Industries
Data and AI Solutions for Healthcare and Life Sciences
Connect clinical, claims and operational data with FHIR-based interoperability, and apply secure, governed AI to reduce administrative burden and improve care.
Connect Healthcare Data to Better Care and Operations
Hospitals, health systems, payers, digital health companies and life sciences organizations generate vast amounts of data: electronic health records, claims, imaging, lab results, devices and patient-reported outcomes. Interoperability rules built on HL7 FHIR APIs, such as the CMS Interoperability and Prior Authorization Final Rule and nationwide exchange frameworks like TEFCA in the United States, are making that data more accessible. The opportunity is to use it to reduce clinician burden, speed up authorizations and identify patients who need attention earlier.
Hanoi Technology builds the data platforms, integrations and AI applications that make this possible. We implement FHIR APIs and pipelines, unify clinical and claims data into governed analytics environments, and deploy machine learning and generative AI with privacy, security and human oversight designed for regulated health data under rules such as HIPAA and GDPR.
The Challenge
The Data Challenges Facing Healthcare Organizations
Clinical and administrative data is abundant, but connecting and trusting it remains hard.
Fragmented, Non-Interoperable Systems
Patient information is spread across EHRs, lab systems, imaging archives, claims platforms and third-party apps that use different formats and code sets. Clinicians and analysts rarely see a complete picture of the patient journey.
Administrative Burden and Clinician Burnout
Documentation, coding, prior authorization and inbox work consume a large share of clinician and staff time. Manual processes delay care and contribute to staff shortages.
Strict Privacy and Security Obligations
Protected health information must be handled under HIPAA, GDPR and local health data laws, and healthcare remains a frequent target of ransomware. Every new data flow or AI tool must be designed with access control, auditability and minimum necessary use.
Inconsistent Data Quality and Terminology
Diagnoses, procedures, medications and lab results are coded differently across sites, and free-text notes hold much of the clinical meaning. Without terminology mapping and quality checks, analytics and AI produce unreliable results.
Business Value
What Modern Data and AI Deliver for Healthcare
Interoperability That Meets Requirements
FHIR-based APIs and data exchange let you share data with patients, providers and payers as rules require. The same foundation powers your own analytics and apps.
Less Administrative Work
Automate prior authorization preparation, coding support, referral intake and document handling. Staff spend more time with patients and less time on forms.
Earlier Clinical Insight
Predictive models flag deterioration, readmission and care gap risks so care teams can intervene sooner. Population health programs target the patients who benefit most.
Better Operational Flow
Forecast admissions, staffing needs and theatre or bed capacity from historical and real-time data. Operations leaders can plan rather than react.
Trusted Research and Real-World Data
De-identified, standardized data sets based on models such as OMOP accelerate research and real-world evidence studies. Researchers spend less time cleaning data.
Secure, Auditable AI
Every AI application is deployed with access controls, logging, evaluation and clinical oversight. Compliance and clinical governance teams can see how tools are used.
Interoperability Is Infrastructure, Not a Project
Healthcare organizations that treat FHIR APIs and standardized data as shared infrastructure, rather than a one-time compliance deliverable, find that every subsequent analytics and AI initiative becomes faster and cheaper. We design for that compounding effect from the first integration.
Our Services
Our Healthcare and Life Sciences Solutions
Six solution areas that connect clinical, financial and operational data to measurable outcomes.
FHIR Interoperability and APIs
We implement FHIR R4 APIs, including Patient Access, Provider Access and Payer-to-Payer patterns, map legacy HL7 v2 and CDA feeds to FHIR, and build integration services that scale.
- FHIR server and API implementation
- HL7 v2 and CDA to FHIR mapping
- API security with SMART on FHIR and OAuth 2.0
Clinical and Claims Data Platforms
We build HIPAA-aligned cloud lakehouses that unify EHR, claims, lab, pharmacy and device data, with terminology mapping and master patient indexing.
- Healthcare data model and pipelines
- Terminology mapping to SNOMED CT, LOINC and ICD-10
- Patient matching and data quality rules
Prior Authorization and Revenue Cycle Automation
We automate the collection of clinical evidence, coverage rules checks and status tracking for prior authorization, and analyze denials to fix root causes in the revenue cycle.
- Prior authorization workflow automation
- Denial prediction and root-cause analytics
- Integration with payer and EHR systems
Predictive and Population Health Analytics
We build models for readmission, deterioration, no-shows, care gaps and risk stratification, with dashboards care managers can act on.
