Oncology EHR Vendors

What should you look for in oncology EHR vendors?

When comparing oncology EHR vendors, look for safe chemotherapy and regimen management, structured staging and biomarker data, support for the mCODE FHIR standard, integrations with radiation, pharmacy, labs and genomics, certified cancer registry reporting, and the ability to extend the system where your workflows differ. Test each with real cases, not demos.

The right choice also depends on context. A community oncology practice, a hospital cancer center on an enterprise EHR and a radiation oncology group have different needs, and the best vendor for one may be a poor fit for another.

What types of oncology EHR vendors are there?

There are three broad types of oncology EHR vendors, and many cancer programs use more than one at the same time.

• Enterprise EHR oncology modules: Oncology functions inside a hospital-wide EHR, such as Epic Beacon, sharing one record with inpatient and ambulatory care.
• Specialty oncology EHRs: Systems built for community oncology practices, combining clinical records, regimens, infusion and practice management.
• Radiation oncology information systems: Systems such as Varian ARIA and Elekta MOSAIQ that manage treatment planning data, delivery and radiation-specific records.

Where systems coexist, the evaluation becomes partly an integration question. A hospital may run its enterprise EHR for medical oncology and a radiation oncology system for radiation therapy, and the quality of the data flowing between them determines whether clinicians see one patient story or two.

Specialty vendors often bundle services such as revenue cycle support, analytics or research networks. Evaluate these separately from the core EHR, and check what happens to your data and contracts if you later drop one of them.

How should regimen management be evaluated?

Regimen management should be evaluated as the most safety-critical feature in an oncology EHR. Errors in chemotherapy dosing can cause serious harm, so test how the system prevents them, not only how it displays orders.

• Regimen library: Maintained protocols with drugs, doses, schedules, premedications and supportive care, and a clear process for local changes.
• Dose calculation: Body surface area, weight and lab-based dosing with documented formulas, rounding rules and recalculation when weight changes.
• Cycle tracking: Cycle and day counts, cumulative doses for drugs with lifetime limits and treatment holds.
• Verification steps: Independent checks by pharmacists and nurses recorded in the system before administration.
• Treatment parameters: Lab thresholds that must be met before treatment, with clear overrides and reasons.
• Oral therapies: Prescriptions, adherence and toxicity checks for oral anticancer drugs taken at home.

Ask who maintains the regimen library and how quickly it is updated when evidence or drug labels change. Then ask how local modifications are tracked, reviewed and kept through vendor updates.

Infusion scheduling belongs in the same evaluation. Chair time depends on the regimen, and a system that links regimen orders to chair scheduling, pharmacy preparation and nursing assignments lets the infusion center run on time instead of rebuilding the day by phone each morning.

How should staging, biomarkers and genomics be stored?

Staging, biomarkers and genomics should be stored as structured data, not only as text in notes or scanned reports. Treatment decisions, trial matching and quality reporting all depend on them, and data trapped in PDFs cannot be queried.

Check whether the system captures cancer staging with the staging system and edition recorded, tumor markers and biomarkers as discrete results, and genomic variants from outside labs as structured findings. Many vendors receive genomic reports only as documents, which leaves practices to retype key results or lose them.

Ask how structured genomic results arrive. Some labs send discrete data through HL7 or FHIR interfaces, while others send only PDFs. The vendor's ability to accept discrete results from the labs you use matters more than a general claim of genomics support.

Also check how the record handles change over time. Cancers recur and progress, biomarkers are retested, and staging may be revised after surgery. The system should keep each version with its date and source, so clinicians and registrars can see what was known when each treatment decision was made.

What is mCODE and why should vendors support it?

mCODE, the minimal Common Oncology Data Elements, is an HL7 FHIR implementation guide for core cancer data. Its STU 4 release is based on FHIR R4 and organizes about 40 profiles into six groups: patient information, disease characterization, health assessment, genomics, cancer treatments and outcomes.

The guide notes that mCODE does not mandate what data must be collected; it defines how the data should be represented when it is shared. That makes vendor support a question of whether the EHR can expose and accept its oncology data in mCODE form through FHIR APIs.

