chatgpt-deep-research-prompt-luxury-rehab-prospecting

ChatGPT Deep Research Prompt: National Luxury Rehab Treatment Center Prospect List

How to use: Paste this entire prompt into a ChatGPT Deep Research session. The goal is a comprehensive, CSV-ready prospect list of luxury and boutique residential addiction treatment centers across the United States, with decision-maker information and LinkedIn URLs where available.

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PROMPT START

I need you to conduct exhaustive research and compile the most comprehensive list possible of luxury and boutique residential addiction treatment centers in the United States. This is for a cybersecurity consulting firm that specializes in HIPAA and 42 CFR Part 2 compliance for high-end treatment facilities. I need this data structured and ready to import into a CRM.

What Qualifies as “Luxury/Boutique”

Include a facility if it meets two or more of these criteria:

• Charges $30,000+permonth(or$1,000+ per day) for residential treatment
• Describes itself using words like: luxury, executive, boutique, exclusive, premier, private, high-end, concierge, resort-style, holistic luxury
• Offers upscale amenities beyond clinical care (private rooms/suites, gourmet/chef-prepared meals, equine therapy, yoga/meditation studios, fitness centers, spa services, adventure therapy, beachfront/mountain/estate setting)
• Accepts primarily private pay or out-of-network PPO insurance (not primarily Medicaid/Medicare)
• Has a low bed count relative to its staffing (typically 6-60 beds with high staff-to-client ratios)
• Targets affluent clientele, executives, professionals, celebrities, or public figures
• Website and branding clearly position it as premium/upscale

What to EXCLUDE

• Chain/corporate facilities owned by large behavioral health corporations with 5+ locations under the same parent brand (e.g., Acadia Healthcare facilities, Universal Health Services facilities, American Addiction Centers mass-market locations). However, DO include smaller boutique chains with 2-4 locations if they are clearly luxury-positioned.
• Community mental health centers, county-funded facilities, methadone/MAT-only clinics, sober living houses without clinical programs, outpatient-only programs, wilderness therapy programs for adolescents
• Facilities that have permanently closed

Data Fields I Need for Each Facility

For every facility, research and provide as many of these fields as possible:

Facility Information:

1. Facility Name (official name)
2. Parent Company/Ownership Group (if different from facility name, or “Independent” if owner-operated)
3. Ownership Type (Independent/Owner-Operated, Small Group (2-4 locations), Private Equity-Backed, Investor-Backed, Nonprofit)
4. Website URL
5. City, State
6. Geographic Region (Southern California, Arizona/Scottsdale, South Florida, Colorado, Northeast, Pacific Northwest, Hawaii, Southeast, Midwest, Mountain West, Texas, Other)
7. Estimated Monthly Cost (from website, directories, or review sites - use a range if exact is unknown)
8. Bed Count (if available)
9. Primary Treatment Focus (Addiction, Dual Diagnosis, Mental Health + Addiction, Eating Disorders + Addiction, Trauma + Addiction, Executive/Professional Recovery)
10. Accreditations (Joint Commission, CARF, LegitScript, state-specific)
11. EHR/EMR System (if discoverable - look for Kipu Health, Sunwave Health, BestNotes, Alleva, Netsmart/myAvatar, TherapyNotes, or others mentioned on job postings or tech stack sites)
12. Estimated Employee Headcount (from LinkedIn company page, or estimate based on bed count)
13. Founded/Year Opened (if available)
14. Notable Differentiators (what makes this specific facility unique - 1 sentence max)

Decision Maker Information (research each facility for up to 3 key contacts):

15. Contact 1 Name (Owner, CEO, or Founder - this is the primary target)
16. Contact 1 Title
17. Contact 1 LinkedIn URL (search LinkedIn for their name + facility name)
18. Contact 2 Name (COO, Operations Director, Executive Director, or Administrator)
19. Contact 2 Title
20. Contact 2 LinkedIn URL
21. Contact 3 Name (Clinical Director, Compliance Officer, or IT Director if one exists)
22. Contact 3 Title
23. Contact 3 LinkedIn URL

Scoring Fields (you calculate these):

24. ICP Fit Score (1-5, based on the scoring rubric below)
25. Priority Tier (Tier 1, 2, or 3, based on the scoring rubric below)

ICP Fit Scoring Rubric

Score each facility 1-5 based on how well they match our ideal customer profile. We are a brand new consulting firm, so we want to prioritize facilities that are:

Score 5 (Perfect fit - Tier 1):

• Independent or small-group owned (owner makes the decisions, no corporate procurement process)
• 15-60 employees (big enough to have real compliance needs, small enough to not have internal IT/security staff)
• Clearly luxury ($40K+/month)
• Located in a state with strong enforcement (California, Florida, New York, Colorado)
• No obvious existing cybersecurity vendor relationship
• Owner/founder has an active LinkedIn presence

Score 4 (Strong fit - Tier 1):

• Meets most of the Score 5 criteria but may be slightly larger, or in a secondary market, or pricing is in the $30-40K range

Score 3 (Good fit - Tier 2):

• Boutique/upscale but not ultra-luxury
• May be slightly larger (60-100 employees)
• May be in a less enforcement-heavy state
• Could be investor-backed with a CEO who still makes operational decisions

Score 2 (Possible fit - Tier 3):

• Part of a small PE-backed group (2-4 facilities)
• Has some corporate structure but decision-makers are still accessible
• May already have some IT infrastructure but likely gaps in Part 2 compliance

