Legal & Medical Malpractice
The plaintiff intake screen that builds the demand. The defense first-48-hours pack that scopes the motion. 10M+ court cases with full opinion text. Provider disciplinary history across 50 states + DC. Peer comparison for standard of care. Hospital staffing and financial trends for negligence. Pharma payment conflicts for bias evidence. All in one conversation, in seconds.
The wedge
Westlaw covers case law. Lex Machina covers dockets. Expert Institute covers experts. Tracers covers identity. Each is good. None of them join the four together against 9.4M providers and 273K facilities the way a med-mal lawyer needs. These are the joins that change cases.
Provider to facility to ownership chain
From a single NPI, walk up through every practice location to the hospital to the parent system to the PE sponsor. This is the joinder map. No legal research tool surfaces it.
How litigators use it: For the plaintiff: identifies the deep-pocket defendant before the demand. For the defense: clarifies which entities the carrier needs to coordinate with on the response.
50-state regulatory history aggregated
A doctor disciplined in Florida who moves to Idaho rarely shows up in an Idaho-only board check. The aggregated 50-state view catches what state-by-state checks miss.
How litigators use it: For the plaintiff: catches negligent-credentialing exposure. For the defense: surfaces what plaintiff's counsel will dig up on cross.
Industry payments tied to prescribing and billing
Pharma and device payments to the named provider, joined to the actual prescriptions or devices billed. The Stark/AKS evidence and the financial-motive evidence in the same query.
How litigators use it: For the plaintiff: anchors the bias narrative. For the defense: identifies impeachment risk on opposing experts.
Expert witness deposition history with case outcomes
Every prior case where the expert testified, what side they took, whether testimony was excluded under Daubert, what the verdict was. The vetting pack a senior litigator builds in two days.
How litigators use it: For both sides: vet the opposing expert before deposition; vet your own expert before retaining them.
Hospital cost reports + CMS quality + deficiencies
For negligent credentialing or understaffing claims, the financial trajectory and deficiency record of the facility are the supporting evidence. Cost reports, HCAHPS, readmissions, and inspection deficiencies in one query.
How litigators use it: For the plaintiff: builds the systemic-failure narrative. For the defense: frames the standard-of-care argument with peer-relative data.
Joinder map
The named physician is rarely the defendant with assets. The parent system is. Medistill walks the chain from a single NPI through every affiliated facility, returns the system roster with combined revenue, and tells you where the joinder target actually is.
Live query
“Walk the ownership chain for HCA Healthcare. Show me every affiliated facility with state, beds, revenue, and quality rating. Roll up totals.”
Facilities
188
States
39
Total beds
22,625
Total net patient revenue
$30.1B
Avg star rating across the system: 3.1. Mix of acute care, children's, and specialty facilities.
| Facility | State | Beds | Net revenue | Star |
|---|---|---|---|---|
| Tallahassee Memorial Healthcare | FL | 468 | $986M | 2 |
| Children's Healthcare of Atlanta at Scottish Rite | GA | 319 | $985M | — |
| HCA Houston Healthcare Kingwood | TX | 567 | $966M | 2 |
+ 185 more facilities returned in the live response.
For the plaintiff side
The 30-second intake screen that builds the demand. Disciplinary history, billing anomalies, peer comparisons, and industry payment conflicts pulled from live CMS, Medicare, and state board data, in one conversation.
What the 30-second intake screen returns
Read time on the output: 30 seconds. The rest of the intake call goes to questions worth asking, not typing names into seven government websites. Below is what the full report looks like on a real provider.
Query
“I'm representing a patient in a malpractice case against Nadia Hassani, MD in LA. I need everything you can find on this doctor - disciplinary history, prior malpractice payouts, how their billing compares to others in the same specialty, the quality scores at his hospital, and any compliance red flags.”
For litigation support purposes | Prepared April 4, 2026
| NPI | 1902166566 |
| Specialty | Thoracic Surgery (Cardiothoracic Vascular Surgery) |
| Gender | Female |
| License (AZ) | #58198, Active |
| Record address | 4733 W Sunset Blvd, Fl 3, Los Angeles, CA 90027 |
| Active practice location | 5777 E Mayo Blvd, Phoenix, AZ 85054 (Mayo Clinic Hospital, payer-verified as of March 2026) |
| NPI enumerated | May 2012 |
| Medicare participating | Yes |
Composite Risk Score: 90/100, Grade F Peer Percentile: 91.5th (composite of 130+ enforcement sources, NPDB payment history, state board orders, hospital deficiencies, and industry payments — she scores riskier than ~91.5% of peers in her specialty and state)
Arizona Medical Board, DISCIPLINE FLAG
California Medical Board
OIG / Federal Exclusions
All payments are from a single company: Intuitive Surgical, Inc., maker of the da Vinci robotic surgery system.
