Clustered 2 filings across 2 jurisdictions · filing window Nov 16, 2018 → Nov 20, 2018. View entity profile → Other incidents for this victim →
incident inc_f40e5bde8f8d4517 · 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.
CA MT
all State AG
Earliest sighting first · deep chronology in Litigation Timeline
May 8, 2018
When the intrusion reportedly occurred, per the linked filings
Aug 1, 2018
Reported by CALIFORNIA AG filing
El Centro Regional Medical Center (ECRMC) reported a data breach to the Montana Attorney General. The breach was reported on 2018-11-16. The breach occurred from 5/8/2018 to 5/11/2018. 5 Montana residents were affected.
Affected (this filing): 5
El Centro Regional Medical Center (ECRMC) reported a data breach involving its third-party vendor, Jobscience. Between May 8 and May 11, 2018, an unknown third party stole electronic information from Jobscience's servers containing job applicant data, including names, addresses, phone numbers, dates of birth, usernames, passwords, email addresses, and Social Security Numbers. ECRMC engaged forensic investigators and the FBI. Affected individuals were offered 24 months of credit monitoring.
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.