Source: Fortune
Summary
The article discusses the challenges of implementing AI in healthcare, particularly in the process of prior authorization for new drugs. The author, who has experience in the tech industry, notes that healthcare faces similar challenges to other industries, including the need for expertise, regulations, and traceability. He argues that the focus should be on changing what’s possible, rather than just automating existing processes.
Our Reading
The numbers tell one story. A new drug approval can trigger a process involving six or seven specialists, costing around $100,000 and taking 60 to 90 days. Meanwhile, patients are in coverage limbo. The same assessment, done with coordinated AI, takes four to eight hours and drops direct labor cost by 97%. The goal shouldn’t be automation, but changing what’s possible.
The author’s experience at Dell and startups taught him that enterprises often buy technology that’s supposed to fix problems, but rarely does. The COVID pandemic and the loss of a friend to breast cancer led him to healthcare, where he found that the mission became deeply personal. The article highlights the challenges of implementing AI in healthcare, including the need for governance, auditability, and traceability.
Healthcare is hard for AI, but it’s useful to learn from. The regulations are brutal, decisions matter, and data sits in systems that were never built to talk to each other. The AAMC projects a shortage of up to 86,000 physicians by 2036, and nurses are stretched thin. One health enterprise has 600 nurses whose main job is prior authorization and payment integrity, spending their days inside paperwork.
The article concludes that the goal shouldn’t be automation, but changing what’s possible. Most enterprise AI deployments are invisible, accelerating steps a human would otherwise take without changing what that human does. Deploying a thousand agents that can’t be coordinated, governed, or audited is the same thinking that gave enterprises a thousand disconnected point solutions a decade ago.
Key Points
The Challenges of Implementing AI in Healthcare
Healthcare faces similar challenges to other industries, including the need for expertise, regulations, and traceability.
The Need for Governance and Auditability
AI deployments in healthcare need to be governed, audited, and made to work for the people they’re supposed to serve.
The Importance of Changing What’s Possible
The goal shouldn’t be automation, but changing what’s possible. Most enterprise AI deployments are invisible, accelerating steps a human would otherwise take without changing what that human does.
Author’s Background
Experience in the Tech Industry
The author has experience in the tech industry, including 11 years at Dell and a few more at startups.
Personal Connection to Healthcare
The author’s experience with the COVID pandemic and the loss of a friend to breast cancer led him to healthcare, where he found that the mission became deeply personal.
Key Statistics
Shortage of Physicians
The AAMC projects a shortage of up to 86,000 physicians by 2036.
Cost of Prior Authorization
A new drug approval can trigger a process involving six or seven specialists, costing around $100,000 and taking 60 to 90 days.
Time Saved with Coordinated AI
The same assessment, done with coordinated AI, takes four to eight hours and drops direct labor cost by 97%.
