Founder of Blueprint. I help companies stop sending emails nobody wants to read.
The problem with outbound isn't the message. It's the list. When you know WHO to target and WHY they need you right now, the message writes itself.
I built this system using government databases, public records, and 25 million job posts to find pain signals most companies miss. Predictable Revenue is dead. Data-driven intelligence is what works now.
Your GTM team is buying lists from ZoomInfo, adding "personalization" like mentioning a LinkedIn post, then blasting generic messages about features. Here's what it actually looks like:
The Typical Remedy Logic SDR Email:
Why this fails: The prospect is an expert. They've seen this template 1,000 times. There's zero indication you understand their specific situation. Delete.
Blueprint flips the approach. Instead of interrupting prospects with pitches, you deliver insights so valuable they'd pay consulting fees to receive them.
Stop: "I see you're hiring compliance people" (job postings - everyone sees this)
Start: "Your facility at 1234 Industrial Pkwy received EPA violation #2024-XYZ on March 15th" (government database with record number)
PQS (Pain-Qualified Segment): Reflect their exact situation with such specificity they think "how did you know?" Use government data with dates, record numbers, facility addresses.
PVP (Permissionless Value Proposition): Deliver immediate value they can use today - analysis already done, deadlines already pulled, patterns already identified - whether they buy or not.
These messages demonstrate such precise understanding of the prospect's current situation that they feel genuinely seen. Every claim traces to a specific government database with verifiable record numbers.
This play combines two verifiable public data sources — CMS outpatient claims data showing MRI volume growth and hospital staffing disclosures (Medicare Cost Reports, annual reports) showing flat FTE counts. The 18% volume growth is specific and documented; the staffing FTE is real. By calculating the per-radiologist load increase (3.2 additional spine studies per shift), the message transforms abstract numbers into operational reality that the prospect cannot dispute. This is a capacity crisis signal disguised as routine administrative data.
The prospect cannot argue with their own data. The message presents a mathematical inevitability: 18% volume growth + zero staffing growth = increasing diagnostic burden per clinician. This triggers urgency because the math is undeniable and the timeline is visible. The buyer recognizes this is not a vendor pitch but a facility-specific workload analysis grounded in their published metrics.
CMS outpatient claims data (publicly available via CMS Provider Utilization files) cross-referenced with hospital staffing disclosures (Medicare Cost Reports or annual reports).
This play demonstrates non-obvious data synthesis: combining volume growth (CMS OPPS claims) with staffing trends (Cost Report FTE) to derive per-radiologist load metrics. No competitor does this without the same data infrastructure. The 18% and staffing numbers must be real — fabrication destroys credibility completely.This play uses two public signals — visible job postings (LinkedIn, careers page) showing concurrent open radiology positions and Medicare Cost Report FTE trends showing year-over-year staffing decline — to infer a departure-outpacing-recruitment crisis. The combination reveals something the prospect may not have consciously connected: repeated openings against a shrinking baseline suggests departures are accelerating, not just normal turnover. This is a non-obvious insight derived from connecting two public data streams that most vendors never synthesize.
The prospect recognizes you have observed something about their institution that they may not have articulated internally. By combining visible job postings with FTE baseline trends, the message implies a staffing crisis that is self-reinforcing: departures lead to workload increases, which trigger more departures. This triggers both concern and respect — concern about the visible spiral, respect for the analytical depth. The buyer feels seen by someone who is paying attention to their specific operational signals.
Job posting monitoring (careers page + LinkedIn) for radiology openings in Q1 2025 and cross-referencing with Medicare Cost Report FTE data for 2022 vs. current baseline.
This play requires monitoring the facility's job postings in real time and correlating with historical Cost Report FTE data. Both sources are public but the synthesis (job posting frequency + FTE decline trend) is non-obvious. The competitive advantage is that you are identifying a staffing crisis signal before internal planning cycles surface it.This play targets academic medical centers using publicly available NRMP Match data to identify institutions with unfilled radiology positions. The 2025 Match results are verifiable within minutes, making this a high-confidence targeting signal. Unfilled residency slots directly translate to attending radiologists absorbing routine annotation and measurement work — exactly the tasks Remedy Logic automates. This pain point is acute and documented in real time.
The message makes the prospect feel specifically understood by citing their own unfilled position data. It avoids generic industry commentary and instead connects the visible problem (unfilled slot) to their hidden operational burden (attending coverage gap on routine spine MRI studies). This triggers recognition: the buyer realizes you've done specific research on their institution, not sent a template to 500 medical centers.
This play uses CMS procedure data (lumbar and cervical spine surgeries at the specific facility) combined with published Medicare Cost Report radiology FTE to infer that imaging interpretation is on the critical path for surgical scheduling. The logic is direct: high surgical volume + limited radiology staff = imaging becomes the bottleneck that delays case scheduling. Both data points are public and facility-specific, making this a verifiable targeting signal without resorting to generic industry statistics.
