Health Insurance & Payers
Map treatment deserts, identify high-risk prescribers, analyze MAT capacity gaps, and benchmark opioid prescribing patterns, board-ready in one conversation. Replace six-figure consulting engagements with a from $199/month subscription.
How it works
These are real reports generated from live CMS data. Ask Medistill to map the opioid crisis , it pulls prescribing rates, treatment facilities, pharma payments, and compliance flags in one conversation.
Query
“Map the opioid crisis across the top 25 US counties with highest Medicaid prescribing rates. For each county show: opioid Rx rate, overdose deaths per 100K, number of OTP/MAT facilities, hospital beds per 10K, Medicare spending per beneficiary, ER visit rate, and mental health provider shortage. Flag treatment deserts.”
CMS Medicaid Opioid Geo · CDC WONDER Mortality · SAMHSA OTP Directory · AHRF · Source: Medistill
| County | State | Rx Rate | OD Deaths/100K | OTP | Beds/10K | Medicare $/bene | ER/1K | Mental % |
|---|---|---|---|---|---|---|---|---|
| San Juan | CO | 16.7 | 41 | 0 | – | $9,619 | 577 | 15.2% |
| White Pine | NV | 13.3 | 41 | 0 | 27.8 | $12,432 | 698 | 14.8% |
| Roberts | TX | 13.3 | 18 | 0 | – | $8,865 | 304 | 13.5% |
| Terrell | TX | 13.2 | 23 | 0 | – | $9,966 | 537 | 14.1% |
| Menominee | WI | 12.8 | 39 | 0 | – | $10,858 | 876 | 18.5% |
| Catron | NM | 11.9 | 39 | 0 | – | $7,951 | 441 | 15.9% |
| Harding | NM | 11.7 | 36 | 0 | – | $11,106 | 410 | 14.9% |
| Huerfano | CO | 11.6 | 34 | 0 | – | $9,389 | 571 | 16.3% |
| Pend Oreille | WA | 11.6 | 26 | 0 | 52.1 | $9,085 | 564 | 15.8% |
| Clark | ID | 11.3 | 23 | 0 | – | $11,787 | 682 | 16.4% |
| Churchill | NV | 11.2 | 33 | 0 | 9.7 | $10,454 | 695 | 14.5% |
| Trousdale | TN | 11 | 47 | 1 | 20.5 | $12,829 | 646 | 15.8% |
| Banner | NE | 10.8 | 13 | 0 | – | $13,447 | 405 | 13.6% |
| Lincoln | WA | 10.8 | 21 | 0 | 54.8 | $11,240 | 577 | 14.2% |
| Ferry | WA | 10.7 | 25 | 0 | 33.8 | $9,822 | 552 | 15.9% |
| Cannon | TN | 10.7 | 44 | 1 | 20.3 | $13,555 | 579 | 15.2% |
| Fremont | WY | 10.6 | 25 | 0 | 20 | $9,900 | 520 | 15.5% |
| Keya Paha | NE | 10.6 | 15 | 0 | – | $14,002 | 452 | 13.9% |
| Hardin | IL | 10.5 | 36 | 0 | 69.4 | $12,949 | 1535 | 18.5% |
| Stevens | WA | 10.5 | 22 | 1 | 16.7 | $8,465 | 571 | 15.3% |
| Clear Creek | CO | 10.5 | 23 | 0 | – | $10,826 | 378 | 14.8% |
| Grundy | TN | 10.4 | 53 | 0 | – | $11,526 | 708 | 18.9% |
Follow-up query
“Who are the top opioid prescribers in those treatment desert counties? Cross-reference each with industry payments for pharma manufacturer relationships, LEIE for federal exclusions, and flag any receiving payments from opioid manufacturers specifically.”
