Your health system is making AI-influenced billing and coding decisions today. CMS has 2,000 new auditors and a 7-year lookback window through 2033. Can you reconstruct every AI-influenced decision they might question?
If the answer is no — or you're not sure — that's the governance gap.
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​CMS launched its HealthTech Ecosystem in April 2026 — endorsing AI deployment at scale across 700+ organizations. The framework covers identity, security, and interoperability. It says nothing about who is accountable when an AI-influenced billing or clinical decision fails under audit. That accountability belongs to your health system. It always did.
ANSI/HSI 2800:2025, the first American National Standard for AI governance in healthcare, places explicit accountability on your Board of Directors and CEO — now, not when federal mandates arrive.
Skadden's February 2026 guidance established that directors face personal liability for AI deployment without governance when standards were foreseeable.
The governance infrastructure most health systems have built is not sufficient to answer the questions that are already being asked.
76% of health systems cannot fully reconstruct AI-influenced decision chains. That's the number CMS auditors will expose.
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$2M+ in hidden annual supervision costs go untracked in the average health system. That's the number your CFO doesn't know exists.
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72% of AI pilots never reach production. That's the return on investment your board approved that never materialized.
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Source: Black Book 2026 Healthcare AI Governance Study, 384 organizations
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ABOUT JEFF PEDONE
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​I implement the operational governance infrastructure that makes AI-influenced clinical and billing decisions reconstructable and defensible. Decision authority documentation. Audit trail architecture. Supervision protocols. Vendor accountability frameworks. Board-level reporting. Delivered in 90 days, directly by me.
Contributing Researcher, Black Book 2026 Healthcare AI Governance Study — 384 healthcare organizations.
30+ years enterprise technology operations across healthcare, government, and critical infrastructure — Red Hat, Cisco, Oracle.
Vendor-agnostic. Works across Epic, Oracle, DAX, Abridge, and any AI stack. No technology to sell. No platform subscription. Operational frameworks that function inside your existing infrastructure.
I work directly with health systems, alongside healthcare law counsel advising clients on AI governance exposure, and through trusted advisor partners who serve mid-market health systems.
THREE ENGAGEMENT MODELS
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Fractional Chief AI Governance Officer
Your board and legal counsel are already asking who is accountable for AI governance in your organization. I can be that person on a fractional basis — starting next month.
Named executive accountability for AI governance. ANSI/HSI 2800:2025-aligned governance structure. Board-level reporting. Ongoing monitoring and escalation. Built and operational in 90 days, without the search timeline, compensation overhead, or organizational commitment of a full-time hire.
Monthly retainer engagement scoped to your organization's size and AI deployment complexity. For health system Boards, CEOs, and General Counsel who need a governance answer before regulators ask the question.
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Law Firm Advisory Partnership
Healthcare law firms advising clients on AI liability, CMS enforcement, and board fiduciary duty need an operational partner who can build the governance infrastructure their attorneys are recommending.
I work alongside healthcare legal counsel as the implementation layer that converts legal guidance into defensible operational practice — providing health system clients with named accountability, documented governance infrastructure, and audit-ready evidence that legal strategy alone cannot produce.
The referral is simple. The value to your client is immediate. The governance gap your attorneys have identified gets closed.
For partners at firms advising health systems on AI governance exposure.
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90-Day Governance Implementation
Full deployment of the Pedone Adaptive Governance Framework™ across your AI stack — Epic, Oracle, DAX, Abridge, ambient documentation, revenue cycle AI, whatever you're running.
Delivers in 90 days: Board-ready governance documentation. Vendor contracts with enforcement clauses. Audit trail architecture. Decision authority matrices. A trained governance committee that can answer the questions CMS auditors and your board will ask.
For CFOs, CCOs, CMIOs, and General Counsel at health systems that have deployed AI and need the governance infrastructure to match.
REGULATORY INTELLIGENCE
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The standards are active now.
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ANSI/HSI 2800:2025 — approved December 2025. First American National Standard. Board and CEO accountability. Not a best practice guide — a national standard.
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NIST AI 800-4 — published March 2026. Formally documented that no validated methodologies yet exist for post-deployment AI monitoring. The mandate is active. The implementation gap is on federal record.
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CMS HealthTech Ecosystem, April 2026 — Federal endorsement of AI deployment at scale across 700+ organizations. Identity, security, and interoperability addressed. AI decision accountability left to the health system.
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OIG 7 Elements / Federal Sentencing Guidelines — AI governance programs that map to the OIG's 7-element compliance framework carry documented mitigation value in federal enforcement proceedings.
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CMS — 2,000 new auditors. 7-year lookback through 2031–2033. AI-influenced billing and coding decisions made today are auditable for the next seven years.
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Norton Rose Fulbright, April 2026 — Healthcare enforcement partner Jeff Wurzburg: 'As AI tools become more integrated and routine, enforcement will focus on governance, documentation and oversight. Boards that lack defined governance frameworks may face heightened scrutiny. Board lapses are increasingly likely to be viewed as oversight failures rather than technology missteps.
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​HHS AI Strategy, September 2025 — Five-pillar federal framework for AI governance in healthcare. Pillar 1 establishes minimum risk practices for high-impact AI including pre-deployment testing, impact assessments, independent review, continuous monitoring, and safe termination protocols. Mid-market health systems subject to HHS oversight are expected to align.
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Skadden, February 2026 — Directors face personal liability for AI deployment without governance when standards were foreseeable and preventable.
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"Cleared for market does not mean ready for 2 AM." — Forbes, March 2026
Complimentary AI Governance Readiness Assessment
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A structured evaluation of your current governance posture across five domains: Decision Authority, Audit Trail Architecture, Supervision Protocols, Vendor Accountability, and Board Reporting. Delivered within 48 hours. No sales process attached. Designed to give health system leaders — and their legal counsel — a clear picture of where exposure exists before regulators ask.
