Clustered 2 filings across 2 jurisdictions · filing window Dec 20, 2011 → Dec 21, 2011. View entity profile → Other incidents for this victim →
incident inc_52e13e70676241b9 · merge_method deterministic · confidence 100%
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 · Identity (basic) · Government ID
NH TN
HHS OCR · State AG
Earliest sighting first · deep chronology in Litigation Timeline
State of New Hampshire Attorney General's office filed a breach notification on 2011-12-20 involving Aegis Sciences Corporation. The provided document source is corrupted or contains only structural placeholders, preventing extraction of specific incident details, affected data types, or individual counts.
Aegis Sciences Corporation, a Tennessee healthcare provider, reported to HHS OCR that a laptop and an unencrypted external hard drive containing ePHI of 2,185 individuals were stolen from a workforce member's vehicle. Compromised data included Social Security numbers, driver's license numbers, demographic information, bank account information (14 individuals), and credit card information (3 individuals). The CE filed a police report, hired a private investigator, offered credit monitoring, and after OCR investigation completed a risk analysis, encrypted all employee computers and removable media, and retrained employees.
Affected (this filing): 2,185
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.