Plan prescribing data · industry payments · OIG LEIE · SAMHSA OTP · Source: Medistill
| Name | St | Spec | County | Opioid Rx | Bene | Opioids | Pharma $ | Opioid Mfr | MAT | LEIE | Risk |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Marie Bohrer | WY | NP | Fremont | 1,686 | 187 | 9 | $53 | YES | 0 | Clear | High |
| Misbah Zmily | NM | IM | Harding adj. | 1,102 | 131 | 7 | $1,435 | YES | 0 | Clear | High |
| Marcos Sunga | IL | IM | Hardin | 1,089 | 144 | 8 | $1,122 | YES | 0 | Clear | High |
| Pamela Ammons | NM | NP | Harding adj. | 859 | 67 | 6 | $121 | No | 0 | Clear | Elevated |
| Jeremy Lewis | WA | FP | Pend Oreille | 851 | 186 | 8 | $2,580 | YES | 0 | Clear | High |
| Anpo Charging Thunder | NE | FP | Keya Paha adj. | 778 | 74 | 7 | $64 | No | 0 | Clear | Elevated |
| Rolf Panke | WA | FP | Lincoln | 741 | 87 | 7 | $5,388 | YES | 0 | Clear | High |
| Christopher Buscher | WA | PA | Pend Oreille | 740 | 128 | 6 | $389 | No | 0 | Clear | Elevated |
| Angelika Kraus | WA | FP | Pend Oreille | 724 | 74 | 7 | $1,770 | YES | 0 | Clear | High |
| Precious Castro-Flores | CO | IM | Huerfano adj. | 703 | 125 | 5 | $661 | No | 0 | Clear | Elevated |
| Mark Bayliss | NM | PA | Harding adj. | 701 | 86 | 5 | $50 | No | 0 | Clear | Elevated |
| James Spurlock | TN | FP | Cannon | 700 | 95 | 6 | $7,864 | YES | 1 | Clear | Monitored |
| Bart Kneeland | WI | FP | Menominee adj. | 689 | 150 | 6 | $34 | No | 0 | Clear | Moderate |
| Frederick Reed | WA | FP | Lincoln | 659 | 81 | 7 | $3,416 | YES | 0 | Clear | Moderate |
| Mari Hunter | WA | NP | Ferry | 601 | 80 | 8 | $0 | No | 0 | Clear | Moderate |
| Heidi Helgeson | CO | FP | Rio Grande adj. | 593 | 79 | 6 | $1,574 | YES | 0 | Clear | Moderate |
| Shannon Radke | WA | FP | Pend Oreille | 579 | 122 | 7 | $3,061 | No | 0 | Clear | Moderate |
| Amir Obbehat | CO | IM | Catron adj. | 574 | 108 | 5 | $1,934 | YES | 0 | Clear | Moderate |
| Jason Weyer | WY | FP | Fremont adj. | 518 | 57 | 5 | $113 | YES | 0 | Clear | Moderate |
| Eladio Chatto | IL | IM | Hardin | 508 | 74 | 3 | $1,060 | YES | 0 | Clear | Moderate |
Follow-up query
“For the 3 counties that do have MAT facilities (Trousdale TN, Cannon TN, Stevens WA), what's the treatment capacity vs. opioid demand? How many SUD treatment slots exist vs. plan opioid volume? Are these facilities keeping up?”
SAMHSA OTP · plan-level · AHRF · N-SSATS · Source: Medistill
| County | State | Population | Medicaid Rx Rate | Plan Opioid Rx | OTP Facilities | Hosp Beds | Psych Beds | Est. SUD Slots | Strain |
|---|---|---|---|---|---|---|---|---|---|
| Trousdale | TN | 11,284 | 11 | 6,341 | 1 | 25 | 0 | 120 | Critical |
| Cannon | TN | 14,678 | 10.7 | 5,287 | 1 | 30 | 0 | 140 | Critical |
| Stevens | WA | 46,282 | 10.5 | 3,224 | 1 | 77 | 8 | 200 | High |
What you can ask
Each question runs against real data. Follow-ups build on previous results , Medistill remembers context across the entire conversation.
Opioid treatment desert mapping
“Map the opioid treatment desert. For the 25 counties with the highest Medicaid opioid prescribing rates, show: overdose death rates, total SAMHSA treatment facilities, how many offer medication-assisted treatment (buprenorphine, methadone), available treatment beds per 100K population, and Medicare spending per beneficiary. Which counties are prescribing the most opioids with zero MAT-capable treatment facilities?”
Joins Medicaid Drug Utilization, CDC mortality, SAMHSA facility data, AHRF beds, and Medicare geographic variation into a single crisis map no existing tool can produce.
“In those treatment desert counties, who are the top opioid prescribers by plan volume? Show their specialties, opioid claims, and whether they receive payments from opioid manufacturers. Are prescribers fueling the crisis in places with no treatment options?”
Cross-references prescribing data, industry payments, and LEIE to connect individual prescriber behavior to county-level crisis data.
“For the counties that DO have treatment facilities, what’s the capacity gap? Show the ratio of annual opioid prescriptions to available treatment beds. Which facilities offer detox vs. outpatient vs. residential? Where is treatment infrastructure most strained relative to demand?”
Quantifies the demand-to-supply ratio using plan claims, OTP enrollment, AHRF beds, and NPI treatment provider data.
Without Medistill
Manual county-by-county research across CDC WONDER, SAMHSA locator, CMS data files, and state databases. Weeks of analyst time to build a single crisis map.
Network compliance screening
“Screen every provider in our Florida network against all compliance databases. Flag anyone with an OIG exclusion, SAM.gov debarment, state board discipline, Medicare opt-out, or NPI deactivation.”
Batch screens against 16+ federal and state databases in seconds, the same check that takes credentialing teams days per provider.
“For the flagged providers, show their Medicare billing volume. How much are we paying to providers with compliance issues?”
Quantifies financial exposure from non-compliant network providers, critical for audit preparation.
“Are any of the excluded providers in our network also receiving pharma payments? Show the payment amounts and companies.”
Surfaces potential fraud patterns, excluded providers still receiving industry payments.