| Year | Nature | Amount | # Payments |
|---|---|---|---|
| 2021–2022 | Education | $4,000 | 2 |
| 2023 | Education | $4,000 | 2 |
| 2023 | Travel & Lodging | $4,728 | 15 |
| Total | ~$12,728 | 19 |
| Year | Beneficiaries | Services | Submitted Charges | Medicare Paid | Charge/Bene |
|---|---|---|---|---|---|
| 2019 | 34 | 72 | $43,075 | $20,900 | $1,267 |
| 2020 | 111 | 309 | $268,019 | $72,473 | $2,414 |
| 2021 | 151 | 365 | $177,748 | $84,914 | $1,177 |
| 2022 | 159 | 365 | $583,680 | $72,879 | $3,671 |
| 2023 | 148 | 346 | $609,961 | $74,942 | $4,122 |
| Metric | Dr. Hassani (2022) | CA Peer Average | Ratio |
|---|---|---|---|
| Submitted charge / beneficiary | $3,671 | $4,921 | 0.75x (below avg) |
| Services / beneficiary | 2.30 | 3.12 | 0.74x (below avg) |
| Avg patient risk score | 1.43 | 2.06 | 0.69x (lower acuity) |
In 2022, her charge-per-beneficiary was below the California peer average. However, her patient risk score was also significantly lower than peers (1.43 vs. 2.06), meaning she was treating less complex patients but approaching peer billing levels, which can be a flag for upcoding or overutilization relative to case complexity.
Her Medicare patients consistently present with: hypertension (75%), hyperlipidemia (75%), cancer (44–64%), COPD (39–44%), chronic kidney disease (22–38%), atrial fibrillation (23–35%). This is a high-acuity surgical population, which is relevant context for outcome expectations.
| Quality Metric | Score |
|---|---|
| CMS Overall Star Rating | ⭐⭐⭐⭐⭐ (5 stars) |
| Mortality, Better than national | 4 measures |
| Mortality, Worse than national | 0 measures |
| Safety, Better than national | 3 measures |
| Safety, Worse than national | 0 measures |
| Readmissions, Better than national | 2 measures |
| Readmissions, Worse than national | 0 measures |
| Issue | Finding | Priority |
|---|---|---|
| AZ Medical Board discipline | Confirmed, details must be pulled from AZMD | High |
| Intuitive Surgical payments | $12,728 across 19 payments (2021–2023) | Medium |
| Billing charge inflation | 3.3x increase in submitted charges/bene (2019–2023) | Medium |
| Lower-acuity patients, near-peer billing | Risk score 30% below CA peers in 2022 | Medium |
| OIG/Federal exclusions | None found | Clean |
| Medicare opt-out/fraud | None found | Clean |
| Current practice location | Phoenix, AZ (not LA), verify jurisdiction | High |
Follow-up query
“How do her outcomes compare to every other cardiac surgeon in LA? Is she an outlier on mortality, complications, or readmissions? I need the numbers.”
Important caveat: Medicare data does not contain individual surgeon mortality, complication, or readmission rates. Those are tracked at the hospital level by CMS and in private STS (Society of Thoracic Surgeons) registry data that isn't available by provider NPI. What we do have is every billing and utilization metric across all 62 LA-area thoracic surgeons.
On volume: Hassani ranked 21st out of 62, mid-tier. Not a high-volume outlier in either direction.
On billing: Despite treating lower-acuity patients, her submitted charges per beneficiary ($3,671) were close to the peer average ($5,166), not a gross outlier, but worth noting given the acuity gap. Her Medicare-paid-per-beneficiary ($459) was about half the peer average ($899), which reflects what CMS actually deemed appropriate, and is consistent with a lower-complexity panel.
On true outcome metrics: For mortality, complications, and readmission rates at the individual surgeon level, you need: (1) STS National Database, the gold standard for cardiothoracic surgery outcomes, subpoena-able via the hospital's quality records; (2) Hospital peer review records, Mayo Phoenix would have M&M conference data and adverse event reports; (3) CMS Hospital Compare, Mayo's mortality rates were better than national average on all four tracked measures, with zero measures worse than national. The most litigation-actionable path is a subpoena for her case-specific outcomes from the hospital's quality department, combined with an STS registry data request.
| Rank | Surgeon | City | Benes | Risk score | $/bene (paid) | $/bene (charged) |
|---|---|---|---|---|---|---|
| 1 | Park, William | Los Angeles | 889 | 2.07 | $465 | $3,696 |
| 2 | Reeves, Ryan | Santa Monica | 660 | 1.61 | $330 | $1,974 |
| 3 | Tanaka, James | Duarte | 321 | 1.83 | $474 | $2,102 |
| 4 | Brennan, Jason | Los Angeles | 287 | 1.67 | $722 | $6,854 |
| 5 | Grigorian, Vartan | Los Angeles | 281 | 3.12 | $351 | $2,672 |
| 6 | Nazari, Ali | Los Angeles | 252 | 3.48 | $812 | $4,815 |
| 7 | Dalton, Henry | Torrance | 241 | 3.41 | $966 | $5,912 |
| 8 | Mora, Daniel | Duarte | 234 | 1.93 | $345 | $1,385 |
| 9 | Shirazi, Farshad | Los Angeles | 232 | 2.50 | $1,688 | $7,854 |
| 10 | Fielding, Robert | Los Angeles | 224 | 2.55 | $428 | $2,479 |
| 21 | Hassani, Nadia← | Los Angeles | 159 | 1.43 | $459 | $3,671 |
| … | 41 more providers (62 total) | |||||
Follow-up query
“Now follow the money. How much is she getting from device companies and pharma? Could there be a financial conflict that influenced her clinical decisions?”
