Clustered 2 filings across 1 jurisdiction · filing window Jul 21, 2017 → Aug 25, 2017. View entity profile → Other incidents for this victim →
incident inc_e13278920ca644cd · merge_method llm · confidence 43%
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.
Health (basic) · Identity (basic) · Government ID
NY
all HHS OCR
Earliest sighting first · deep chronology in Litigation Timeline
Kaleida Health reported to HHS on 2017-07-21 a Hacking/IT Incident affecting 2789 individuals. Breached information located on Email. Employees were victims of an email phishing scheme affecting ePHI including names, medical record numbers, dates of birth, diagnoses, and treatment information. The entity notified HHS, individuals, and media, and retrained staff.
Affected (this filing): 2,789
Kaleida Health (Buffalo, NY) reported to HHS OCR on 2017-08-25 a Hacking/IT Incident affecting 744 individuals. Numerous employees were victims of an email phishing scheme that compromised ePHI including names, medical record numbers, dates of birth, diagnoses, Social Security numbers, health insurance information, and treatment information. Breached information was located in Email. No business associate was involved. The CE notified HHS, affected individuals, and the media, revised policies and procedures, and retrained its workforce. OCR obtained assurances corrective actions were implemented.
Affected (this filing):
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.