Industry Challenges
The data challenges healthcare organizations face
Healthcare data is among the most sensitive, most fragmented, and most regulation-bound data in any industry — and the stakes of getting it wrong are uniquely high.
Privacy and compliance
PIPEDA, provincial health privacy acts, and PHIPA require strict data governance, consent tracking, and audit trails.
Siloed EHR systems
Epic, Cerner, Meditech, and legacy hospital systems store data in incompatible formats, blocking a unified patient view.
Manual reporting burden
Clinical staff spend hours on data entry and administrative reporting that could be automated, freeing time for patient care.
Research data readiness
Clinical trial data, real-world evidence, and population cohort studies require clean, well-documented datasets that are hard to produce.
How We Help
How LanaCloud delivers results
We build secure, privacy-by-design data platforms for healthcare organizations — ingesting, transforming, and serving clinical data with full governance and auditability.
Privacy-by-design pipelines
Data de-identification, consent management, and role-based access control baked into every layer of the pipeline.
EHR integration & harmonization
FHIR R4-compliant data integration across Epic, Cerner, and legacy systems into a unified clinical data repository.
Population health dashboards
Interactive dashboards for health administrators tracking chronic disease prevalence, readmission rates, and resource utilization.
Clinical NLP & text mining
Extract structured insights from clinical notes, discharge summaries, and pathology reports using transformer-based NLP models.
Automated regulatory reporting
CIHI, provincial ministry, and accreditation reporting automated through scheduled data pipelines with full lineage.
Predictive readmission models
ML models that flag high-risk patients before discharge, enabling proactive care coordination and reducing 30-day readmissions.
Use Cases
Real-world applications
For hospitals, health authorities, clinic networks, and health-tech companies across Canada.
Chronic disease registry analytics
Automated population-level analytics for diabetes, hypertension, and COPD registries with trend monitoring.
Analytics · Population HealthClinical trial data pipelines
Automated data collection, cleaning, and validation pipelines for Phase II/III clinical trials.
Data Engineering · ResearchED wait-time prediction
ML models predicting emergency department wait times and volume surges to optimize staffing and patient flow.
ML · OperationsCIHI submission automation
End-to-end pipeline that pulls from EHR, transforms to CIHI DAD format, validates, and submits automatically.
Compliance · AutomationReady to build a governed health data platform?
We understand PHIPA, FHIR, and CIHI — and we speak both data engineering and clinical workflow.