What This Tool Does
We do not just know which facilities have problems — we know which counties have the highest concentration of under-staffed, citation-heavy facilities. We stack-target families in those areas with creative that speaks directly to the fear every family has when a loved one is in a nursing home. The result is a campaign audience that is emotionally primed and geographically precise before a single ad is placed.
Chain intelligence aggregates data across ownership chains. Five Special Focus designations in one chain is a corporate policy failure case, not an individual facility case — and it changes both the legal theory and the campaign strategy.
Data Sources
- CMS OSCAR (Online Survey, Certification and Reporting System) — Complete deficiency citation records for all 15,000+ Medicare-certified nursing facilities, including severity levels, scope, and repeat citation history.
- CMS Payroll-Based Journal (PBJ) — Actual staffing hours reported payroll data versus self-reported staffing figures. The gap between what facilities report and what they actually staff is a core negligence indicator.
- CMS Five-Star Quality Rating System — Composite facility ratings including health inspections, staffing, and quality measures.
- OIG Excluded Provider List — Facilities and individuals excluded from Medicare and Medicaid participation due to fraud, abuse, or neglect findings.
- Long-Term Care Ombudsman Complaint Data — State-level complaint records from the Long-Term Care Ombudsman programs, including substantiated complaints by category.
- CMS Special Focus Facility (SFF) Program — Facilities with persistent serious quality problems, designated for enhanced federal oversight.
The Facility Scoring Model
Each facility receives a plaintiff opportunity score from 0 to 100, weighted across five dimensions: deficiency citation frequency and severity (30%), staffing gap between PBJ actuals and self-reported figures (25%), OIG exclusion history (20%), ombudsman complaint substantiation rate (15%), and SFF designation history (10%).
Facilities scoring above 70 are flagged as HIGH priority. Chain-level aggregation identifies ownership groups with systemic problems across multiple locations — critical for establishing corporate liability.
"Five Special Focus designations in one chain. That is not an individual facility case — that is a corporate policy failure. The federal government documented it. MTAA built the engine to find it."
— MTAA Platform DocumentationWhat the Export Produces
- Facility scorecard — deficiency history, staffing gap analysis, OIG status, SFF designation
- Chain intelligence report — ownership chain analysis across all affiliated facilities
- County concentration map — geographic density of high-score facilities by county
- Family demographic profile — age, income, and family composition of residents' typical family members in the facility catchment area
- Competitive landscape — existing plaintiff firm activity in the facility's geographic market
- Campaign targeting parameters — geo-radius parameters and demographic callout recommendations
Campaign Use Cases
Elder Abuse / Neglect
Target families in the radius of facilities with documented abuse and neglect citations. Mae-register creative (empathy/connection) consistently outperforms anger-register for family members of residents.
Wrongful Death
SFF-designated facilities with repeat deficiency patterns are high-value wrongful death targets. Chain ownership enables multi-facility campaigns with unified corporate liability theory.
Financial Exploitation
Ombudsman complaint data includes financial exploitation categories. Target family members in facilities with substantiated financial complaint histories.
Understaffing / COVID
PBJ staffing gap data identifies facilities that systematically under-staffed during COVID surges. The gap between reported and actual staffing is the liability hook.
The Nursing Home Discovery Engine is available exclusively to plaintiff law firms working with MTAA on active campaigns.