Practices & health systems
Oncology groups modernizing the data analytics workflow.
Data Analytics for Oncology
Data Analytics for Oncology
We build data analytics for oncology — pairing the engineering of a purpose-built data analytics with the real workflows, codes, and devices of oncology care, all handled to HIPAA standards.
HIPAA-ReadyFHIR R4InteroperablePHI-Safe
Trusted by global innovators
What data analytics for oncology involves
Oncology care has specific needs — Oncology software has to model complex, protocol-driven care — chemotherapy regimens, TNM staging, molecular markers, and clinical-trial eligibility — where a small error carries real clinical weight. A data analytics built for it has to serve those needs directly rather than repurpose a generic product.
Building data analytics for oncology means solving turning the specialty's clinical and operational data into decisions, not just dashboards, tuned to the codes and workflows that matter here (ICD-O / TNM cancer staging, FHIR R4 Condition, MedicationRequest & genomics, HL7 v2 lab and pathology feeds), with PHI handled to HIPAA standards from the first commit.
What we build into Oncology Data Analytics
The data analytics engineering, tuned to oncology.
Specialty data warehouse
Specialty data warehouse, built for the oncology use case.
Quality and outcome dashboards
Quality and outcome dashboards, tuned to oncology workflows.
Population and operational analytics
Population and operational analytics for a connected, useful data analytics.
Oncology codes & workflow
Built around the codes and workflow that matter in oncology: ICD-O / TNM cancer staging, FHIR R4 Condition, MedicationRequest & genomics, HL7 v2 lab and pathology feeds.
Privacy-first by design
PHI protected with encryption, access controls, and audit logging under a BAA.
Built to integrate
We engineer for turning the specialty's clinical and operational data into decisions, not just dashboards, connected to the systems oncology care already uses.
Where it fits
Who builds data analytics for oncology
Teams bringing better oncology products and workflows to market.
Digital health startups
Oncology-focused data analytics products.
MedTech & device makers
Oncology data analytics tied to devices and data.
Payers & programs
Oncology data analytics as a benefit or program.
Built for oncology, engineered to last
Data Analytics for oncology earns its place by fitting the workflow, not the other way round.
Protocol-driven
Regimen and staging logic built safety-first
Interoperable
FHIR R4, HL7 v2, and SMART on FHIR by default
MVP-ready
Core data analytics workflow for oncology, shipped then scaled
Compliance-First Healthcare App Development Services Backed by Global Standards
15-Minute Scoping Call01
HIPAA
Health Insurance Portability and Accountability Act
Protect PHI with privacy-first architecture, encrypted storage and transmission, strict access controls, and traceable audit logs.
02
GDPR
General Data Protection Regulation
Implement lawful consent flows, data minimization, retention controls, and secure processing for sensitive health data.
03
FHIR
Fast Healthcare Interoperability Resources
Enable standardized health data exchange across apps, care teams, and systems through robust FHIR-ready APIs.
04
HL7
Health Level Seven International
Support enterprise-grade interoperability with HL7-based integrations for records, events, and clinical messaging workflows.
05
HITRUST
Health Information Trust Alliance
Align security programs to healthcare-specific control and risk management practices trusted by providers and ecosystem partners.
06
HITECH
Health Information Technology for Economic and Clinical Health Act
Design with breach notification readiness, digital record safeguards, and operational controls that support regulated care programs.
07
SaMD
FDA Software as a Medical Device
Plan software quality, traceability, and documentation pathways for products that may require SaMD review and submission.
08
MDR (EU)
Medical Device Regulation (European Union)
Prepare EU market-ready processes for risk classification, evidence tracking, and lifecycle governance under MDR expectations.
09
SAMHSA
Substance Abuse and Mental Health Services Administration
Apply confidentiality controls and consent-aware sharing models for behavioral and mental health data experiences.
Why oncology needs a purpose-built data analytics
Oncology codes, standards & devices
ICD-OFHIRHL7
Our Process
How we build data analytics for oncology
We prove the core workflow with real users before scaling — clinical fit and adoption decide the outcome.
1.
Discovery & Clinical Design
We map the oncology workflow and the core data analytics job to be done.
Workflow-shaped
2.
Core Build
We build the data analytics substance with interoperability and PHI handling engineered in.
Compliant
3.
Integrations
EHR, device, and standards integration so it connects to the systems already in use.
Connected
4.
Launch & Iterate
A measured launch tracking adoption and outcomes, then iteration.
Adoption-tuned
Frequently Asked Questions
It is built around oncology — Oncology software has to model complex, protocol-driven care — chemotherapy regimens, TNM staging, molecular markers, and clinical-trial eligibility — where a small error carries real clinical weight — and its codes and workflows (ICD-O / TNM cancer staging, FHIR R4 Condition, MedicationRequest & genomics, HL7 v2 lab and pathology feeds), rather than a one-size-fits-all product. That fit is where the clinical value and the engineering live.
Ready to build data analytics for oncology?
Tell us your oncology workflow and goals. We'll return a plan for data analytics that's clinically credible, interoperable, and built to ship.
contact@agnotic.com
Partnerships
contact@agnotic.com