The Parallels and Peculiarities of Selling Clinical AI Into Hospitals
summary
Hospitals present concrete hurdles: selling takes 18+ months while implementations that look like “60 hours” stretch to six months, so one-year contracts often fail; successful deals require early referenceable clients even if those references are subsidized (eg Cleveland Clinic). Market size and signals include 120,000 clinics, roughly 50–125 billion annual PE/VC flow, and 20% of US GDP in healthcare. Technical specifics: integration via Smart on FHIR and Epic App Orchard, common tools like Dragon/Nuance, clinical wins include radiology automation and sepsis early warning systems, with adoption hampered by workflow, regulatory, and data bias issues.
AI-written summary · about
chapters (11)
- 0:00Introduction and speaker background
- 1:26Healthcare market structure overview
- 6:15Opportunities and challenges in healthcare AI
- 10:29AI applications across healthcare settings
- 15:42Clinical AI types and adoption trends
- 19:16Selling to hospitals deal mechanics
- 23:07Why healthcare AI is worth the effort
- 23:34Q&A on integration costs and regulations
- 25:56Interoperability and data standards
- 28:00Global data analysis and unstructured EHR data
- 31:57Closing remarks and Q&A wrap-up
related talks
35mFireside Chat
25mThe Ethical Introduction of AI in Clinical Practice: Is There Such a Thing?
11mIntelligent Health AI Conference September 2020 - Cievert
42mAI-Powered Healthcare CRM | Cognitive Companion Revolution
23mHow AI Is Rebuilding Healthcare
11mClinical AI Agents with Oracle Health
30mBuilding a Billion-Dollar Healthcare Product at Zebra Technologies
24mLeveraging Voice in Healthcare After COVID