Source: industry payments | 2018–2024
Total Payments: $16,486 across 64 transactions, 11 companies
Zero pharma payments. 100% device companies. Zero research grants. Zero ownership stakes.
| Company | Total | # Payments | Years | Products |
|---|---|---|---|---|
| Intuitive Surgical, Inc. | $15,106 | 50 | 2019–2024 | Da Vinci SP, Da Vinci Xi, Da Vinci Surgical System |
| AtriCure, Inc. | $410 | 5 | 2019–2021 | AtriCure Cryosurgical System, CryoICE |
| Medtronic, Inc. | $298 | 2 | 2023 | Signia (surgical stapler) |
| Davol Inc. | $155 | 1 | 2024 | Progel Applicator Spray Tips |
| Boston Scientific | $154 | 2 | 2022 | EXALT Model D (bronchoscope) |
| Ambu Inc. | $137 | 1 | 2023 | (food, no product linked) |
| Abbott Laboratories | $133 | 1 | 2019 | MitraClip system |
| Covidien LP | $93 | 2 | 2018–2019 | Sonicision (ultrasonic dissector) |
| Type | Total | What it means |
|---|---|---|
| Education | $8,000 | Payments for attending or leading Intuitive-sponsored training events |
| Travel & Lodging | $4,728 | Intuitive covered travel to San Diego, Sunnyvale, La Jolla, Phoenix, Newark |
| Food & Beverage | $3,748 | Meals across 11 companies |
| Gift | $10 | One gift from Intuitive Surgical |
| Year | Total | Driver |
|---|---|---|
| 2018 | $21 | Covidien meal |
| 2019 | $731 | Intuitive meals + AtriCure + Abbott MitraClip |
| 2020 | $136 | AtriCure only |
| 2021 | $1,405 | $1,000 Intuitive education + meals |
| 2022 | $3,455 | $3,000 Intuitive education (da Vinci Xi) + meals |
| 2023 | $10,214 | Peak year, $4,000 education + $4,728 travel (15 trips) + meals |
| 2024 | $525 | Intuitive meals continuing into 2024 |
The escalation from $136 in 2020 to $10,214 in 2023 is the most striking pattern in this data.
Concentration: 91.6% of all payments ($15,106 of $16,486) came from a single company, Intuitive Surgical, maker of the da Vinci robotic surgery system. This is not a doctor with scattered relationships; this is a doctor with a primary industry affiliation.
Product specificity: Every Intuitive payment was tied to the da Vinci Surgical System (standard and Xi variants) and the da Vinci SP (single-port system). These surgical robots cost hospitals $1–2.5M each and generate ongoing per-procedure fees. Intuitive pays surgeons like Hassani to train other surgeons on the robot, which drives adoption and revenue.
The education payments are the key line items. Three payments totaling $8,000, in July 2021, March 2022, and February 2023, were categorized as “Education” and tied specifically to the da Vinci Xi and da Vinci SP systems. This means she was likely serving as a proctor or faculty trainer for Intuitive, proctoring other surgeons using the robot. That relationship creates a material incentive to use and advocate for da Vinci systems.
The 2023 travel pattern is significant. Intuitive covered 15 separate travel events for her in 2023, including trips to San Diego (multiple times), Sunnyvale (Intuitive's HQ), La Jolla, Phoenix, and Newark. The third-party payment structure is also worth examining in deposition.
If the case involves robotic surgery: If your patient's procedure involved a da Vinci robot, the conflict argument is direct: she received ongoing compensation from the device manufacturer both before and during the treatment period.
If the case involves atrial fibrillation treatment: The AtriCure payments (cardiac ablation devices) and the Abbott MitraClip payment (mitral valve repair) add a secondary layer of potential conflict depending on the specific procedures at issue.
What this doesn't show: No research payments, no ownership interest, no consulting agreements formally disclosed. That doesn't mean consulting agreements don't exist; they may be structured differently or disclosed under different categories. A deposition demand for all contracts with device companies from 2018–2024 is warranted.
Recommended discovery: Request all proctoring and consulting agreements with Intuitive Surgical via discovery; depose on whether she disclosed her Intuitive relationship to the patient pre-operatively; examine operative records to determine whether the da Vinci system was used and whether it was necessary for the patient's specific case.
For the defense side
The demand letter arrives Monday. The carrier wants a coverage opinion by Wednesday. Medistill compresses the defendant audit, the opposing expert vetting, the venue read, and the precedent check into one conversation, with primary-source citations the response writes itself off of.
Scenario
Cardiologist client at a Houston hospital. Demand alleges arrhythmia misdiagnosis, ICD failure, and consequential anoxic injury. Plaintiff demand $5M. Plaintiff's named expert: Douglas Zipes, MD, cardiology, Indianapolis IN. Carrier needs a coverage opinion in 48 hours.
Defendant audit
“Pull the full regulatory record for our client (NPI 1XXXXXXXXX). Every prior NPDB report, every state action, every industry-payment line, every CMS quality flag at the Houston facility. Build me a timeline.”
Opposing expert vetting
“The plaintiff’s expert is Douglas Zipes, NPI 1902860752, cardiology, Indianapolis. Pull every prior case naming him as expert, every Daubert challenge, every published article on ICD therapy. Flag testimony excluded or limited.”
Venue analytics
“This will be filed in Harris County. Pull the last 7 years of TX cardiology malpractice payments, median, average, max, and trend. Where do TX MD payments sit relative to NY and CA?”
Precedent check
“The plaintiff is leading on informed consent. Confirm Canterbury v. Spence (464 F.2d 772) is still controlling and surface every TX case that distinguishes or follows it in the last 15 years.”
