GovKM

Healthcare

Healthcare, clinical care, public health, medical AI, health data, payer, provider, and regulated health decision environments.

Related Articles

Explore GovKM research, case studies, and analysis related to this sector.

Organizational continuity framework showing a finalized fraud finding crossing an organizational boundary into reporting, financial action, records, and downstream institutional state.
Consequence Continuity

A Finding Is Not Complete Until Its Consequences Propagate

HHS OIG found cases where California Medicaid fraud determinations did not reliably become the required downstream reporting and financial actions. The case establishes consequence continuity: a finding is not institutionally complete until its required effects propagate across organizational boundaries into dependent decisions, actions, and records.
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Information continuity framework showing a regulatory drug-status change propagating through labeling, FDA data, CMS formulary records, coverage decisions, payments, and future reuse.
Lifecycle Continuity

The Drug Changed Status. The Government’s Information Did Not.

HHS OIG found $587.7 million in ineligible Medicare Part D payments after drug status changes failed to propagate consistently through FDA data, labeling, CMS formulary records, and downstream payment logic. The case demonstrates that authoritative state changes are dependency events, not local database updates.
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Governance architecture illustrating the evidentiary chain from policy authority through context and decision to action and the record needed to prove compliance.
Government Governance

The Rule Existed. The Institution Could Not Prove the Decision Followed It.

An HHS OIG audit of Arizona Medicaid shows that policy authority is not operational governance by itself. Institutions must preserve evidence showing how governing requirements actually constrained decisions and actions.
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GovKM infographic explaining why AI provenance is not the same as evidence. It contrasts traceable source origin with evidence fitness for purpose, showing how authentic, current, and fully traceable information can still produce an institutionally invalid
Artificial Intelligence and Institutional Continuity

Provenance Is Not Evidence: Why Traceable AI Can Still Be Institutionally Wrong

Provenance proves where information came from; it does not prove that the information is sufficient, authorized, or contextually appropriate evidence for a particular institutional decision. Trustworthy AI must preserve the transition from Source to Evidence and then qualify that evidence through Authority and Context.
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Healthcare AI governance infographic showing the Continuity Topology from Source and Evidence through Authority, Context, Decision, Action, Record, Institutional Memory, and Future Reuse, illustrating how data provenance must remain connected to institutio
Organizational Continuity

Provenance Is Necessary, but Clinical AI Needs Continuity

Healthcare AI governance increasingly recognizes data provenance as a minimum trust requirement. But provenance alone cannot preserve the institutional relationships that connect clinical evidence to authority, context, decisions, actions, records, and responsible future reuse.
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CONTINUITY CONTEXT

Return to the Continuity Foundations

Sector-specific evidence is one view into the broader GovKM continuity model. Continue into the Framework, Research, Articles, OrgAI, or GAIB to understand the relationships behind these applications.