Ambient clinical documentation
Record where source information came from, provider review, patient choice, corrections, retention rules, known limits, and note history.
Healthcare AI Evidence and Assurance
Make healthcare AI reviewable.
DigiTrust helps healthcare organizations connect source evidence, repeatable checks, clinician or accountable human approval, approved use, known limits, and a complete review history across important AI workflows.
Designed to complement—not replace—clinical judgment, privacy, security, compliance, ethics, and existing AI governance.
30–45 day pilot2–3 AI workflowsNo protected health information required for initial discoveryAWS Marketplace purchasing
The healthcare AI trust challenge
Human oversight is essential. The evidence behind it must be reviewable.
Healthcare organizations already maintain meaningful controls around patient safety, privacy, clinical authority, quality, research, security, and compliance. As AI enters clinical, administrative, research, and patient-facing workflows, those controls need an operational record that shows how they worked in practice.
DigiTrust preserves the review path. It does not make clinical decisions, replace clinicians, provide legal advice, certify regulatory compliance, or override customer governance.
Priority workflows
Start where AI already influences care, research, communication, or operations.
Clinical research and trial matching
Connect source evidence, eligibility rules, recommendation context, human review, known limits, approval, and a complete decision history.
Patient communication and digital health
Record where content and data came from, the approved purpose, personalization rules, human escalation, known limits, and accountability.
Administrative and operational AI
Review scheduling, automation, knowledge assistants, data and tool access, approval, exception handling, and review evidence.
Healthcare AI innovation programs
Preserve evaluation evidence, success criteria, safety checks, deployment approval, known limits, rollback decisions, and evidence for expansion.
Clinical decision assistance
Connect source evidence, AI-system context, checks, clinician approval, recommendation limits, exceptions, and the final accountable decision.
The DigiTrust healthcare trust record
Make existing human-review and governance controls operationally visible.
Source and workflow history
Identify what information, systems, models, tools, and events influenced the AI-assisted result.
Repeatable, rule-based checks
Record clear checks for integrity, quality, supporting facts, approval rights, conflicts, and safe disclosure.
Clinician or accountable human approval
Record the reviewer, role, approved purpose, conditions, decision, and time period.
Approved-use boundaries
Show whether the AI was used for the approved purpose and stop changed or withdrawn use from inheriting approval.
Known limits and review history
State uncertainty and unavailable evidence clearly and preserve the history for governance, quality, privacy, security, or later review.
Healthcare Pilot
Evaluate two to three consequential workflows in 30–45 days.
DigiTrust Enterprise AI Evidence and Assurance Pilot
Begin with high-level workflow context and synthetic, de-identified, or customer-approved information. Patient-identifiable data, clinical recordings, credentials, and protected health information remain out of the initial discovery path.
Commercial termsCustomer-specific
Scope2–3 workflows
PurchasingAWS Marketplace
ExpansionProduction pathway
- 1Workflow and control assessment
Current clinical or operational use, evidence, governance, authority, privacy, and review state. - 2Evidence and assurance map
Sources, checks, approvals, patient-choice or purpose boundaries, exceptions, and limitations. - 3Production roadmap
Prioritized implementation design, operating controls, data handling, success criteria, and expansion. - 4Executive findings
Customer-safe outputs for clinical, digital, privacy, security, compliance, risk, and purchasing teams.
Who should participate
Bring the people who own the workflow, patient or business consequence, and accountable authority.
Chief Digital or Transformation Officer
Chief Information Officer
Chief Medical Information Officer
Clinical Informatics
AI Governance
Privacy and Compliance
Information Security
Enterprise Risk or Internal Audit
Workflow Clinical or Operational Sponsor
Data and safety boundaries
Start with the workflow—not sensitive patient data.
Initial discovery can use high-level workflows, synthetic examples, de-identified context, or approved metadata.
DigiTrust records and supports human approval; it does not replace clinical judgment.
Privacy, security, quality, ethics, legal, and compliance teams retain their responsibilities.
Any later data handling depends on contracts, architecture, access controls, security requirements, and customer authorization.
Buying through AWS Marketplace
A credible purchasing path for an enterprise healthcare pilot.
DigiTrust is publicly listed in AWS Marketplace. For qualified opportunities, DigiTrans can work with the customer and AWS teams to confirm how AWS is involved, the implementation approach, healthcare-specialist participation, purchasing, and production expansion.
AWS Marketplace availability does not imply AWS endorsement or a guarantee of clinical, patient-safety, security, privacy, regulatory, or business outcomes.
Start with one reviewable workflow
Make healthcare AI easier to govern, explain, and expand.
Request a 30-minute briefing to identify the strongest workflow, accountable stakeholders, information needed for review, how AWS is involved, and the pilot path.