## The Reality of AI in Healthcare
After 15 years working in healthcare technology, I've seen plenty of AI promises come and go. Some delivered real value. Many didn't. Here's what I've learned about what actually works.
What's Working Right Now
1. Medical Billing and Coding Automation
This is the unsung hero of healthcare AI. It's not glamorous, but it works. AI systems can now: - Automatically code procedures from clinical notes with 90%+ accuracy - Flag billing errors before submission - Reduce claim denials by 30-40%
We've implemented these systems for multiple healthcare networks. The ROI is immediate and measurable.
2. Predictive Analytics for Patient No-Shows
Simple, practical, and effective. Machine learning models that predict which patients are likely to miss appointments allow: - Proactive outreach to high-risk patients - Better scheduling optimization - Reduction in no-show rates by 15-25%
3. Clinical Documentation Assistance
Large language models are finally mature enough to help with: - Summarizing patient histories - Drafting clinical notes from dictation - Extracting key information from unstructured records
The key here is "assistance" - these tools augment clinicians, they don't replace them.
What's NOT Working (Yet)
Fully Autonomous Diagnosis
Despite the headlines, AI as a standalone diagnostic tool isn't ready for prime time. It works best as a second opinion or triage tool, not as the primary decision-maker.
One-Size-Fits-All Solutions
Every healthcare organization is different. Pre-built AI solutions that promise to work "out of the box" rarely deliver. Customization is essential.
The Pragmatic Approach
If you're considering AI for your healthcare organization, here's my advice:
- **Start with a specific, measurable problem** - Not "we need AI" but "we need to reduce claim denials by X%"
- **Choose boring over flashy** - Billing automation beats robot surgeons for most organizations
- **Plan for integration** - AI is useless if it doesn't fit your existing workflows
- **Expect iteration** - First deployments are rarely perfect; plan for refinement
Conclusion
AI in healthcare is real and delivers value—when applied pragmatically. The organizations seeing results aren't chasing the latest trends. They're solving specific problems with proven approaches.
Want to explore how AI could help your healthcare organization? Let's talk.