Call Extraction: CDL-MedScout Data Discussion

DDX ID: 0389 Date: 2025-09-03 Duration: ~29 minutes Participants: Phil Cranmer — CDL Nuclear. Daniel Wheaton (AE), Ronald Miller (Data Team Lead) — MedScout. Call type: Data validation session — Phil shared side-by-side Excel comparison of MedScout vs AcuityMD procedure volumes. Triage: Rich. Phil’s data validation methodology, the AcuityMD competitive dynamics, and the “claims underreport by ~50%” insight all surface new fingerprint material. 4 distinct insights.

Distinct Insights

1. CDL validates claims data against their own billing records — they have ground truth

What: Phil: “There’s a couple scenarios where I looked at our billings. I know what they’re doing in pet and pet ct, and sometimes it’s fairly close to yours. Okay? Sometimes this one, not so much.” He also referenced field validation: “I’ll give you the feedback from our team. Well, an N of two is they already felt like acuity was under reporting based on what they were able to find out once they speak with clients or prospects.”

So what: CDL has something most claims data buyers don’t — access to actual billing records from their installed base. When CDL places a PET camera at a facility, they know exactly how many PET and PET/CT procedures that facility performs because CDL handles the billing (or at minimum, has visibility into it through their service agreement). This means CDL can compare any claims data vendor’s numbers against reality for their existing customers. When Phil says MedScout shows “approximately half the volume” compared to AcuityMD on the same codes and timeframes, and his field team says even AcuityMD underreports, he’s triangulating three data sources: (1) MedScout claims data, (2) AcuityMD claims data, and (3) CDL’s own billing actuals. This triangulation capability makes Phil an unusually sophisticated data buyer. He’s not choosing between vendors based on feature demos — he’s running quantitative accuracy tests against known values. Any vendor selling to CDL needs to be prepared for this level of scrutiny.

Speaker credibility: Phil, with Excel analysis prepared before the call. Very high — this is systematic evaluation, not casual feedback. Scope: Company-wide vendor evaluation methodology. Motion: Both.

2. Claims data underreporting is ~50%, and both vendors underreport — this is a market-wide issue, not a vendor differentiator

What: Phil noted MedScout showed roughly half the volume AcuityMD showed for the same facilities and codes. But his field team said even AcuityMD underreports: “They already felt like acuity was under reporting based on what they were able to find out once they speak with clients or prospects.” Phil framed the comparison: “It’s almost like you guys report approximately half close, but it’s like half the volume, right?”

So what: CDL has empirically confirmed that claims data universally underreports actual procedure volumes. MedScout shows ~50% of AcuityMD’s numbers, and AcuityMD itself underreports compared to real billing data. This means the absolute numbers in any claims dataset are directional, not precise — CDL treats them as relative indicators for prioritization, not as literal volume counts. The practical implication: CDL’s 800+ SPECT threshold in the platform likely maps to a real-world volume much higher than 800. If claims data captures roughly half of actual volume, an 800 SPECT count in claims might represent 1,600+ actual procedures. CDL’s reps would know this intuitively from field experience, which is why they validate on the ground rather than trusting claims data as the final word. This also explains why CDL’s choice between data vendors isn’t primarily about accuracy (both are inaccurate) but about relative accuracy, consistency, and usability. Phil needs to “start feeling good about the data” — not perfect data, but data he can calibrate and trust directionally.

Speaker credibility: Phil, with triangulated evidence. Very high. Scope: Market-wide claims data limitation, not CDL-specific. Motion: Both.

3. AcuityMD deployed defensive competitive tactics when CDL began evaluating alternatives

What: Phil: “Acuitymd, who I’m with today claims they have the best data easy to say when I’m on my way out, but they also casted doubt on other providers’ data quality. I should say including you guys.”