Without Medistill
Manual OIG LEIE checks one-by-one, separate state board lookups, SAM.gov searches, Medicare opt-out list downloads. Days of tedious work per provider.
ACO performance benchmarking
“Which ACOs are actually saving money? Pull every MSSP ACO, rank by generated savings per assigned beneficiary. For the top 20, show their quality scores, number of participating providers, track level, and years in the program. What predicts success?”
Analyzes ACO REACH and MSSP performance data alongside quality scores to identify the structural patterns that drive savings.
“Compare MSSP ACOs vs ACO REACH entities on the same metrics. Is the direct contracting model outperforming traditional shared savings?”
Head-to-head comparison of the two dominant value-based care models, critical for network strategy.
Without Medistill
Download multiple CMS data files, normalize across different program years and reporting formats, build custom analytics. Months of work.
GLP-1 spend trajectory
“Our actuarial team needs to understand GLP-1 spend trajectory. Pull plan total drug spending for semaglutide, tirzepatide, liraglutide, and dulaglutide by year. Show year-over-year growth rate in cost and volume. At current trajectory, project the spend increase.”
Combines plan-level Drug Spending data across multiple years with prescribing volume trends, the data actuaries need but takes weeks to compile manually.
“Which states have the highest GLP-1 prescribing rates? Cross-reference with obesity prevalence from CDC PLACES, are the states with the most obesity getting the most GLP-1 prescriptions, or is there a mismatch?”
Maps prescribing geography to disease burden, identifies both appropriate utilization and potential over/under-prescribing markets.
“How many of the top GLP-1 prescribers are endocrinologists vs PCPs vs other specialties? Is this being driven by specialists or primary care?”
Prescriber specialty mix determines formulary strategy, PCP-driven markets respond to different interventions than specialist-driven ones.
Without Medistill
Download raw plan-level spending files, build year-over-year trend analysis, separately download CDC prevalence data, manually cross-reference. Weeks of analyst time.
Hospital price transparency
“We’re renegotiating hospital contracts in Texas. Pull every hospital’s negotiated rate for the 10 highest-volume DRGs from price transparency data. Show us where we’re paying more than the state median and by how much.”
Queries 6.1B price transparency charges to find rate outliers, the analysis that gives your negotiation team leverage.
“For the hospitals where we’re 20%+ above market, what are their quality scores? Are we paying a premium for better outcomes, or just overpaying?”
Connects price data to quality data, separates justified premiums from pure overpayment.
Without Medistill
Download individual hospital machine-readable files (hundreds of them), normalize across different formats, build comparison analytics. Massive data engineering effort.
Network risk monitoring
Flag at-risk hospitals in your network before CMS does
Our model is trained on 7 years of hospital financial data and predicts financial distress 12 months ahead. Every acute care and critical access hospital in the US, 6,170+ facilities, scored 0–100 with A–F grades.
| Hospital | State | Risk Score | Grade | Key Driver | Trend |
|---|---|---|---|---|---|
| AD Hospital East LLC | TX | 94.9 | F | Negative margin + declining discharges | Worsening |
| Knox County Hospital | TX | 86.1 | F | Margin declined 26pp year-over-year | Worsening |
| Wilson N Jones Regional | TX | 85.3 | F | Discharge decline 15%+ | Worsening |
| Cochran Memorial Hospital | TX | 83.9 | F | Negative operating margin | Stable |
| Culberson Hospital | TX | 80.9 | F | Revenue/discharge below peer average | Worsening |
Why switch
Opioid crisis mapping
Opioid crisis mapping
Manual county-by-county research, weeks of work
22-county deep analysis in one question
Prescriber analysis
Prescriber analysis
Separate database queries, manual joins
Cross-reference prescribing data, industry payments, LEIE in seconds
Treatment capacity
Treatment capacity
Call each facility, compile manually
MAT facility capacity vs. demand, auto-calculated
Pharma payment tracking
Pharma payment tracking
Download flat files, manual matching
Industry payments linked to prescriber + opioid products
Compliance screening
Compliance screening
Manual OIG + state board checks
130+ databases, instant, with risk scoring
Dashboards
Dashboards
Pre-built templates, IT tickets
Ask for a dashboard, generated instantly
Reports
Reports
Manual exports, copy-paste
"Turn this into a report", formatted, downloadable
Modifications
Modifications
New query, start over
"Filter to WA state only", refine by asking
Scheduled monitoring
Scheduled monitoring
Not available or enterprise add-on
Schedule any query daily/weekly via Claude
Price
Price
$100K–$300K/yr consulting + data subscriptions
From $199/mo
Hospital network risk monitoring
Hospital network risk monitoring
Not available
Predictive model, 6,170 hospitals scored, A-F grades, 12-month forward
Data coverage
Screen Network Providers
Monitor Prescribing Risk
Assess Network Adequacy
Plan Formulary & Drug Spending
Facility-Level Screening
Cost & Utilization Benchmarks
9.4M providers, 130+ compliance sources, 1.12M formulary records, geographic adequacy , all queryable through AI at from $199/month.