Explainable AI for Healthcare
Health systems and payers run AI in clinical decision support, coding, and claims, where FDA and HIPAA auditors scrutinize every output. Seekr® gives compliance and clinical informatics teams observability, explainability, and a defensible audit trail for every decision.
How Seekr works for healthcare
From clinical model evaluation through ongoing production monitoring, Seekr gives clinical informatics, compliance, and operations teams the evidence they need at every stage of the AI lifecycle.
Prepare and govern your data
Seekr can connect to clinical data sources through standard interoperability protocols like FHIR and HL7, subject to the BAA chain and access approvals each system requires. Prepare claims records, clinical registries, and structured EHR exports into training-ready datasets with full lineage. Your data stays in your environment, and every preparation step is logged, so you can show regulators exactly what the model learned from.
Select and certify models
Compare candidate models side by side against your own patient populations, clinical scenarios, and safety criteria. For imaging models, Seekr can support verification that de-identification of PHI in DICOM metadata and pixel data has been completed before any training use. Score for accuracy, bias across patient demographics, and failure modes before any model touches a clinical or administrative decision, so the model you approve is the one you can defend.
Deploy with full provenance
Every output carries a confidence score and a source trail that traces the answer back to the data, the reasoning, and the model version that produced it. When a clinician, a patient, or a regulator asks how a recommendation was reached, the evidence is already assembled.
Monitor and re-evaluate
Detect model drift as patient populations change, clinical guidelines evolve, coding rules update, or disease prevalence shifts. Retest against the same criteria on a continuous basis, and maintain a documented record of performance, risk, and change over time.
Core capabilities
Explain every clinical output, score models against your risk profiles, monitor drift, document every correction, and run anywhere PHI must stay, at predictable cost.
Model-agnostic explainability
Trace any output to the data and reasoning behind it, across model, tools, and steps, so you can show clinicians and regulators exactly what drove it.
Quantified model risk
Define risk profiles for your specialties, populations, and regulations. Score models against them, set thresholds, and catch failures before production.
Flexible deployment
Run in Seekr’s cloud, yours, on premises, or air gapped. Each is designed to keep PHI in your BAA chain and help you meet HIPAA and residency rules.
Continuous evaluation and drift detection
Populations change and guidelines evolve. Detect drift in accuracy, safety, and risk posture, with audit-ready evidence of performance over time.
Contestability
When your team sees why a decision was made, they can challenge it, correct it, and confirm the fix, with every intervention.
Predictable cost structure
Match model and compute to each workload, with evidence to defend every choice. The leanest model that meets requirements keeps costs predictable.
Seekrflow is the AI operating system for healthcare
Launch prebuilt healthcare solutions instantly or create your own on a robust, extensible architecture built for scale. SeekrFlow enables full visibility and control over how AI learns, reasons, and delivers results in modern healthcare environments.
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Deploy Seekr anywhere
Seekr cloud
Fully managed in the Seekr cloud. Fastest path to production for health systems piloting AI on administrative or coding workflows before scaling to clinical applications.
Fully managed in the Seekr cloud. Fastest path to production for health systems piloting AI on administrative or coding workflows before scaling to clinical applications.
Your cloud
Deploy in your AWS, Azure, or GCP environment. Patient data stays in your tenancy within your approved BAA chain.
Deploy in your AWS, Azure, or GCP environment. Patient data stays in your tenancy within your approved BAA chain.
On-premises
Deployment for organizations that require physical control over EHR integrations, imaging archives, or air-gapped clinical environments.
Deployment for organizations that require physical control over EHR integrations, imaging archives, or air-gapped clinical environments.
At the edge
Deploy at the clinic, the imaging center, or the point of care where latency or connectivity constraints require processing closer to the patient.
Deploy at the clinic, the imaging center, or the point of care where latency or connectivity constraints require processing closer to the patient.
FAQ
How does Seekr help healthcare organizations defend AI outputs?
Seekr traces every AI output to the data, reasoning, and model version that produced it, creating an evidence trail that maps to what regulators, clinicians, and patients need to see. Instead of reconstructing why a clinical recommendation was made after a challenge, your team produces the provenance at the time of the output. This covers clinical decision support, coding, risk stratification, claims processing, and any workflow where an FDA reviewer, a HIPAA auditor, a clinician, or a patient needs to see how the answer was reached.
Can Seekr run AI on protected health information without that data being exposed?
Yes. Seekr deploys in your cloud, your data center on premises, or air gapped. EHR data, imaging archives, lab results, and claims records can stay within your approved BAA chain. Seekr is designed to support Business Associate Agreements, and your data is never used to train another organization’s models. You choose the deployment model, and Seekr runs where your data already lives.
How do we evaluate whether an AI model is safe for clinical use?
Seekr lets you compare candidate models side by side against your own patient populations, clinical scenarios, and safety criteria rather than relying on generic benchmarks or vendor claims. You define what matters (diagnostic accuracy across patient demographics, bias detection, performance under edge-case clinical scenarios) and score models against those criteria before any model reaches clinical production.
What happens when conditions like patient populations change, and a model needs to change with it?
Seekr monitors for drift in accuracy, safety, and risk posture on an ongoing basis and flags when a model’s behavior deviates from the criteria it was approved against. You retest against the same framework you used during initial evaluation, which creates a documented record of performance over time. When conditions change, you re-evaluate, and the evidence record stays continuous.
How is Seekr different from clinical analytics and monitoring solutions?
Clinical analytics tools measure outcomes: what diagnoses were made, what codes were assigned, what costs were incurred. Seekr goes further by answering what influenced the decision, tracing which inputs materially drove the output rather than reporting the result. In an FDA review, a HIPAA audit, or a malpractice proceeding, the difference between showing your clinical metrics and showing your evidence is the difference between describing an outcome and defending a decision.
Make every clinical and administrative decision one you can defend
See how Seekr works for healthcare clinical informatics, compliance, and operations in a 30-minute technical briefing.