For carrier coverage opinion | Prepared May 2, 2026
| Expert | Douglas Zipes, M.D. |
| NPI | 1902860752 |
| Specialty | Internal Medicine, Cardiovascular Disease |
| Practice | Indianapolis, IN, ZIP 46202 |
| Risk score | 0 / 100, Grade A (clean across all 147 sources) |
| Compliance flags | None. OIG, SAM, FDA debarment, FDA CLIIL, NPDB anonymized, all 50 state boards, all clear. |
| Court cases as named party | None. Witness, not litigant. |
Source: NPDB Public Use File, malpractice payment reports filed against MDs (license field 10), all years.
| State | Payments | Avg payment |
|---|---|---|
| New York | 23,950 | $274,038 |
| California | 19,819 | $130,459 |
| Pennsylvania | 14,679 | $235,733 |
| Texas (your venue) | 12,386 | $191,553 |
| Florida | 12,253 | $228,430 |
| Case | Canterbury v. Spence, 464 F.2d 772 (D.C. Cir. 1972) |
| Status | Still good law. Not overruled. Foundational reasonable-patient standard for informed consent. |
| Citing cases | Multiple TX cases follow the reasonable-patient framework. No TX appellate decision has explicitly distinguished or rejected it. |
| Related precedent | Sabo v. Canterbury (4th Cir. 2006), 173 F. App'x 265, citing the original framework. |
Total time, demand letter to coverage opinion: under 90 minutes.
What the data actually shows
The numbers below come straight from the live data. Run them on your own NPI, your own venue, your own state. The platform answers in seconds.
Live screen output
“Run a full intake screen on Christopher Duntsch, NPI 1790916393.”
Public-record example. The screen returns 5 flags across 2 states and 1 federal exclusion in under a second.
NPDB malpractice payment reports by origin year
The 2019 to 2020 spike is a one-time HRSA backlog clearing. Post-2020 the steady state is roughly 37K to 42K newly reported payments per year nationwide.
Top states by NPDB malpractice volume
Eight states account for roughly half of all named-payment reports in the file. New York alone holds 588K. The venue concentration tells you where the market actually is.
Top appellate venues for medical-expert testimony
Cases naming a medical expert in the opinion text concentrate in eight courts. If you are vetting an opposing expert, prior testimony is most likely to surface in one of these.
What you can ask
Each question runs against real data. Follow-ups build on previous results. Medistill remembers context across the entire conversation. The same eight workflows below are run by plaintiff intake teams and defense first-48-hours responders, often in parallel on the same case.
Expert witness vetting
“I’m vetting an expert witness for a cardiac surgery malpractice case. Pull everything on them, billing history, quality scores, license status in every state, malpractice history, and industry payments. I need to know if this expert will hold up under cross-examination.”
Full background on an expert witness in seconds, billing, discipline, payments, quality, before opposing counsel finds it first.
“They’ve got 3 disciplinary actions in 2 states. What were they for and are they resolved? Opposing counsel is definitely going to bring these up.”
Cross-state disciplinary history with resolution status, know the full picture before deposition.
“How does their surgical volume actually compare to peers? If they’re claiming to be a high-volume surgeon but only doing 50 cases a year, that undermines their credibility as an expert.”
Peer volume benchmarking exposes or validates the expert’s claimed experience level.
“What about their prescribing? Map their plan drugs to adverse event data, if the expert witness is a heavy prescriber of a drug with a growing safety signal, opposing counsel could use that to undermine their credibility.”
Prescribing-to-safety crosswalk finds patterns opposing counsel will exploit.
“Run their predictive risk score. If our expert witness scores in the top 10% for likelihood of future enforcement, opposing counsel could argue they’re not exactly a model physician. I need to know before we put them on the stand.”
Predictive risk scoring flags experts who look clean today but may not be tomorrow.
Without Medistill
Manually check each state board, separately pull Medicare claims, search OpenPayments by hand, hire a billing analyst. Weeks of paralegal time plus $5K–10K in expert review fees.
Opioid prescribing defense
“Our client is being accused of over-prescribing opioids. I need to show that their prescribing is within the normal range. Find 20 similar doctors, same specialty, same area, similar patients, and compare their opioid rates to our client’s.”
Automated peer comparison finds 20 similar providers and benchmarks opioid prescribing, the statistical defense that takes analysts weeks to build.
“Are there providers in the peer group who prescribe even more opioids and haven’t been sanctioned? That’s important for our defense, show me if any have DEA actions or board discipline.”
Finding unsanctioned higher-prescribers in the peer group is the core of a selective prosecution defense.
Without Medistill
Request Medicare Provider Utilization data from CMS, hire a pharmacoepidemiologist to build peer comparisons, manually cross-reference DEA and state board records. $15K–25K in expert costs.
Hospital upcoding defense
“We’re defending a hospital group accused of upcoding. Can you pull their DRG distribution and compare it to peer hospitals nationally? I need to see if they’re really outliers or if the government is cherry-picking.”
DRG distribution analysis against national peers, the data that separates legitimate billing from fraud.
“For the DRGs where they do look high, like 3x the peer rate, what’s the dollar exposure? I need to estimate the potential False Claims Act damages.”
Quantifies FCA exposure by DRG, the number opposing counsel will put in front of the jury.
