AI readiness assessment for healthcare

Where AI value shows up first in healthcare

The first returns in healthcare tend to be operational: faster prior authorization and insurance approvals, fewer claim denials, earlier warning of patient deterioration, and clinician time freed from documentation.

How the questions change for healthcare

These questions are asked in the healthcare edition instead of the standard wording. The pillar, section and scoring are pinned, so your score stays comparable with organizations in other industries.

1. Data FoundationsHow do you protect patient information when you use it for AI (under patient-privacy laws like HIPAA or GDPR)?Patient records raise the bar. This asks whether privacy is built into how your AI is trained and used — not added on afterwards.Answer options are rewritten for this industry, from “No specific controls for patient data in AI” to “Advanced methods that let AI learn without ever exposing real patient records”.
2. AI Governance & SecurityHow ready are you for healthcare AI rules — where AI that helps make clinical decisions can be regulated like a medical device, and is treated as "high-risk" under the EU AI Act?AI that touches clinical decisions carries heavy obligations (it can be regulated as a medical device). AI used only for back-office or operational tasks carries much lighter ones.Answer options are rewritten for this industry, from “It's not on our radar” to “Ahead of the pack; we talk to health regulators (like the FDA or EMA) about AI policy”.
3. Model ManagementBefore you trust an AI tool that affects patient care, how thoroughly do you test it — including on real patients going forward, and across different patient groups?Back-office AI can launch on basic accuracy and fairness checks. AI affecting patients needs testing on real patients over time, not just on past records.Answer options are rewritten for this industry, from “No formal clinical testing” to “Tested on real patients going forward, with ongoing monitoring after launch”.
4. AI Use-Case PortfolioIn the last year, what was the biggest single payoff from one of your AI tools? (In healthcare this is often faster insurance approvals, fewer claim denials, earlier warning of patient deterioration, or freed-up clinician time.)Pick a number you can defend. "Saved nurse time" only counts when you can express it as full-time staff freed up.Answer options are rewritten for this industry, from “We don't have one live yet” to “$10M or more (e.g. far fewer claim denials, or shorter hospital stays)”.

Pillars this edition emphasizes

All nine pillars are scored for every organization. These are the ones where the healthcare edition changes what is asked.

One scoring model, comparable across industries

The industry edition changes wording, examples and answer options, never the maths. Every organization is scored on the same nine weighted pillars and lands in one of the same five levels, which is what makes peer benchmarks meaningful. The full method is on the methodology page.

Other industry editions

• Financial Services
• Manufacturing
• Retail
• Technology