So what: When CDL signaled they were evaluating alternatives to AcuityMD, Acuity’s response was to attack competitor data quality rather than defend their own accuracy. Phil’s “easy to say when I’m on my way out” framing reveals he sees through this tactic — he recognizes it as retention-motivated rather than evidence-based. But the tactic still had an effect: it added doubt to Phil’s evaluation and contributed to his “struggling with the data” state. For the fingerprint, this confirms CDL was an AcuityMD customer before switching to MedScout (the switch happened Oct-Nov 2025 per context). It also reveals the competitive dynamics of the healthcare claims data market: when a customer evaluates alternatives, incumbent vendors FUD (fear, uncertainty, doubt) competitor data quality because data accuracy is inherently hard to prove or disprove. CDL’s unique advantage — having ground truth billing data — makes them resistant to FUD, but Phil still wanted the discrepancies explained before committing to a switch.

Speaker credibility: Phil, describing his direct experience with AcuityMD’s retention approach. Very high. Scope: Competitive intelligence about CDL’s vendor evaluation process and AcuityMD’s behavior. Motion: Both.

4. NPI-level vs facility-level attribution explains volume discrepancies — CDL’s targeting requires facility-level rollup

What: Daniel explained that MedScout attributes procedures to individual NPIs or CCNs, while some volume that appears under “Banner Tucson” in AcuityMD might be spread across multiple billing entities in MedScout’s data: “Rather than falling all under banner Tucson, it might fall under a group associated with banner Tucson because they’re billing it from their practice.” Ronald flagged a specific anomaly: “I was looking at Flagstaff Medical Center because that seemed to be a bit of an anomaly like what we were showing like 15 cases which seems very low.”

So what: The attribution methodology difference reveals a real tension in CDL’s targeting needs. CDL’s sales teams target facilities — “Banner Tucson” is a target, not an individual NPI. When MedScout’s data distributes volume across multiple NPIs and billing entities associated with the same physical facility, the total facility volume looks lower than in AcuityMD’s facility-level rollup. For CDL’s prospecting workflow, this means a high-value target (e.g., Banner Tucson doing 1,000 SPECT/year) might appear as several smaller entries (300 under one NPI, 200 under another, 100 under a different billing entity, etc.), each of which might fall below CDL’s volume thresholds. The Flagstaff Medical Center showing only 15 cases is almost certainly an attribution fragmentation issue, not a real volume. CDL needs claims data that can both attribute to individual providers (for identifying clinical champions) AND roll up to facility level (for target prioritization). This dual-level attribution need is specific to CDL’s two-phase sales approach: identify the facility, then find the champion within it.

Speaker credibility: Daniel and Ronald (MedScout data team) explaining methodology; Phil identifying the practical impact. High across all parties. Scope: Data methodology issue affecting all CDL targeting. Motion: Both (hospital targets especially, since hospital billing involves more NPIs per facility).

Transcription Notes

“Acuity”/“acuitymd” — refers to AcuityMD, CDL’s prior claims data platform. Consistent throughout.
“Ronnie” — Phil’s nickname for Ronald Miller (MedScout Data Team Lead). Not a transcription error.
Ronald Miller not previously in term bank. Should be added as MedScout Data Team Lead.
CCN — CMS Certification Number. Correctly used in technical discussion about attribution methodology.
“S Fathom Notetaker” in party names — automated meeting recorder, not a person. Same as 0363.
Banner Tucson and Flagstaff Medical Center — Arizona facilities Phil used as comparison cases. Banner is a major health system; Flagstaff is a standalone facility.
The MDCN includes extensive coaching notes and SPICED analysis added post-call by MedScout. The actual conversational content is the ~29-minute discussion documented in the <detailed_conversation> section.

Term Bank Addition Candidates

Ronald Miller = MedScout Data Team Lead. Participated in data validation call.
AcuityMD — already in context, but this transcript adds detail: CDL was an active AcuityMD customer, and Acuity deployed defensive FUD during CDL’s evaluation process.
Banner Tucson / Flagstaff Medical Center = Arizona target facilities used in Phil’s data comparison. Useful as reference examples of attribution discrepancy.