“Could the community explain it? Map the hospital’s ZIP to county disease burden, if they’re in a county with 2x the national diabetes rate, some of the upcoding might be legitimate higher acuity. Show me the demographics side by side with the billing outliers.”
Community health data can justify billing patterns that look suspicious in isolation, the acuity defense.
Without Medistill
Subpoena CMS DRG data, hire a healthcare economist to build peer benchmarks, separately pull Census and CDC county data. 6-week engagement at $150K+.
Wrongful termination, credentialing retaliation
“A physician is suing their former employer for wrongful termination after they were denied credentials. Check every compliance database, was there a legitimate reason to deny them, or is this retaliation?”
Full compliance screening determines whether the employer had grounds, or whether the denial was pretextual.
“The employer said it was ‘quality concerns.’ Pull their MIPS scores and compare to the department average. Does the data back that up or not?”
MIPS benchmarking against department peers tests the employer’s stated justification with real data.
“Check if they were running any clinical trials at that employer. If they were a PI on active trials and got terminated, the employer may have disrupted federally funded research, that strengthens the retaliation argument.”
Clinical trial disruption adds a federal funding angle that escalates the retaliation claim.
Without Medistill
Manually check compliance databases, separately request MIPS data from CMS, search clinical-trial registry by hand. Multi-week paralegal project.
Qui tam, billing fraud whistleblower
“We’re building a qui tam case. Our whistleblower says this physician group is billing for services they’re not actually performing. Can you pull their billing by procedure and see if any volumes are way out of line, like 5x the national average for their specialty?”
Procedure-level billing analysis flags statistical outliers, the evidence that turns a whistleblower tip into a viable FCA case.
“For the procedures that look suspicious, is it one doctor driving it or is it everyone in the group? That tells me whether it’s a rogue individual or organized fraud.”
Provider-level drill-down distinguishes individual outliers from group-wide schemes, changes the entire case strategy.
“Check if the biggest billers are getting industry payments from companies whose products they’re billing for excessively. That’s another thread for the case.”
Connecting industry payments to billing outliers builds the financial motive narrative.
Without Medistill
Hire a forensic billing analyst, subpoena Medicare claims data, separately search OpenPayments. $50K–100K in expert and data costs before the case even files.
Facility affiliation mapping for standard of care
“Our client is a surgeon at a 200-bed community hospital being sued for malpractice. Show me every provider affiliated with that hospital, grouped by specialty. I need to prove this facility doesn’t have the specialist coverage a trauma center would.”
Facility lookup with specialty grouping reveals the full roster breakdown, if the hospital has 0 interventional cardiologists and 2 general surgeons, that’s the standard-of-care defense.
“Compare the specialty mix at this hospital to the nearest Level I trauma center side by side. Show the gap in every specialty group, Surgery, Cardiology, Neurology, etc.”
Specialty-by-specialty comparison quantifies the resource gap, a community hospital with 2 surgeons can’t be held to a trauma center’s standard.
“Find all surgeons with the same specialty within 25 miles. How many are there, what are their volumes, and how far away? I need to show whether a reasonable referral option actually existed.”
Geographic search with distance calculations establishes whether timely referral was feasible, if the nearest specialist is 45 minutes away, that changes the standard of care.
Without Medistill
Manually request medical staff rosters, call surrounding hospitals, hire an expert to map regional resources. Weeks of discovery prep.
Case law research, find precedent by doctrine
“Find all “informed consent” cases involving hospitals in New York from 2010 to 2020. I need precedent showing that written consent forms alone don’t satisfy the duty to inform.”
Full-text search across 10M+ court opinion texts finds cases by legal doctrine, not just party names. Returns snippets with surrounding context.
“Pull the full opinion for the top result. I need the court’s reasoning, who the judge was, and which attorneys were involved.”
Retrieves complete opinion text with judges, attorneys, and parties, everything you need to cite the case.
“Is Canterbury v. Spence still good law? Has it been overruled, reversed, or distinguished by later decisions?”
One-call Shepardize check against the 2.6M citation-treatment graph, returns overrules/reverses/follows/distinguishes counts with the citing cases, so you never cite dead law in a brief.
“Now search for “res ipsa loquitur” cases in surgical malpractice. Are there recent precedents where the doctrine was successfully applied?”
Phrase search finds exact legal doctrine matches across all 10M+ cases, sorted by date.
“How many malpractice cases has this hospital been involved in over the past decade? Show the trend by year and which courts.”
Litigation trend analysis with aggregate counts, replaces manual Westlaw case counting.
Without Medistill
Westlaw or Lexis subscription ($20K–40K/yr), plus hours of associate time to run searches, read opinions, and compile memos. $300–800/hr.
Society practice guideline by specialty + allegation
“We are defending a shoulder-dystocia case from 2019. Pull every ACOG guideline that touches shoulder dystocia or macrosomia, with bulletin numbers and publication years, so I can cite by PB number in the brief. Then check whether NICE has a comparator we can reference as persuasive authority.”
Searches 3,600+ society guidelines (ACOG, AHA/ACC, IDSA, ACP, NICE, USPSTF) with relevance scoring. Returns ACOG PB 178 (Shoulder Dystocia), PB 216 (Macrosomia), and the NICE comparator in one call. The bulletin number plus year is the brief-citation form even when the full ACOG PDF is member-gated.