- Risk stratification models
- Care gap and quality measure dashboards
- Model monitoring and bias review
Generative AI for Clinical and Administrative Work
We deploy assistants that summarize records, draft letters and responses, extract data from referrals and answer staff questions from policies, always with human review.
- Record and referral summarization
- Policy and protocol assistant
- Evaluation sets and safety guardrails
Real-World Data and Research Platforms
We standardize data into research models such as OMOP CDM, apply de-identification and build secure environments for research and life sciences partners.
- OMOP CDM transformation
- De-identification and governance workflows
- Secure research workspaces
Explore
Services for Healthcare and Life Sciences
- Data IntegrationFHIR, HL7 and API integration across clinical systems.Learn more
- Generative AISafe assistants for clinical and administrative work.Learn more
- Machine LearningPredictive models for risk, capacity and operations.Learn more
- Data GovernancePrivacy, consent and access controls for health data.Learn more
- SecurityProtect PHI and critical clinical systems.Learn more
- Reporting and Data VisualizationQuality, operational and population health dashboards.Learn more
Our Approach
How We Engage with Healthcare Organizations
A delivery approach that puts privacy, clinical safety and adoption on equal footing with technology.
- 01
Clinical, Regulatory and Data Discovery
We meet clinical, operational, IT and compliance stakeholders to understand the workflow, data sources, interoperability obligations and privacy constraints for the initiative.
Deliverables: Workflow and data source map · Privacy and compliance requirements
- 02
Use Case Definition and Safety Plan
We define the use case, success measures and where human review is required, and agree a clinical safety and evaluation approach before development starts.
Deliverables: Use case charter · Evaluation and oversight plan
- 03
Secure Integration and Data Foundation
We set up compliant environments, integrate source systems through FHIR, HL7 or database interfaces and standardize terminology and patient identity.
Deliverables: Integration services · Curated, standardized data sets
- 04
Build, Evaluate and Co-Design
We deliver in sprints with clinicians and staff reviewing working software, and measure models and assistants against agreed evaluation sets.
Deliverables: Working application · Evaluation results
- 05
Go-Live, Adoption and Monitoring
We support training, phased rollout and ongoing monitoring of accuracy, usage, bias and system performance, with clear support and escalation paths.
Deliverables: Rollout and training plan · Monitoring dashboards and runbooks
Use Cases
Healthcare and Life Sciences Use Cases
Scenarios where better data and AI change daily work for clinicians, payers and researchers.
Prior Authorization Packet Preparation
When a clinician orders an imaging study, the system checks payer requirements and assembles the relevant notes, results and history from the EHR. Staff review and submit a complete request instead of searching charts manually.
Payer Patient Access and Payer-to-Payer APIs
A health plan exposes claims, clinical and prior authorization data through FHIR APIs. Members can use apps of their choice, and data follows them when they switch plans.
Readmission Risk and Discharge Planning
A model scores inpatients for readmission risk using diagnoses, history and social factors. Care coordinators prioritize follow-up calls and post-discharge services for those at highest risk.
Referral Intake Automation
Faxed and emailed referrals are classified, key data is extracted and matched to existing patient records. Scheduling teams receive complete, structured referrals ready for triage.
Capacity and Staffing Forecasting
Hospital operations teams forecast emergency department arrivals and inpatient census days ahead. Staffing and elective scheduling are adjusted before bottlenecks appear.
Real-World Evidence Studies
A life sciences team analyzes de-identified, OMOP-standardized patient data to understand treatment patterns and outcomes. Studies that once required months of data preparation start in weeks.
Technology
Platforms and Standards We Work With in Healthcare
We build on the interoperability standards and cloud services designed for regulated health data.
- HL7 FHIR R4
- HL7 v2
- SMART on FHIR
- CDS Hooks
- Da Vinci Implementation Guides
- OMOP CDM
- SNOMED CT
- LOINC
- ICD-10
- DICOM
- Azure Health Data Services
- AWS HealthLake
- Google Cloud Healthcare API
- Databricks
- Snowflake
- Power BI
Platforms
Deliver Healthcare and Life Sciences on Your Preferred Platform
- Microsoft AzureModernize data, analytics and AI within your Azure tenant.Explore
- AWSBuild, migrate and run data and AI workloads on Amazon Web Services.Explore
- Google CloudBigQuery, Looker and Vertex AI for data-driven organizations.Explore
- DatabricksUnified lakehouse for data engineering, analytics and AI.Explore
- SnowflakeElastic cloud data platform for analytics and data sharing.Explore
Why Hanoi Technology
Why Organizations Choose Hanoi Technology
Senior, Cross-Functional Teams
Strategy, data, AI, cloud and engineering specialists work as one team, so nothing is lost between hand-offs.
