Michael R. Schwartz, MD Inc. reported to HHS on 2025-10-23 a Hacking/IT Incident affecting 9809 individuals. Breached information located on Desktop Computer.
Affected (this filing): 9,809
Clustered 3 filings across 1 jurisdiction · filed Oct 23, 2025. View entity profile → Other incidents for this victim →
incident inc_9a457e6ff0234434 · merge_method deterministic · confidence 100%
Discovered → first regulatory filing
Time between earliest and latest filing
Not recorded for this incident
Discovery variance · Leak precedence · Materiality delta · SEC filing delay — no leak-site claim in this cluster; no SEC 8-K in this cluster; needs two dated filings.
PHI
CA
HHS OCR · State AG
Earliest sighting first · deep chronology in Litigation Timeline
Jan 20, 2025 → Aug 26, 2025
When the intrusion reportedly occurred, per the linked filings
Aug 25, 2025
Reported by CALIFORNIA AG filing
Michael R. Schwartz, MD Inc. reported to HHS on 2025-10-23 a Hacking/IT Incident affecting 9809 individuals. Breached information located on Desktop Computer.
Affected (this filing): 9,809
Michael R. Schwartz, M.D., Inc. experienced unauthorized remote access to an office computer containing patient personal and health information. The intrusion occurred between January 20, 2025, and August 26, 2025, and was discovered on August 25, 2025. Affected data included names, Social Security numbers, addresses, emails, phone numbers, photographs, and medical record numbers. The organization secured systems, engaged cybersecurity experts, replaced hardware, and offered 12 months of identity monitoring.
HHS OCR breach portal entry: Michael R. Schwartz, MD Inc., a California healthcare provider, reported a Hacking/IT Incident affecting 9,080 individuals, submitted 2025-10-23. The breached information was located on a Desktop Computer. No business associate was indicated. The portal row provides no narrative description of root cause, threat actor, data types beyond PHI inferred from OCR scope, or remediation.
Affected (this filing): 9,080
About this clustering
DisclosureLens links filings into incidents through layered matchers: deterministic rules (same source document, multistate filings of one breach, tight-window same-victim pairs), a weighted-similarity scorer for cross-source candidates, and an operator review queue for everything uncertain. Each link records its own method and confidence — shown per filing in the timeline below. The system defaults to NOT merging when uncertain, because a false merge (collapsing two unrelated breaches) is more harmful than a false split (showing related filings separately); uncertain pairs route to human review instead of auto-merging. Filing summaries shown in the timeline are AI-generated extracts — verify each against its linked source.