“Plaintiff's expert is going to claim the standard was broader than what the 2017 PB said. Pull every revision history for ACOG PB 178 — when was it issued, when was it superseded, what was current on the date of delivery?”
Year filter + version chain shows the bulletin that was canonical on the date of injury, not the current revision. Critical for any case with a 5-7 year discovery window where the bulletin has since changed.
“Now do the same for the AHA acute coronary syndromes guideline — we have an MI delayed-diagnosis case from 2022, what was AHA/ACC's recommendation as of that date, and what does the 2025 update say about it?”
Same composer, different specialty: routes to AHA/ACC dataset (682 guidelines + statements) and surfaces the 2022 baseline plus the 2025 ACC/AHA/ACEP/NAEMSP/SCAI update for the standard-evolution narrative.
Without Medistill
ACOG + AHA society memberships ($600-1,500/yr each), per-state library access for paywalled IDSA full text, manual searching of each society's website with no relevance scoring. Plus medical-librarian retention at $150-250/hr for the version-history reconstruction.
Defense first 48 hours, one query
“Demand letter just came in. Surgical retained foreign body, North Carolina, severe injury. Build the first-48 pack: defendant identity through the ownership chain, NC caps and SOL, venue pace, judge outcome lean, comparable cases, and a settlement bracket. I have a carrier call in two hours.”
One call returns the full first-48-hours pack: provider profile, state caps and SOL, venue pace, judge outcome lean, settlement bracket, comparable cases, likely co-defendant chain, and a 7-step next-steps checklist. The pack a senior litigator builds in eight hours.
“What is the realistic verdict range for surgical retained foreign body in NC at severe severity? I need a number to take into the next call with the carrier.”
Returns NPDB-aggregate low / target / high with the state non-economic cap auto-applied. 530K payment records back the bracket and the cap math runs against all 50 states' damage-cap laws.
“Pull the assigned judge's outcome lean, full population not sampled. How many of her med-mal cases reached final judgment, of those, plaintiff vs defendant, and what is the settle-or-pending denominator?”
Joins federal med-mal docket data to judgment and disposition codes across the judge's entire docket. Most federal med-mal settles before judgment, so the tool reports both the judgment-bearing slice and the settle-or-pending denominator. No 'Judge X is N% defense' off a tiny sample.
Without Medistill
Multiple tool subscriptions, paralegal triage, healthcare economist for the severity ladder, manual NC cap math. Days of work, $5K-15K in expert costs before the first carrier call.
Opposing counsel intel, before the deposition
“We are deposing the lead partner from Smith & Jones next week. Has she historically done plaintiff or defense work? Pull her track record across the entire 10M-case corpus, side-classify every appearance, and tell me which judges she appears in front of most.”
Classifies plaintiff vs defense across every published opinion the attorney appears in. Reads attribution patterns ('for plaintiff', 'for defendant', 'represented the defendants') scoped to the attorney's last name. Full population, not sampled, with denominator discipline so percentages stay honest.
“Her partner is the named expert in 8 of the 10 most-recent cases on the docket. Side-classify him too. Has he testified plaintiff-side or defense-side, or both?”
Classifies the expert's side across multiple cases by reading the opinion text directly. Replaces the senior-associate task of pulling each case and reading for attribution.
“Now check the venue, what is the federal med-mal time-to-termination at this court, P25 / median / P75? Which judges in this district move med-mal fastest?”
Returns termination percentiles and early-disposition rates per federal court for med-mal cases specifically. Useful for venue selection on the plaintiff side and budget pacing on the defense side.
Without Medistill
Manual Westlaw search per attorney, read each case, classify by hand, then repeat on the named expert. 6-8 hours per opposing counsel + expert pair.
Litigation profile, full provider dossier with case history
“Build a complete litigation profile for this surgeon. I need their compliance record, every facility they’ve worked at, their pharma payments, CMS deficiencies at their hospitals, AND any court cases involving them, all in one report.”
Combines compliance screening (130+ sources), facility affiliations, industry payments, CMS deficiencies, and court case search into a single provider dossier.
“They worked at 3 hospitals. Search court cases involving each hospital separately. Are there patterns, malpractice cases that follow this surgeon across facilities?”
Cross-facility litigation pattern analysis reveals providers who generate lawsuits everywhere they go.
Without Medistill
Separate Westlaw search, separate compliance check, separate NPI lookup, separate industry-payment download. Multiple tools, multiple subscriptions, days of assembly.
Expert vetting
Westlaw shows you prior cases. We score the expert. Four metrics, each computed from corpus data the page already describes — surfaced here as named tools so you can call them by name.
Discovery, plaintiff and defense
0–100 + A–F grade
Composite of prior-case count (0–50), recency (–5 to +25), Daubert exposure (+5), NPI active status (–25 to +5), specialty fit (–10 to +15), IME/QME state roster (+10), recent PubMed pubs (+15), compliance flags (–25 to +5), industry consulting $ (0–10).
Use it for: Predicts likelihood the expert will say yes before you cold-call. Westlaw shows prior cases; we predict next case.
Cross-exam, defense prep
Industry-payment overlap, manufacturer + corporate parent
Pass the defendant NPI and the opposing expert. Returns every device or pharma manufacturer that paid both — including parent-company rollup (Davol + Bard + BD → BD, Covidien + Medtronic → Medtronic, Ethicon + DePuy + MDBS → J&J MedTech). Dollars, years, products.
