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 Viz.ai 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 radiology staff" (job postings - everyone sees this)
Start: "Your off-hours stroke cases average 89 minutes imaging-to-notification vs 42 minutes during day shift" (CMS quality data with actual performance metrics)
PQS (Pain-Qualified Segment): Reflect their exact situation with such specificity they think "how did you know?" Use government data with dates, facility names, actual performance metrics.
PVP (Permissionless Value Proposition): Deliver immediate value they can use today - analysis already done, patterns already identified, benchmarks already calculated - whether they buy or not.
Company: Viz.ai
Core Problem: Healthcare providers struggle with delayed disease detection and diagnosis because medical imaging analysis is time-consuming and prone to human oversight, causing patients to wait for treatment and increasing clinical workload. Viz.ai automates medical image analysis to accelerate diagnosis delivery and improve patient outcomes.
Product Type: B2B SaaS - Healthcare AI
Title: VP of Clinical Operations or Chief Medical Officer
Key Responsibilities:
Key KPIs:
These messages demonstrate precise understanding of the prospect's situation (PQS) or deliver immediate actionable value (PVP). All insights trace back to verifiable data sources.
For multi-hospital integrated delivery networks, analyze transfer times between specific facility pairs to identify where care coordination breaks down most severely. Show them exactly which site combinations have the longest delays.
Network-level analysis is sophisticated and immediately actionable. The variance between site pairs shows exactly where to prioritize process improvements. This helps clinical leaders fix system-level problems rather than guessing which facilities need attention first.
This play requires aggregated inter-facility transfer data showing origin-destination pairs, timestamps, and handoff stages across hospital networks.
Combined with public CMS network affiliation data. This synthesis is unique to your operational insights.Show comprehensive stroke centers their hour-by-hour diagnostic performance data, revealing the specific time windows when off-hours cases experience the longest delays compared to day shift performance.
Off-hours performance is a known blind spot for clinical operations leaders. The specificity of knowing exact time deltas and having hour-by-hour breakdowns makes this immediately actionable for staffing decisions and workflow improvements. The brain tissue urgency resonates clinically.
This play requires aggregated before/after implementation data showing off-hours vs daytime diagnostic speed improvements, stratified by stroke center certification level and region, from hospital customers processing stroke imaging cases 24/7.
Combined with public CMS quality data. This synthesis is proprietary to your operational insights.Target primary and comprehensive stroke centers with certifications expiring within 12 months that show declining imaging efficiency metrics. The combination of certification deadline plus performance decline creates urgency.
Certification renewal is a high-stakes deadline with real consequences. The 22% decline with exact minute increases shows you've tracked their performance trend over time. This creates immediate urgency without being pushy - it's just data.
Deliver quarter-by-quarter performance analysis showing the consistent decline pattern in imaging efficiency, helping stroke centers identify which specific process steps degraded over time before their certification audit.
The quarterly trend shows a process degradation pattern that Joint Commission auditors will flag. Offering the breakdown of which specific steps slowed down helps them address root causes before the audit, creating clear preparation value.
For multi-hospital networks, deliver facility-by-facility transfer time analysis showing which site pairs have the longest coordination delays, helping them prioritize which routes need process improvements first.
Multi-site network analysis is valuable and sophisticated. Identifying exactly where coordination breaks down (during handoff between community hospital detection and comprehensive center intervention) helps fix system-level problems. Clear value even if they don't buy.
This play requires aggregated inter-facility transfer data showing origin-destination pairs, timestamps, and handoff stages across hospital networks.
Combined with public network affiliation data. This network-level synthesis is unique to your operational insights.Target comprehensive stroke centers with significant off-hours vs daytime diagnostic speed gaps. Use CMS quality data combined with benchmarking to show them they're missing the 60-minute target window when specialist availability drops overnight.
The 47-minute delta is extremely specific and concerning. Off-hours blind spots are real - clinical leaders don't track this well. The easy routing question makes it frictionless to respond. Credibility comes from clearly analyzing their actual timestamps.
This play requires aggregated hospital imaging timestamp data segmented by time-of-day, compared against their own daytime performance baseline and peer benchmarks.
Combined with public CMS quality data. This time-segmented analysis is unique to your operational insights.Deliver 12-month door-to-imaging performance trend showing exactly when performance started declining, helping stroke centers identify root causes before their Joint Commission recertification audit.
The urgent timeline creates real pressure. Month-by-month trend data is actionable - it helps them identify exactly when processes broke down so they can address root causes. Clear audit prep value with low commitment ask.
Deliver detailed trauma imaging volume analysis showing Saturday-Sunday peak patterns compared to weekday averages, helping trauma centers understand exactly when bottlenecks occur and optimize staffing.
The specific weekend volume analysis with staffing correlation is actionable. Offering detailed breakdown they can act on helps build business case for weekend coverage adjustments. This is immediately useful operational intelligence.
