Clinical data integration
Connect information from clinical systems, laboratories and payer sources. Define formats, data quality and access requirements.
- HL7, CCD and flat files
- EMR and payer databases
INTELLYLABS / HEALTHCARE TECHNOLOGY
Bring fragmented clinical information, reporting and patient communication into connected platforms — so care teams spend less time chasing data and more time with patients.

WHAT WE BUILD
We work with providers, health systems and health-tech companies on the platforms behind better care.
Connect information from clinical systems, laboratories and payer sources. Define formats, data quality and access requirements.
Design data transformation and repeatable reporting around the quality and operational questions your team needs to answer.
Plan role-based access, identity integration and scalable architecture around your users and environment.
Conversational assistants that help patients book appointments, find information and get answers faster.
Use data and AI to anticipate claim denials and reduce manual rework in billing workflows.
Secure web and mobile apps for patients, clinicians and care coordinators.

CASE STUDY
Clinical, laboratory and payer information was spread across flat files, HL7, CCDs and EMR databases, and reporting depended on manual integration.
A microservices approach and cloud data platform automated inbound and outbound files, enabled repeatable reporting and provided patient-record access through a shared web experience with single sign-on.

OUR PRINCIPLES
Healthcare data demands care. We design with privacy, consent and auditability in mind and work within your compliance framework from the very first workshop.
HOW WE WORK
Recurring reporting and integration needs.
Map, cleanse and model data sources.
Develop the web platform and access layer.
Integration and automation testing before go-live.
BLOGS
Read more from our team.

How conversational AI supports patients and care teams.
Read the case study→
Predicting claim denials and reducing rework with AI.
Read the case study→
How ML helps researchers read biological data.
Read the case study→NEXT STEP
Share your systems, reporting needs and integration challenge. We will help you plan a practical next step.