Use it for: The kill-shot at deposition: "You and Dr. X both took money from the same company." Nobody else surfaces this.
Daubert risk, both sides
excluded_count / total_daubert_challenged
Joins medical-expert corpus (206K cases, expert_context = daubert_challenge) and damages-expert corpus (99K cases, is_daubert + daubert_outcome columns from opinion text). One unified vulnerability number per expert. Returns matched case list with case_name, court, decision_date, opinion_url.
Use it for: Estimate the risk an expert will be excluded before you retain — or before opposing counsel motions to exclude yours.
Pre-deposition intel
plaintiff / defense / court-appointed + 0–1 confidence
Regex-pattern classification of testimony allegiance inside the opinion itself. Returns confidence score, plaintiff_hits, defense_hits, and the matched snippet. Aggregates across every prior opinion to surface whether an expert leans one way.
Use it for: VerdictSearch uses verdict outcome as proxy. We read the actual testimony, opinion by opinion.
All four are exposed as MCP tools (rank_potential_experts, expert_witness_destroy_kit, expert_witness_record, expert_side_classifier) — callable from the chat surface, or from Claude / ChatGPT / your own agent via the Medistill MCP endpoint.
Why switch
Provider background check
Provider background check
Manual: OIG, SAM, state board websites, one at a time
130+ federal + state sources in one query. Risk score, letter grade, full history.
Peer comparison (standard of care)
Peer comparison (standard of care)
Hire expert witness at $500/hr to compile manually
Automated peer ranking by specialty, region, and volume. Show where your provider falls.
Hospital staffing analysis
Hospital staffing analysis
FOIA request, weeks of waiting
Full provider roster by specialty. "This hospital had 2 surgeons and no cardiologist on staff."
Geographic referral feasibility
Geographic referral feasibility
Not available from any legal research tool
All specialists within X miles. "Was a reasonable referral option even available?"
Hospital financial distress
Hospital financial distress
Subpoena cost reports, hire forensic accountant
5–7 year revenue, margin, staffing trends. Prove understaffing was a cost-cutting choice.
Financial conflict of interest
Financial conflict of interest
Manual OpenPayments.cms.gov search
Pharma/device payments by company, product, and year. Tie payments to prescribing patterns.
Billing anomaly detection
Billing anomaly detection
Subpoena Medicare data, hire billing analyst
Provider billing vs peer average. Flag 3x outliers instantly.
Court case database
Court case database
Westlaw/Lexis ($20K–$40K/yr subscription)
10M+ cases with full opinion text. Search by name, citation, or legal doctrine.
Opinion text search
Opinion text search
Westlaw/Lexis full-text search (separate subscription)
Search “informed consent” or “standard of care” inside 10M+ opinion texts. Snippets with context.
Litigation trend analysis
Litigation trend analysis
Manual case count compilation
Case counts by year, court, state, decade. Compare trends across hospitals or doctors.
Malpractice history
Malpractice history
NPDB self-query ($4.75 each) or request via hospital
1.9M NPDB adverse action reports. Prior claims, settlements, board actions.
Multi-state discipline
Multi-state discipline
Check each state board website individually
All 50 states + DC license checked simultaneously. Full order text from 32 states + DC (135K narratives, ~5K chars/order). Read the misconduct, not just the flag.
Composite risk scoring
Composite risk scoring
Not available from any tool
0–100 score and A–F grade from 130+ enforcement sources, NPDB payment history, state board orders, hospital deficiencies, and industry payments. Inputs and weights disclosed.
Damage caps & SOL by state
Damage caps & SOL by state
Westlaw/Lexis secondary sources or manual research
Instant lookup for all 50 states + DC, caps, SOL, discovery rule, statute of repose, minor exceptions
Billing code lookup
Billing code lookup
Ask a billing expert at $250+/hr
Explain any CPT/HCPCS code with avg Medicare payment
4M classified cases + citation treatment
4M classified cases + citation treatment
Manual tagging after running Westlaw search
4.0M cases in 55 legal categories. 2.6M citation-level treatment classifications (predicted overruled, reversed, affirmed, distinguished from citing-opinion language patterns), plus “Cited by” reverse lookup ranked by legal influence. Pattern-based, not editor-curated. Editor-verified treatment is on the roadmap.
Verdict & settlement analytics
Verdict & settlement analytics
VerdictSearch ($$$) or JuryVerdictAlert subscription
530K malpractice payment records. Avg, median, max by state, year, and allegation type (1990–2025).
Settlement bracket with state cap applied
Settlement bracket with state cap applied
VerdictSearch lookup + manual cap math + healthcare economist for severity ladder
Low / target / high settlement range from NPDB-aggregate medians, with the state non-economic cap auto-applied (CA MICRA, NC, TX, etc.). The number a partner walks into the carrier call with.
Judge outcome lean (federal med mal)
Judge outcome lean (federal med mal)
Lex Machina judge analytics (enterprise pricing, federal civil only)
Judge outcome lean inferred from every med-mal case the judge has presided over. Full federal docket coverage, not a sample. Reports both the judgment-bearing slice and the settle-or-pending denominator so percentages stay honest. No “Judge X is 100% defense” off 4 judged cases out of 400.