Deliver hour-by-hour overnight stroke case performance data showing which specific hours have the worst diagnostic delays, helping comprehensive stroke centers target coverage gaps during peak fatigue periods.
Extremely granular time analysis reveals surprising insights (early morning 2am-5am worst performance). The fatigue correlation is plausible and actionable. This helps target specific coverage gaps to improve patient outcomes.
This play requires case-level imaging timestamps segmented by hour showing performance variance throughout overnight shift, from hospitals processing stroke imaging cases 24/7.
Combined with public CMS quality data. This hourly-level analysis is unique to your operational insights.Target stroke centers with certifications expiring in March 2025 that show door-to-imaging time increases year-over-year. The specific renewal date plus benchmark comparison creates urgency.
Urgent timeline tied to actual renewal date. Specific metric decline is actionable. Benchmark comparison adds context without being pushy. Creates urgency through data, not sales pressure.
Target comprehensive stroke centers showing significant performance variance between overnight and day shift stroke case processing. Focus on facilities where overnight delays double their day shift averages.
Time-of-day analysis is valuable and not commonly tracked. The doubling effect is alarming. Off-hours blind spot is real for most clinical operations teams. The easy yes/no question reduces friction.
This play requires imaging timestamp data segmented by shift time showing performance variance between overnight and day operations.
Combined with public CMS quality data. This shift-segmented analysis is unique to your operational insights.Target Level I/II trauma centers with high imaging case volumes that show weekend backlogs when radiologist staffing drops. Identify facilities where Saturday-Sunday pending studies peak significantly above weekday averages.
Very specific about weekend problem. The 90-minute delay implication is serious for trauma outcomes. Staffing correlation shows deep understanding. Easy question to answer makes it frictionless.
Target multi-hospital integrated delivery networks (3+ facilities) with above-benchmark inter-hospital transfer times. Focus on networks where stroke transfers between community hospitals and comprehensive centers exceed 60-minute coordination targets.
Network transfer insight is specific and valuable. Handoff detail shows operational understanding. Transfer coordination is a known pain point for multi-site systems. Easy tracking question.
This play requires transfer timestamp data showing handoff stages between facilities in multi-hospital networks.
Combined with public CMS network affiliation data. This handoff-level analysis is unique to your operational insights.Target multi-hospital networks (3+ facilities) with stroke transfers averaging significantly above 60-minute coordination benchmarks. Focus on networks where inter-facility transfers show consistent delays during patient handoffs.
Specific to multi-site operations. The 58-minute gap is concerning for patient outcomes. Transfer coordination is a known pain point. Easy routing question.
This play requires transfer timestamp data across hospital networks showing origin-to-destination times.
Combined with public network affiliation data. This network-level synthesis is unique to your operational insights.Target trauma centers where Saturday afternoon trauma imaging queues peak significantly above weekday averages, correlating with reduced radiologist coverage during high-volume periods.
Saturday-specific insight is valuable. Staff count correlation adds credibility. Weekend coverage is known pain point. Easy routing question.
Target Level I/II trauma centers with documented weekend imaging backlogs that correlate with reduced radiologist staffing. Focus on facilities where Saturday pending studies peak at 2x weekday averages.
Saturday-specific insight is actionable. Staff correlation shows understanding. Weekend coverage pain is real.
Old way: Spray generic messages at job titles. Hope someone replies.
New way: Use public data to find hospitals in specific painful situations. Then mirror that situation back to them with evidence.
Why this works: When you lead with "Your off-hours stroke cases average 89 minutes vs 42 minutes day shift" instead of "I see you're expanding your neurology department," 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 data. Here are the sources used in this playbook:
| Source | Key Fields | Used For |
|---|---|---|
| CMS Hospital Compare Quality Data | facility_name, facility_id, quality_measures, imaging_efficiency_metrics, patient_safety_indicators | Hospital quality benchmarking, network affiliations |
| CMS Outpatient Imaging Efficiency Data | facility_name, imaging_measure_rates, imaging_efficiency_metrics | Identifying diagnostic bottlenecks and efficiency gaps |
| CMS Hospital Outpatient Quality Reporting (OQR) Program Data | ed_efficiency_measures, imaging_process_measures, ed_throughput_metrics | ED efficiency and imaging process performance tracking |
| Stroke Center Certification Database (Joint Commission) | certification_level, certification_date, facility_name | Certification renewal tracking and compliance risk identification |
| National Trauma Data Bank (NTDB) | imaging_procedures_performed, time_to_diagnosis, facility_level | Trauma center imaging volumes and diagnostic speed analysis |
| Viz.ai Internal Performance Data | door_to_diagnosis_time, off_hours_performance_delta, care_coordination_metrics | Proprietary benchmarks and before/after implementation analysis |