A note from Jeff Pinto · healthcare-AI procurement · for IQVIA Canada (Healthcare Advisory)
I'd love to start with a short call to hear what your advisory engagements actually need. You're probably swamped, so I did some homework first.
So I built a small worked example, on a representative, public-archetype vendor field, of an evaluation method I've built before: a weighted scorecard and four structural questions that land a procurement verdict on one screen. It's a rough sketch on synthetic numbers, not your data and not a finished product, just a faster way to show what working together could look like than a blank-page call.
Your advisory team sells market access, health economics, and real world evidence to payors, manufacturers, hospitals, and provincial bodies. More of those engagements now hinge on a clinical-AI or data-vendor selection, and the procurement underneath it is thin: a demo, a slide about hallucinations, an unnameable reference customer, and a price line that isn't a bid, it's a conversation. The buyer signs anyway; 18 months later nobody owns whether the tool still does its job. Your clients carry that gap, and your consultants can't always staff a specialized evaluation against it on demand.
Worked example, live: iqvia-scorecard.pages.dev (representative field, no client data).
Your consultants could build a scorecard; a sophisticated team has the pieces. What an internal team can't easily hand a client is an outside examiner's objectivity on the vendor choice, the speed of a method already authored, and a track record the client can cite. The value is independence and a name on the method, not raw capability. I'd rather say that than oversell a slider.
Fixed-scope. Priced to fit inside an advisory engagement you've already sold; commercials in a one-page SOW after a call. The retainer is offered only if the method proves recurring reuse, never the default, not a platform fee.
One 30 to 45 minute call with the advisory lead. Bring one recent engagement where a clinical-AI or data vendor had to be chosen. I'll run that field through the four questions and the scorecard live, show where the demo-weighted and the defensible answers diverge, and hand you the one-page version your consultant performs tomorrow. If your team already scores this rigorously, I'll say so.
Book it: jeffpinto.com/engage · Method: the OMA procurement-scorecard note
Jeff Pinto runs a small, independent data and AI advisory practice (jeffpinto.com). Thirty years across AI data and privacy, health tech, marketing analytics, renewables, logistics, and broadcasting; the last seven in ML and AI. Hands-on at Meta, Uber, and IBM, plus six startups (one turnaround, three acquisitions). Two MScs: computer science (Toronto) and engineering (Loughborough). Engagements are fixed-scope, four to twelve weeks, no platform and no subscription; whatever gets built, the IP transfers to you.
The edge for this one: the scorecard and the four questions aren't theory. I authored them in 2013 scoping the Ontario Medical Association's Big Data initiative against a roughly $263M four-year provincial eHealth budget, and I've evaluated healthcare-AI vendors across three decades since, including ML pipelines for psychiatric records at CAMH. The method has a paper trail; this is a re-application, not a cold pitch.
Sources: IQVIA FY2024 results (revenue $15.4B, ~88,000 employees in over 100 countries; company IR release) · IQVIA Canada Real World Solutions (advisory pillars: market access, HEOR, RWE) · the scorecard and four questions trace to Jeff's 2013 Ontario Medical Association Big Data engagement, scoped against a roughly $263M four-year provincial eHealth budget · regulatory frame: PHIPA. Vendor figures in the worked example are representative, not real vendor scores. Full workup in workbook.md.