Opposing counsel side breakdown
Opposing counsel side breakdown
Lex Machina partner profiles (enterprise pricing) or manual case-by-case Westlaw read
Plaintiff vs defense classification of opposing counsel across every published opinion they appear in. Full population, not sampled. Returns counts with denominator discipline, only reports a percentage when the sample is large enough to be meaningful.
Court time-to-termination (venue pace)
Court time-to-termination (venue pace)
Lex Machina court analytics or manual PACER docket pulls
Time-to-termination percentiles plus early-disposition rates for med-mal cases at any federal court. Compare venues before filing.
Expert witness side classification (aggregate)
Expert witness side classification (aggregate)
Manual case-by-case opinion read; no commercial tool ships an aggregate side classifier
Classifies the expert’s side (plaintiff, defense, court-appointed) across multiple opinions. Tells you whether the opposing expert leans one way before the deposition.
Expert Willingness Score
Expert Willingness Score
Westlaw shows prior cases. No commercial tool predicts willingness to testify.
0–100 + A–F grade. Composite of prior-case count, recency, Daubert exposure, NPI active status, specialty fit, IME/QME roster, recent PubMed pubs, compliance flags, industry $.
Shared-sponsor destroy kit (expert + defendant)
Shared-sponsor destroy kit (expert + defendant)
Not available from any tool. Manual industry-payment cross-search at best.
Every manufacturer that paid BOTH the opposing expert and the defendant doctor on industry payments. Curated 50-family corporate-parent rollup (Davol/Bard/BD, Covidien/Medtronic, Ethicon/DePuy/MDBS→J&J).
Cross-corpus Daubert vulnerability score
Cross-corpus Daubert vulnerability score
Westlaw / LexisNexis segregate medical and non-medical expert search; no unified score
excluded_count / total_daubert_challenged, unified across medical (206K cases) + damages (99K) corpora. Matched case list with case_name, court, decision_date, opinion_url.
Expert witness case database
Expert witness case database
Manual search via Westlaw Litigation Profile Suite
206K cases mentioning expert witnesses. 75K medical experts. 1,700+ Daubert challenges.
Carrier closed-claims studies
Carrier closed-claims studies
Buy individual carrier reports one at a time, no cross-comparison
16 source documents: 9 Doctors Company specialty studies, 3 MedPro Group, 2 Curi, 2 Aon/ASHRM HPL Benchmark (97,100 claims). Filter by specialty, publisher, category, year. Pair with NPDB for triangulation.
Federal docket coverage
Federal docket coverage
PACER Pro ($30/quarter cap, federal only) or commercial bulk ($15K–50K/yr)
71M+ federal cases. 240K federal med-mal dockets pre-filtered (NOS 362 + 367) by court, judge, party, filing date. Filing-level visibility, not just opinions.
Judge profiles & financial disclosures
Judge profiles & financial disclosures
Manual JCC.gov disclosure forms + Wikipedia bios
16K federal + state judges, 51K appointments (who appointed, vote, dates), 1.9M judge investment line items for COI research. Pre-recusal motion intel.
Standard-of-care literature
Standard-of-care literature
Hire medical librarian or pay per-article on Wiley/Elsevier
From a CPT/HCPCS code plus an allegation theory, returns peer-reviewed evidence from 40M+ PubMed articles (corpus current through May 2026). Standard-of-care citations for delayed-diagnosis, wrong-site, and informed-consent cases.
Society practice guidelines (ACOG / AHA / ACP / IDSA / NICE / USPSTF)
Society practice guidelines (ACOG / AHA / ACP / IDSA / NICE / USPSTF)
ACOG / AHA society membership ($600-1,500/yr each), manual searching of each society’s site, library access for paywalled IDSA full text
3,600+ society guidelines indexed: ACOG (709 Practice Bulletins, Committee Opinions, Patient Advisories, Care Consensus), AHA/ACC (682), NICE (2,033), IDSA, ACP, USPSTF. Cite ACOG PB 178 (Shoulder Dystocia), AHA 2025 ACS, IDSA Sepsis by ID and year. ACOG abstracts available even when the full PDF is member-gated.
IME / QME state-roster check
IME / QME state-roster check
Per-state portal lookup, no centralization
NY WCB IME (2,755) + CA DWC QME (23,229) rosters indexed. Confirm credentials before retaining or cross-examining a workers' comp expert.
Federal healthcare statutes
Federal healthcare statutes
Westlaw/Lexis complete USC + CFR (full coverage)
19 key statutes (EMTALA, Stark, FCA, HIPAA, NPDB) with full text searchable by citation or keyword
Speed
Speed
2–4 weeks of paralegal research
Full provider dossier in under 60 seconds
Cost
Cost
$10K–50K in expert fees, analyst hours, FOIA costs per case
from $199/mo unlimited queries. Bill to client matter.
Data coverage
Search 10M+ Court Cases
Verify Any Provider's Background
Check 130+ Compliance Sources at Once
Analyze Billing Against Peers
Uncover Financial Conflicts
Assess Hospital Standards of Care
Find Providers by Geography
Legal Research Tools
Federal Healthcare Statutes
Verdict & Settlement Analytics
Carrier Closed-Claims Studies
Expert Witness Database
Provider Cross-Walk Dossier
Standard-of-Care Pairing
10M+ court cases with full opinion text. 130+ enforcement databases. 1.9M malpractice reports. Full provider dossier in under 60 seconds. from $199/mo monthly or $9,990/yr annual (2 months free), bill to client matter.