Vendors that support mCODE make it easier to exchange records with other cancer centers, send data to research networks and trial sponsors, and build analytics without custom extraction for each system. Ask for the specific profiles supported and examples of real data exchanged.

Support can be partial. A vendor may expose patient and disease profiles but not genomics or treatment data, so request a sample export for a complex case and compare it with the guide before accepting a general compliance claim.

Which integrations should an oncology EHR support?

An oncology EHR should support integrations across the many systems involved in cancer care. Gaps in any of these usually lead to retyping and missed information.

• Radiation oncology: Treatment summaries, courses and doses exchanged with the radiation system.
• Pharmacy and infusion: Orders, preparation and administration records, and pump integration where available.
• Laboratory and pathology: Results that feed treatment parameters, and pathology reports with structured findings.
• Genomics labs: Orders sent electronically and discrete results returned.
• Cancer registries: Reports for state cancer registries, supported by the certification criterion in 45 CFR 170.315(f)(4) for transmission to cancer registries.
• Clinical trials: Eligibility screening, enrollment status and research data capture.

Ask whether each integration is a standard product, a custom interface or a roadmap item. The answer affects cost and timeline, and roadmap items should not be treated as available.

Ask for references from customers running the same integrations with the same partner systems. A vendor's interface with one radiation system or genomics lab tells you little about how it works with another, and the people who run those interfaces every day will describe the failure modes candidly.

Which questions should you ask in vendor demos?

In vendor demos, ask the vendor to run your scenarios rather than theirs. Prepare scripts from real, de-identified cases and watch how many clicks, screens and workarounds each step needs.

• Dose change: Show a patient whose weight drops mid-treatment and how doses are recalculated and verified.
• Treatment hold: Show a lab value below threshold and the hold, override and documentation steps.
• Genomic result: Show a discrete genomic result arriving from an outside lab and how it appears in the chart.
• Data export: Export a patient's oncology data in mCODE form through the API.
• Registry report: Generate a cancer registry report and show how errors are corrected.
• Local regimen: Add a local regimen variation and show the review and approval trail.

Include pharmacists, infusion nurses, oncologists and registrars in the demos. Each group catches problems the others miss, and their support will matter during implementation.

How should cost and contract terms be compared?

Cost and contract terms should be compared over the full life of the system, not just the first-year quote. Oncology systems carry ongoing costs that general EHR comparisons often miss, and some only appear once the practice depends on the vendor.

• Regimen content: Whether library updates are included or sold as a separate subscription.
• Interfaces: Per-interface fees for labs, genomics, radiation and registries, and charges for changes.
• API access: Fees or limits on FHIR and bulk data access, which affect analytics and custom apps.
• Data exit: The format, completeness and cost of a full data export if you leave.
• Bundled services: Terms that tie the EHR to billing, analytics or research programs.

Data exit terms deserve the closest reading. Oncology records span years of treatment and surveillance, and a practice that cannot export regimens, doses and structured results in usable form is effectively locked in, whatever the contract says about termination.

When does a custom oncology layer make sense?

A custom oncology layer makes sense when the vendor system covers the core record well but your program has needs it does not meet, such as specialized trial matching, survivorship programs, patient-reported outcomes or cross-site analytics.

Build these as SMART on FHIR apps, services that read and write through vendor APIs, or data pipelines into a separate analytics platform. That keeps the EHR as the source of truth and avoids modifying vendor code, which makes upgrades safer.

Patient-reported outcomes are a common example. Symptom tracking between visits can catch treatment toxicity early, but many oncology EHRs offer limited tools for it. A separate app that collects symptoms, alerts the care team on severe scores and writes results back to the chart fills that gap without replacing anything.

How Agnotic can help

Agnotic Technologies builds oncology software alongside existing EHRs: genomic result integration, mCODE data pipelines, cancer registry and research data feeds, patient-reported outcome apps and analytics across sites.

We also help cancer programs prepare vendor evaluations, writing demo scripts and testing integrations so decisions rest on how systems handle real cases.

Sources

• HL7: mCODE FHIR Implementation Guide
• 45 CFR 170.315: ONC certification criteria for health IT