The Healthcare AI Stack: Building vs. Buying
Many mid-market healthcare leaders have explored major platforms like Epic Cheers, Veradigm, and Health Catalyst. While the capabilities may seem right, the implementation timelines, costs, and data compatibility raise questions.
So, what should you build and what should you buy?
USM Business Systems specializes in assisting mid-market health systems, specialty pharmacies, and pharma/CRO organizations in making this decision. Here is the framework we follow.
Starting with Data Reality
Your data environment, not your budget or timeline, determines your stack.
If your EHR is up to date, your prior auth workflow is organized, and your payer data is reliable, you have more platform options. On the other hand, if you’re managing messy data infrastructure, most platforms may not deliver as expected.
When evaluating vendors, consider how their platform handles dirty data.
Strengths of Platforms
Off-the-shelf healthcare AI platforms excel when:
- Your data infrastructure aligns with their integration assumptions
- Your use case fits their pre-built models without extensive customization
- You have internal IT resources for ongoing maintenance
- Your budget and timeline can accommodate a 9–18 month implementation
For organizations meeting these criteria, a platform is a suitable choice.
Advantages of Custom AI Agents
A custom healthcare AI agent is ideal when:
- Your data environment requires significant cleanup for platform reliability
- Your use case demands specific modifications to pre-built models
- You want the agent tailored to your unique data and requirements
- You need rapid deployment
Custom builds necessitate a partner with healthcare domain expertise to ensure operational and compliance alignment.
Decision-Making Framework
USM applies a three-question filter to healthcare engagements:
1. Is the problem standard or specific?
2. How clean is the underlying data?
3. What is the decision speed requirement?
The Hybrid Approach
Most mid-market healthcare teams opt for a hybrid approach, buying infrastructure at the commodity layer and building custom intelligence on top. This is the architecture USM deploys, connecting to existing systems without requiring major system changes.
Deployment timeline: 8–12 weeks with ROI measurable from week one.
USM provides a no-cost architecture consultation for healthcare leaders exploring AI solutions. Book a session at usmsystems.com.