Score 1 (Low priority - Tier 3):

• Larger PE-backed groups approaching the chain threshold
• Likely has corporate IT or procurement processes
• Still worth having in the database for later outreach as we build credibility

How to Sort the Final Output

Sort the entire list by:

1. ICP Fit Score (5 first, then 4, then 3, etc.)
2. Within each score tier, sort by Geographic Region in this priority order:
   • Colorado (our home state, easiest to build relationship)
   • Southern California (highest concentration of luxury rehab)
   • Arizona/Scottsdale (high concentration, accessible)
   • South Florida (high concentration)
   • Northeast (Connecticut, New York, Massachusetts)
   • Hawaii
   • Pacific Northwest
   • Mountain West (Utah, Montana)
   • Southeast
   • Texas
   • Midwest
   • Other
3. Within each region, sort by Estimated Employee Headcount (smallest first, as they are most likely to lack internal IT resources)

Where to Research

Search ALL of these sources systematically. Do not just search one or two and stop:

Treatment Center Directories:

• SAMHSA Behavioral Health Treatment Services Locator (findtreatment.gov) - filter for Residential, Private Pay
• Psychology Today treatment center directory
• LuxuryRehabs.com
• Rehabs.com
• AddictionCenter.com
• NAATP member directory (naatp.org) - National Association of Addiction Treatment Providers
• LegitScript certified treatment center directory
• GoodTherapy.org treatment centers

State Licensing Databases (prioritize these states):

• California DHCS (Department of Health Care Services) licensed facilities
• Florida DCF (Department of Children and Families) licensed substance abuse providers
• Arizona ADHS (Department of Health Services) licensed behavioral health facilities
• Colorado BHA (Behavioral Health Administration) licensed facilities
• New York OASAS (Office of Addiction Services and Supports) certified programs
• Connecticut DMHAS licensed programs
• Utah DSAMH licensed facilities
• Hawaii DOH licensed facilities

Business/Company Research:

• LinkedIn company pages (search “residential treatment” OR “luxury rehab” OR “addiction treatment center”)
• Crunchbase and PitchBook for PE-backed facilities
• Google searches: “luxury rehab [state]”, “executive treatment center [state]”, “boutique addiction treatment [city]”, “private pay rehab [state]”
• Google Maps searches for high-end treatment centers in known cluster areas
• Glassdoor and Indeed job postings (facilities hiring often list their tech stack and have headcount indicators)
• Better Business Bureau listings

Industry Sources:

• NAATP membership lists and conference attendee lists
• Behavioral Health Business (trade publication) - facility profiles and PE deal announcements
• National Council for Mental Wellbeing
• State addiction treatment provider association directories

Review Sites (for discovering facilities and estimating pricing):

• Google Reviews (filter by luxury rehab by city)
• Yelp
• Treatment center review aggregators

LinkedIn Decision-Maker Research

For EVERY facility, attempt to find decision-maker LinkedIn profiles:

1. Search LinkedIn for the facility name as a company page
2. From the company page, look at “People” to find Owner/CEO/Founder, Operations Director, Clinical Director
3. If no company page exists, search LinkedIn for: “[Facility Name]” + owner/CEO/founder
4. Search LinkedIn for the facility name in the “Experience” section of people profiles
5. For each person found, provide their full LinkedIn URL (linkedin.com/in/username format)

If you cannot find a LinkedIn URL after searching, note “LinkedIn not found” rather than leaving it blank, so I know you actually looked.

Output Format

Provide the results in TWO formats:

Format 1: CSV-ready table

Structure it as a table with all 25 columns listed above as headers, with one row per facility. Use pipe-delimited format so I can easily convert to CSV. Use “N/A” for fields you could not find. Use “LinkedIn not found” specifically for LinkedIn fields you searched but could not locate.

Format 2: Regional summary

After the table, provide a brief summary organized by geographic region:

• How many facilities found in each region
• The top 3-5 Tier 1 prospects in each region with a one-sentence note on why they are top priority
• Any notable patterns (e.g., “Arizona has a high concentration of PE-backed facilities” or “Colorado facilities tend to be smaller and independently owned”)

Important Notes

• Be thorough over fast. I would rather have 150 deeply researched facilities than 400 with missing data. But aim for 200+ if possible.
• Do not fabricate data. If you cannot confirm a data point, leave it as “N/A” or “Unconfirmed”. Do not guess at pricing, headcount, or LinkedIn URLs.
• Check if facilities are still operating. If a website is down, reviews are stale (nothing in 2+ years), or there are news reports of closure, exclude it.
• PE ownership matters. If a facility is owned by a known PE firm, note the PE firm name in the Parent Company field. This affects our sales approach.
• EHR information is a bonus. If you find it on job postings, tech stack databases (BuiltWith, Wappalyzer), or facility marketing materials, include it. If not, “N/A” is fine.
• The LinkedIn URLs are the most valuable part of this research. Prioritize finding decision-maker LinkedIn profiles even if it means spending less time on secondary data fields.

Context on Why This Matters

This prospect list will be used for LinkedIn InMail outreach and cold email campaigns. The better the data quality, especially LinkedIn URLs and ownership type, the more effectively we can personalize our outreach. We are a new firm with limited credits, so getting the targeting right on the first pass is critical. Every InMail and email needs to land with the right person at the right facility.

PROMPT END