Alex Levi Daniels @AlexLeviDaniels - Twitter Profile

AlexLeviDaniels retweeted

Nish Hari

@NishHari_

26 days ago

@morgancheatham Welcome to the chat: https://t.co/X5EbeI1viA

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AlexLeviDaniels retweeted

predoc

@predoc_ai

27 days ago

Predoc Curated Data is now generally available. Turn fragmented records — PDFs and other unstructured sources included — into clean, normalized, longitudinal patient data. From Predoc retrievals or your own sources. For care teams, analytics, and AI. https://t.co/N7LfD8wLCX

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AlexLeviDaniels retweeted

Blake Madden 🏥

@B_Madden4

about 1 month ago

"Multi-agent systems" has officially entered the healthcare buzzword hall of fame, right up there with "value-based care" and "interoperability." Everyone's throwing agents at everything and calling it innovation. But every so often, a company actually shows their work. And that's exactly what Predoc (@predoc_ai) did with a new report on how they built the multi-agent architecture behind their medical records retrieval and curation engine. The full report (link below) is a useful blueprint for any healthcare exec trying to figure out what "building with AI" should actually look like in practice or where AI can have the highest impact. 4 things worth digging into: 1. Bespoke work is the opportunity, not the obstacle. Predoc makes the case that the most valuable automation opportunities aren't the clean, standardized tasks. Those get commoditized fast. It's the messy, facility-specific, exception-riddled workflows that are actually defensible. I think that's right, and it's a useful gut-check for any exec evaluating an AI vendor's ROI or worth buying. 2. The dataset is your moat. Predoc built its system on 300K-400K provider-research tasks, nearly 3 years of transcribed retrieval calls, and millions of reviewed record pages. The foundation models are swappable. That accumulated, structured "tribal knowledge" is not. 3. Start from first principles. Break the workflow down into its simplest parts. Bound each job, structure the handoff, escalate the exception. Predoc lays out how they gave each agent a job (research, voice, indexation, extraction, curation) and a structured output the next agent can act on immediately. When something doesn't fit, the agent escalates to a human, and that resolution gets fed back into the system. 4. The numbers back it up. I was pretty intrigued by some of the results in this piece: A 2-week-plus turnaround compressed to a median of 3 business days. Provider-research time down 70%. First-pass retrieval success up nearly 50%. 94.6% of pages indexed without human intervention. The bigger theme I keep coming back to: this is a case study in systems of intelligence sitting on top of disorganized, disparate systems of record. Predoc's real output isn't "faster fax retrieval." It's a normalized, longitudinal clinical data layer that other applications can actually query. Big thanks to brand partner Predoc for sitting down with me and showing their work on this one. https://t.co/jV7bBcExqH

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