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Operational Guidance for Healthcare AI Decisions
Practical analysis, regulatory updates, and evaluation frameworks for the cross-functional leaders accountable for third-party AI risk.
9 briefsSorted by publication date
Regulatory & ComplianceThe Regulatory Landscape: Turn AI Governance Into an Operational AdvantageFederal and state agencies are establishing clear, practical expectations for healthcare AI. Emerging frameworks across predictive decision support, care automation, and generative tools emphasize a core principle: healthcare organizations need clear visibility and documented oversight for the software they deploy.5 min · August 19, 2026Read related domain →Clinical Safety & DriftBias, Equity, and Patient Safety: Ensuring AI Works for Every Patient in Every Care SettingSoftware vendors often showcase impressive overall accuracy metrics during product demos. However, aggregate numbers can easily hide performance dips for specific age groups, language speakers, or care settings, whether in a hospital, a home health visit, a clinic, or a senior living facility.5 min · August 5, 2026Read related domain →Regulatory & ComplianceGenerative AI and PHI Protection: Securing Modern Patient Data PipelinesGenerative AI, from ambient documentation scribes to patient messaging assistants, operates differently than traditional healthcare software. While standard cloud tools store static data in relational databases, generative tools rely on complex data pipelines involving prompt processing, vector search, and temporary context windows.5 min · July 22, 2026Read related domain →
2026 AI Renewal Guide
What to ask before you renew any AI tool this year.
2026 AI Renewal Guide
Eight questions to put in writing before you renew any AI tool this year.
The eight questions, with the evidence to require for each
The clause that follows when the answer is no
State overlays by jurisdiction
A one-page renewal checklist
Procurement & TermsThe AITPRM Procurement Matrix: A Clear Structure for Evaluating Every VendorHealthcare organizations need a repeatable, consistent method to evaluate vendor software. Sourcing teams want an evaluation framework that is simple enough for lean staff to execute, strong enough to present to executive committees, and aligned with modern industry expectations.4 min · July 8, 2026Read related domain →Clinical Safety & DriftAutomation Bias and Clinical Overrides: Why Adoption Does Not Equal SafetyHealthcare organizations frequently monitor technology adoption rates as a key measure of success. High usage metrics across ambient scribes, predictive risk scores, and workflow automation are encouraging, but adoption alone does not guarantee safety.6 min · June 24, 2026Read related domain →Supply Chain RiskAI Supply Chain Risk: Managing Subprocessors and Component LayersTraditional software procurement evaluates the primary vendor. Modern software applications, however, are built on multiple component layers.6 min · June 10, 2026Read related domain →Agentic AI & ScopeAgentic AI in Healthcare: Setting Safe Boundaries for Autonomous ActionModern healthcare software is evolving from passive recommendation engines into active systems capable of executing operational tasks. Applications can now schedule follow up appointments, draft patient portal messages, prepare diagnostic orders, submit prior authorization requests, and trigger routine administrative workflows.5 min · May 27, 2026Read related domain →Procurement & TermsHealthcare AI Sourcing: Establishing Balanced Liability and Contractual ResponsibilityStandard software agreements often include generic liability caps tied strictly to annual subscription fees, broad disclaimers regarding operational responsibility, and limited indemnification for software or data events. While standard for general administrative tools, these terms can create an operational imbalance when applied to clinical, administrative, or workflow automation tools across health systems, medical practices, home health agencies, and post acute care providers.5 min · May 13, 2026Read related domain →