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Request your scorecard by emailing us: jeff@pedoneai.com
What the AHA's Federal Submission Means for Health System AI Governance
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Jeffrey Pedone, Founder & CEO, Pedone AI Advisors
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On February 23, 2026, the American Hospital Association submitted formal comments to HHS in response to the agency's Request for Information on accelerating AI adoption in clinical care (RIN 0955-AA13). The 15-page submission, filed on behalf of nearly 5,000 member hospitals, 270,000+ affiliated physicians, and 2 million nurses, contains what may be the most consequential policy signal for health system AI governance to date.
The AHA explicitly called on HHS to develop risk-based post-deployment standards for AI-enabled medical devices.
That single recommendation confirms what many operational leaders have been navigating quietly: the governance infrastructure for AI systems after they've been deployed into clinical workflows largely doesn't exist — and the industry's most influential trade association is now asking the federal government to create it.
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Why This Matters Now
The AHA's submission doesn't exist in isolation. It arrives alongside several converging regulatory signals:
The FDA is shifting from pre-market review to post-market surveillance for adaptive AI systems. As reported in Forbes on February 24, 2026, the agency is moving safety responsibility from regulators to the health systems operating these tools in clinical settings.
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CMS has deployed approximately 2,000 new auditors with a 7-year lookback window on clinical documentation — documentation that, in many health systems, is already being influenced by ambient AI tools, clinical decision support systems, and AI-assisted coding platforms.
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The Black Book 2026 Healthcare AI Governance Study, covering 384 organizations, found that 76% of health systems cannot fully reconstruct AI-influenced clinical decision chains. The data connecting AI output to clinical action — the metadata that would demonstrate how a decision was made, not just what was decided — isn't being captured.
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The AHA's submission ties these threads together. In their words, the "black box" nature of AI systems "can make it more challenging for hospitals and health systems to identify flaws in models that may affect the accuracy and validity of an AI tool's analyses and recommendations."
Three Implications for Health System Leaders
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1. Post-deployment governance is becoming a regulatory expectation, not a best practice.
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The AHA called for "performance metrics, evaluation thresholds and communication requirements for ongoing performance to end users." When the nation's largest hospital trade association asks HHS to formalize these standards, it signals that voluntary governance frameworks will eventually become compliance requirements. The question isn't whether standards are coming — it's whether your organization builds the infrastructure proactively or retrofits it reactively under federal timelines.
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2. The governance gap is structural, not procedural.
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The AHA noted that 74% of hospitals already use multiple teams to evaluate predictive AI, including senior leaders, department heads, and IT staff. Yet the submission still calls for additional standards — because having teams that evaluate AI is not the same as having infrastructure that governs it operationally. The gap isn't in awareness or intention. It's in the operational architecture: decision authority frameworks, documentation protocols, audit trail systems, and supervision structures that function at the speed of clinical workflows.
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3. Mid-market health systems face disproportionate exposure.
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The AHA explicitly acknowledged that "rural, critical access and other safety net hospitals may not have the staff or resources to support governance structures and ongoing measurement activities." This is the digital divide applied to AI governance — and it extends well beyond rural hospitals. Any health system that has deployed AI tools without a dedicated internal governance team faces the same structural vulnerability. The organizations with the fewest resources to build governance infrastructure are often the ones with the most to lose from its absence.
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The Liability Dimension
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The AHA's submission also addressed the liability ambiguity that's becoming increasingly urgent. They noted that "many providers are concerned about liability ambiguity, particularly when AI algorithms could result in inaccurate recommendations that lead to poor outcomes."
This concern maps directly to the dual bind now facing clinicians: potential liability if they rely on AI and it produces flawed recommendations, and potential negligence claims if they fail to use AI tools that are becoming the standard of care. Neither side of that equation is resolvable without governance infrastructure that makes AI-influenced decisions defensible — with documentation that exists at the point of decision, not reconstructed after an adverse event.
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What Should Health Systems Be Assessing?
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For organizations evaluating their current governance posture, the AHA's submission points to several critical domains:
- Decision authority documentation. When a clinician accepts or overrides an AI recommendation, is the basis for that decision captured? Is there a clear record of who held decision authority and how it was exercised?
- AI decision chain reconstruction. If CMS audits clinical documentation that was influenced by AI, can your organization demonstrate how AI shaped the output? Not just what was documented — but how AI contributed to the clinical reasoning behind it.
- Post-deployment monitoring infrastructure. Beyond initial validation, does your organization have ongoing measurement and evaluation processes for AI tools in production? The AHA called for these standards to include "performance metrics, evaluation thresholds and communication requirements" — a useful benchmark for self-assessment.
- Vendor accountability frameworks. The AHA stated that "third-party vendors must be responsible for the ongoing integrity of the tools they sell." Does your organization have contractual and operational mechanisms to hold AI vendors accountable for model performance, transparency, and post-deployment support?
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The Window Is Open
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The AHA's submission creates a clear before-and-after moment. Before February 23, 2026, post-deployment AI governance was a forward-looking concern. After it, the industry's largest trade association has formally asked the federal government to make it a regulatory standard.
Health systems that build governance infrastructure now will define the operational frameworks that become industry benchmarks. Those that wait will inherit compliance mandates designed without their input.
The regulatory trajectory is clear. The only variable is whether your organization leads or follows.
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Jeffrey Pedone is the Founder and CEO of Pedone AI Advisors, a healthcare AI governance consultancy focused on post-deployment operational frameworks. He is a Contributing Researcher on the Black Book 2026 Healthcare AI Governance Study and brings 30+ years of enterprise technology operations experience across regulated, high-stakes industries including government, healthcare, and critical infrastructure.
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