across systems, risking care gaps.
  • 04Audit requirements are extremely stringent. Every access must be logged, justified, and reportable for 7+ years.
  • 05Clinical system integration requires HL7/FHIR compliance, but legacy systems speak proprietary protocols
  • 06Data scientists need access for population health analytics, but PHI exposure risks are significant

/ Solution approach

How the pattern runs

  • Compliance-First Architecture: Data classification, handling rules, and access controls are foundational, not afterthoughts. Every data element tagged by sensitivity.
  • Master Patient Index: Probabilistic matching across name variations, addresses, and demographics creates golden patient record. Reduces duplicates 58%.
  • Encryption Everywhere: AES-256 at rest, TLS 1.3 in transit. Encryption keys managed through HSM with automatic rotation. No exceptions.
  • Complete Audit Trail: Every data access logged with user, timestamp, justification, and data accessed. Retention for 7+ years with immutable storage.
  • Clinical Integration Hub: HL7v2 and FHIR R4 connectors for EHR/EMR systems. Bidirectional sync with conflict resolution.
  • Research Data Sandbox: De-identified datasets for analytics with re-identification risk monitoring. Researchers get insights without PHI exposure.

/ Key learnings

Hard-won lessons from 12 deployments

01

Compliance must be automated from day one. Manual compliance processes fail at scale and create audit gaps.

02

Master patient index with fuzzy/probabilistic matching is essential. Exact matching misses 40% of duplicates.

03

Encryption is baseline, not a feature. Any system handling healthcare data must assume breach attempts.

04

Audit trail requirements are more extensive than initially understood. Plan for 10x expected storage.

05

Clinical staff involvement in design is critical. Systems that don't fit clinical workflow get worked around.

06

Data de-identification for research is harder than expected. Re-identification risk assessment is required.

/ Stack

  • Patient MDM
  • Compliance Automation
  • Encrypted Storage
  • API Gateway
  • Clinical Integration
  • Audit Logging

/ Industries served

  • Healthcare Services
  • Healthcare Tech
  • Clinical Research
  • Pharma

/ Results

What the pattern delivers

100%

Patient identity unified

Golden patient record established across all systems and jurisdictions

58%

Duplicate reduction

Master patient index eliminated duplicate and fragmented records

Zero

Compliance violations

HIPAA, GDPR, and local healthcare privacy compliance maintained through 3 audits

100%

Audit coverage

Every data access logged with complete chain of custody

/ More patterns

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Let's Walk Through This Playbook