The prospect is in an operational constraint they may not have articulated clearly to external parties. The message connects two visible facts (surgical volume from CMS, radiology staffing from Cost Report) to reveal a hidden cost: delayed surgical scheduling due to imaging reads. This triggers recognition because it names a problem that surgical leadership feels acutely but often attributes to other causes. The buyer realizes you understand their workflow dependency structure.
These messages provide actionable intelligence before asking for anything. The prospect can use this value today whether they respond or not.
This is a true PVP (personalized value proposition) that requires pre-work. The sender has already built a facility-specific MRI load model using CMS claims and Medicare Cost Report staffing data, showing when the prospect hits a realistic throughput ceiling (Q3 2025). The model is concrete and falsifiable — the prospect can verify or dispute it immediately. This level of preparation signals serious, non-template engagement and creates an asymmetric information advantage for the sender.
The prospect is being offered something they cannot get elsewhere: a custom analysis of their specific bottleneck timeline. The message implies that the sender understands their constraint structure (hiring cycle timing) well enough to anticipate a crisis before it becomes visible to internal stakeholders. This triggers both urgency and respect — the buyer recognizes they are speaking with someone who has done real work, not sent a form letter.
CMS Outpatient Prospective Payment System (OPPS) claims data and Medicare Cost Report FTE data for the specific facility.
This play requires pulling and modeling real CMS + Cost Report data for each prospect. The Q3 ceiling finding must be derived from actual numbers — any fabrication collapses the message credibility. The competitive advantage is time-to-insight: you are warning them of a crisis before internal planning cycles surface it.This is a high-value PVP that pre-builds a quantitative business case for the prospect's CFO or board. The sender combines Medicare Cost Report salary data (published compensation by facility and department) with public job posting data (open radiology positions) to calculate the attrition exposure tied to spine MRI manual workload. The model uses the prospect's actual salary band, not an industry average — immediately addressing the most common objection to turnover cost arguments. This converts an abstract pain (radiologist burnout) into a specific financial risk (replacement cost at their salary level).
Finance and board conversations require numbers, not narratives. By offering a pre-built model using the prospect's actual compensation data, the message enables the recipient to present a justified business case to decision-makers. It also demonstrates that the sender understands the financial language of healthcare — not just clinical problems, but retention economics. This triggers credibility and removes friction from the buying conversation.
Medicare Cost Report radiology FTE compensation data for the specific facility and cross-referencing with public job postings (LinkedIn, Indeed, facility careers page) to establish the open position timeline.
This play requires pulling Cost Report salary data and monitoring job postings to establish the open position timeline. The model must use facility-specific salary data — not industry averages. The competitive advantage is that you are quantifying an internal cost (retention/replacement) that the prospect has not yet computed, enabling them to justify AI investment to their CFO.Old way: Spray generic messages at job titles. Hope someone replies.
New way: Use public data to find companies in specific painful situations. Then mirror that situation back to them with evidence.
Why this works: When you lead with "Your Dallas facility has 3 open OSHA violations from March" instead of "I see you're hiring for safety roles," you're not another sales email. You're the person who did the homework.
The messages above aren't templates. They're examples of what happens when you combine real data sources with specific situations. Your team can replicate this using the data recipes in each play.
Every play traces back to verifiable public data. Here are the sources used in this playbook:
| Source | Key Fields | Used For |
|---|---|---|
| Hospital MRI Procedure Volume Data (Definitive Healthcare) | facility_name, location, mri_procedure_volume, mri_procedures_percentage_of_market, facility_type, health_system_affiliation | Identifying high-volume imaging centers where diagnostic delays are most acute and automation impact is highest; combining with staffing data to reveal capacity crisis signals |
| PACS/EHR Integration & Healthcare IT Adoption Data | facility_name, pacs_system_vendor, ehr_system_integration, cloud_adoption_status, dicom_hl7_compliance, integration_date | Showing digital maturity and technical infrastructure capability to integrate AI imaging tools; identifying institutions with DICOM/HL7 compliance for readiness assessment |
| Healthcare IT Job Postings - Radiology AI & Imaging Roles | employer_name, job_title, location, salary_range, posting_date, required_skills, job_description | Identifying facilities actively investing in AI/automation infrastructure and revealing staffing gaps through concurrent open positions; monitoring turnover signals and digital transformation initiatives |
| NRMP Radiology Program Match Data | program_name, program_state, diagnostic_radiology_positions_offered, interventional_radiology_positions_offered, positions_filled, match_rate | Identifying academic medical centers and hospitals with unfilled radiology residency positions; quantifying attending coverage burden and workforce gaps by institution and region |
| Radiologist Staffing Shortage & Burnout Data (ACR, HPI, NRMP Match Data) | radiologists_per_100k_population_by_state, demand_vs_supply_gap, burnout_rates, attrition_rates, unfilled_job_openings, nrmp_match_unmatched_positions | Validating pain domain (radiologist staffing constraints and burnout); identifying regions with acute shortages; correlating with facility-specific data to quantify local shortage severity |