Buy-side and sell-side healthcare M&A, in one conversation

“Now layer in litigation exposure. Pull every malpractice, employment, and billing case filed against this hospital in the last 10 years from federal and state courts. Flag any active class actions and summarize the 5 largest settlements.”

Searches 10M+ federal court opinions + state case dockets by hospital name and parent entity, extracts settlement amounts and active litigation from full-text opinions.

“Map their entire referral network. Which physicians send patients here? How many are employed vs. independent? If the top 10 referrers retire or leave, what percent of admissions disappear?”

Uses CMS Medicare Shared Patient Patterns to quantify referral concentration risk, the #1 overlooked variable in hospital valuations.

“Run our distress model. Given their margin trajectory, payer mix, and CMS quality scores, what’s the probability they default within 36 months? Benchmark against the 47 nonprofit hospital bankruptcies since 2020.”

Predictive model trained on historical bankruptcies (Steward, Mercy Iowa, Hahnemann) to quantify deal risk under status-quo operations.

“For the top 20 targets, pull every physician’s industry-payment receipts. Which groups have disproportionate device-manufacturer relationships? Those rebate streams don’t survive PE ownership, haircut the EBITDA accordingly.”

Cross-references industry payments (pharma/device payments to physicians) with billing patterns to identify revenue that’s actually kickback-adjacent and won’t transfer.

“Screen all 340 physicians across the top 20 targets against our 130-source compliance database, OIG, SAM, FDA debarment, every state board, NPDB, CA DCA, TX TMB. Produce a risk score per target and flag deal-killers.”

Batch compliance screening across 130+ federal/state/enforcement sources with A–F risk grading, catches disciplinary history that standard credentialing misses in 60%+ of multi-state physicians.

“Benchmark each target’s top producers against state peers. Flag physicians whose per-procedure billing exceeds the 95th percentile, that’s revenue at risk from RAC audits or post-close billing normalization.”

Compares physician-level CPT billing against state and national distributions to quantify how much of the target’s revenue is sustainable vs. outlier-dependent.

“For each target, pull the CMS ASC quality reporting record. Flag facilities with patient-safety deficiencies, readmission outliers, or ASCQR non-participation, those are the ones where the quality story breaks the deal.”

Cross-references the POS iQIES registry against CMS ASC quality measures to separate strong operators from facilities that are cheap for a reason.

“Score each target on PE-readiness: OR count (scale), physician owner count (governance complexity), and state certificate-of-need regime (barrier to new entry). Produce a weighted rank.”

The three variables that actually drive ASC roll-up economics: capacity, ownership-consolidation friction, and whether the market is protected from greenfield competition.

“Map the payer mix. For each ranked target, pull every commercial insurer that has the ASC in-network, and benchmark their case rates against the metro 50th percentile. Which facilities are negotiating above market?”

Joins POS iQIES facility addresses against parsed payer-side negotiated-rate filings from major national carriers to quantify pricing leverage, the EBITDA lever that actually expands post-close.

“Calculate market concentration by practice group. Is this a 3-player market or fragmented? Show HHI and top-4 share, and which groups have grown via acquisition vs. organic hiring over the last 5 years.”

Computes Herfindahl-Hirschman Index by practice affiliation and tracks physician movement across tax IDs over time to distinguish rollup players from organic growers.

“Identify the KOLs. Who gets the highest speaker/consulting fees from device manufacturers, has the most peer-reviewed publications, and performs the highest-complexity procedures? If the dominant group’s top 3 KOLs walked, how much revenue and referral gravity would they take?”

Fuses industry payments, PubMed authorship, and CMS procedure complexity to identify irreplaceable physicians, the real retention risk in any PE thesis.

“Run the aging curve. What percent of cardiologists in this metro are within 10 years of likely retirement, by practice group? Which groups face a supply cliff, and how does that reshape the acquisition thesis?”

Uses medical-school graduation year across every physician in the metro to surface workforce cliffs that can erode a PE hold-period exit multiple.

“Which plaintiff firms are winning these cases? Pull every verdict and settlement over $1M, identify the plaintiff’s counsel, and rank by win rate and average recovery. Who are the repeat players?”

Extracts attorneys of record from full opinion texts across 10M+ cases, identifies the MDL-driving firms before they become a defense problem.

“Forecast the next class. Which failure modes in MAUDE are accelerating but haven’t generated litigation yet? That’s the next MDL, when does our client need to reserve?”

Statistical trending on MAUDE event rates by failure mode, correlated against historical patterns of MDL formation (typically 18–36 months after adverse-event inflection).

“For every physician included, pull negotiated rates across the 5 largest competing payers. How much below market are we contracting at, and where are we above? Flag any physician we’re overpaying by more than 20%.”

Benchmarks every physician’s rates against competing payer contracts across the major national carriers.

“Retention risk: for the network we just designed, which physicians are most likely to leave in the next 24 months? Score by age, practice affiliation churn, malpractice frequency, and industry-payment trajectory.”

Predictive retention model using five signal sources, matters because a narrow network that loses its core providers in year 1 fails CMS network adequacy re-review.

“For the flagged physicians, pull the full compliance screen, NPDB, OIG LEIE, state boards in every state they’ve been licensed, CMS revocations, DEA actions. Score each one A–F.”

Screens across all 130+ compliance sources and produces defensible A–F risk grades, the output compliance counsel needs for a Stark/AKS self-disclosure analysis.

“If we have to terminate the 5 highest-risk physicians, what happens to hospital volume? Model the referral loss, downstream admissions, and ancillary revenue impact, and how quickly independent competitors would absorb the displaced patients.”

Referral-network simulation quantifies the real cost of compliance action so leadership can decide between self-disclosure, termination, or remediation with eyes open.

“Surface anything ugly on the founders or principal physicians, license actions, malpractice payments, prior bankruptcies. We’d rather know before the buyer’s diligence does.”

Screens the sponsor and named physicians against NPDB malpractice, board orders, SAM/OIG enforcement, and bankruptcy filings, with cited source documents on every flag.

“Build the buyer list. Strategic acquirers actively expanding in ASC platforms, PE sponsors with healthcare-services funds, and second-wave consolidators. Rank by deal velocity over the last 18 months.”

Cross-references active healthcare-services investors, recent transaction history, and stated investment mandates to produce a ranked bidder list with deal-velocity scoring.

“For the top 10 buyers, pull every healthcare-services investment in the last 24 months with announced sizing. Which ones are actively raising a new fund? Those are the buyers with deployment pressure.”

Surfaces each fund’s recent transaction record, paired with public Form D filings, to flag which sponsors are mid-raise and need to deploy.

“Pull the principal investors’ track records on dental or DSO platforms. Names, deal year, exit type if any. The bid that wins is usually the one whose principal already knows the market.”

Screens PE fund leadership against closed transactions in the dental and DSO verticals to surface named partners with platform-level vertical experience.

“For each compliance flag found, draft the management response. Are these resolved, in process, or live? For each live item, what’s the remediation plan, and what’s the timeline to closure pre-LOI vs at signing?”

Maps each flag against the data room narrative, separates resolved-and-disclosed from active-and-unresolved, prepares management talking points before the buyer’s call.

“Stress-test the EBITDA. If the buyer applies a haircut for any concentration risk above 15% from a single payer, what’s the resulting valuation? Pre-empt the rebuttal we’ll need at the negotiation.”

Pulls payer mix from billing data, models buyer haircut scenarios at multiple concentration thresholds, outputs the rebuttal-ready bid floor for sponsor negotiation prep.