Proven Delivery
More than 50 projects delivered for clients in North America, Europe and Asia-Pacific.
Radical Transparency
A written update every week and a demo of working software every sprint, so you always know where things stand.
Cost-Effective Global Delivery
Experienced engineers from our Hanoi delivery hub, with working-hour overlap for every major time zone.
Success Story · SaaS
AI support assistant and smart escalation for a global SaaS company
A project-management SaaS provider receiving thousands of support requests a day needed faster answers without growing the support team. We built an AI assistant grounded in its knowledge base, with escalation to human agents.
Read the Full StoryMore Success Stories
Education and EdTech
2×course completion rateAdaptive learning paths for a global online education platform
An online education platform had strong enrollment but only about 35% of learners finished their course. We built adaptive learning paths, a mobile app for short lessons and progress features that keep learners going.
Resources
Featured Resources
Industry trends and practical guidance to help you get more value from data, AI and technology.
FSMA 204 After the Delay: Using Shipment Monitoring to Get Traceability-Ready by 2028
The FDA Food Traceability Rule deadline moved to July 2028, but the data work is large. Here is how to use shipment monitoring and event data to get ready without wasting the extra time.
CSRD After the Omnibus: What the Revised ESRS Mean for Reporting From 2027
The Omnibus and the simplified ESRS reshaped EU sustainability reporting in 2026. Here is who is in scope, what changed in the standards and how to adapt your data and process.
OpenTelemetry in 2026: Building a Vendor-Neutral Observability Strategy
OpenTelemetry has become the default way to collect telemetry. Here is what changed in 2026 and a practical roadmap for a vendor-neutral observability stack.
Turn Your Health Data into Better Outcomes
Book a free consultation to discuss interoperability, analytics or AI priorities with our healthcare data team. We reply to every enquiry within 24 hours.
Healthcare and Life Sciences FAQ
How do you protect patient data?
We design every solution around the privacy and security rules that apply to you, such as HIPAA in the United States and GDPR in Europe. That includes encryption in transit and at rest, role-based access aligned with minimum necessary use, audit logging, de-identification where full identifiers are not needed and environments hosted in your cloud tenancy. We also work within your security review and risk assessment processes.
Can you help us meet CMS interoperability and prior authorization requirements?
Yes. We implement the FHIR-based APIs and workflows that interoperability rules require, such as Patient Access, Provider Access, Payer-to-Payer exchange and Prior Authorization APIs, using HL7 Da Vinci implementation guides where applicable. We also help with the underlying data mapping, identity management and testing. Because requirements and deadlines differ by organization type and program, we start by confirming your specific obligations with your compliance team.
Do you work with our EHR?
We integrate with major EHR platforms through their FHIR APIs, HL7 v2 interfaces, vendor app marketplaces and reporting databases, depending on what the vendor and your licenses support. We prefer standards-based integration so solutions remain portable. For workflow tools, we can embed applications inside the EHR using SMART on FHIR or CDS Hooks so clinicians do not need to switch systems.
Is generative AI safe to use in clinical settings?
Safety depends on the use case and the design. We generally start with administrative and documentation tasks where a human reviews every output, such as summarizing records or drafting letters. We ground answers in your own approved content, build evaluation sets with clinicians, log every interaction and define clear boundaries for what the tool must not do. Tools that may qualify as medical device software require additional regulatory assessment, which we flag early.
How long does a typical healthcare data project take?
A focused integration or analytics use case typically reaches production in three to six months. Building an enterprise clinical and claims data platform is usually phased over several releases, with the first data domains available within a few months. Timelines depend mainly on access to source systems, vendor cooperation and internal review processes. We plan these dependencies early and provide weekly written updates so delays are visible.
Do you also support life sciences and digital health companies?
Yes. For digital health companies we build FHIR-enabled products, data pipelines and AI features with privacy built in. For life sciences organizations we standardize real-world data, build research environments and support analytics for medical affairs and commercial teams. Our pharmaceutical industry page describes our work on GxP-regulated manufacturing and quality data in more detail.